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White A, Saranti M, d'Avila Garcez A, Hope TMH, Price CJ, Bowman H. Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI. Neuroimage Clin 2024; 43:103638. [PMID: 39002223 PMCID: PMC11299565 DOI: 10.1016/j.nicl.2024.103638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/22/2024] [Accepted: 06/29/2024] [Indexed: 07/15/2024]
Abstract
Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning and interpreting the predictive features, as well as, how to effectively combine neuroimaging and tabular data (e.g. demographic information and clinical characteristics). This paper evaluates several solutions based on two strategies. The first is to use 2D images that summarise MRI scans. The second is to select key features that improve classification accuracy. Additionally, we introduce the novel approach of training a convolutional neural network (CNN) on images that combine regions-of-interests (ROIs) extracted from MRIs, with symbolic representations of tabular data. We evaluate a series of CNN architectures (both 2D and a 3D) that are trained on different representations of MRI and tabular data, to predict whether a composite measure of post-stroke spoken picture description ability is in the aphasic or non-aphasic range. MRI and tabular data were acquired from 758 English speaking stroke survivors who participated in the PLORAS study. Each participant was assigned to one of five different groups that were matched for initial severity of symptoms, recovery time, left lesion size and the months or years post-stroke that spoken description scores were collected. Training and validation were carried out on the first four groups. The fifth (lock-box/test set) group was used to test how well model accuracy generalises to new (unseen) data. The classification accuracy for a baseline logistic regression was 0.678 based on lesion size alone, rising to 0.757 and 0.813 when initial symptom severity and recovery time were successively added. The highest classification accuracy (0.854), area under the curve (0.899) and F1 score (0.901) were observed when 8 regions of interest were extracted from each MRI scan and combined with lesion size, initial severity and recovery time in a 2D Residual Neural Network (ResNet). This was also the best model when data were limited to the 286 participants with moderate or severe initial aphasia (with area under curve = 0.865), a group that would be considered more difficult to classify. Our findings demonstrate how imaging and tabular data can be combined to achieve high post-stroke classification accuracy, even when the dataset is small in machine learning terms. We conclude by proposing how the current models could be improved to achieve even higher levels of accuracy using images from hospital scanners.
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Affiliation(s)
- Adam White
- Department of Computer Science, City, University of London, UK
| | | | | | - Thomas M H Hope
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, University College London, UK
| | - Howard Bowman
- School of Psychology, University of Birmingham, UK; School of Computer Science, University of Birmingham, UK
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2
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Bourached A, Bonkhoff AK, Schirmer MD, Regenhardt RW, Bretzner M, Hong S, Dalca AV, Giese AK, Winzeck S, Jern C, Lindgren AG, Maguire J, Wu O, Rhee J, Kimchi EY, Rost NS. Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity. Brain Commun 2024; 6:fcae007. [PMID: 38274570 PMCID: PMC10808016 DOI: 10.1093/braincomms/fcae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 09/01/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.
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Affiliation(s)
- Anthony Bourached
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Robert W Regenhardt
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- University of Lille, Inserm, CHU Lille, U1171—LilNCog (JPARC)—Lille Neurosciences & Cognition, Lille F-59000, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
| | - Stefan Winzeck
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Computing, Imperial College London, London SW7 2RH, UK
| | - Christina Jern
- Institute of Biomedicine, Department of Laboratory Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg 41390, Sweden
- Department of Clinical Genetics and Genomics Gothenburg, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg 41345, Sweden
| | - Arne G Lindgren
- Department of Neurology, Skåne University Hospital, Lund 22185, Sweden
| | - Jane Maguire
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund 22185, Sweden
- University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - John Rhee
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02139, USA
| | - Eyal Y Kimchi
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Evaston, IL 60201, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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Park CH, Kim MS. Stratified predictions of upper limb motor outcomes after stroke. Front Neurol 2024; 14:1323529. [PMID: 38239320 PMCID: PMC10794733 DOI: 10.3389/fneur.2023.1323529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction Longitudinal observations of upper limb motor recovery after stroke have suggested that certain subgroups may exhibit distinct recovery patterns. Here we sought to examine whether the predictive ability for post-stroke upper limb motor outcomes could be enhanced by applying conventional stratification strategies. Method For 60 individuals who suffered the first stroke, upper limb motor impairment was assessed with the upper extremity Fugl-Meyer assessment (UE-FMA) at 2 weeks as a baseline and then 3 months post-stroke. Brain structural damage at baseline was assessed by MRI data-derived markers ranging from traditional lesion size to the lesion load and to the disconnectome. Linear regression models for predicting upper limb motor outcomes (UE-FMA score at 3 months post-stroke) based on baseline upper limb motor impairment (UE-FMA score at 2 weeks post-stroke), brain structural damage, and their combinations were generated, and those with the best predictive performance were determined for individual subgroups stratified according to initial impairment (severe and non-severe), lesion location (cortical and non-cortical), and neurophysiological status (motor evoked potential-positive and motor evoked potential-negative). Results The best predictions were made by baseline upper limb motor impairment alone for subgroups with less functional impairment (non-severe) or less structural involvement (non-cortical), but by the combination of baseline upper limb motor impairment and brain structural damage for the other subgroups. The predictive models tailored for subgroups determined according to initial impairment and neurophysiological status yielded a smaller overall error than that for the whole group in upper limb motor outcome predictions. Discussion The predictive ability for upper limb motor outcomes could be enhanced beyond the one-size-fits-all model for all individuals with stroke by applying specific stratification strategies, with stratification according to initial impairment being the most promising. We expect that predictive models tailored for individual subgroups could lead closer to the personalized prognosis of upper limb motor outcomes after stroke.
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Affiliation(s)
- Chang-hyun Park
- Division of Artificial Intelligence and Software, College of Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Min-Su Kim
- Department of Physical Medicine and Rehabilitation, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Department of Regenerative Medicine, College of Medicine, Soonchunhyang University, Cheonan, Republic of Korea
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Verschure PFMJ, Páscoa Dos Santos F, Sharma V. Redefining stroke rehabilitation: Mobilizing the embodied goal-oriented brain. Curr Opin Neurobiol 2023; 83:102807. [PMID: 37980804 DOI: 10.1016/j.conb.2023.102807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/21/2023]
Abstract
Advancements in stroke rehabilitation remain limited and call for a reorientation. Based on recent results, this study proposes a network-centric perspective on stroke, positing that it not only causes localized deficits but also affects the brain's intricate network of networks, transiting it into a pathological state. Translating these system-level insights into interventions requires brain theory, and the Distributed Adaptive Control (DAC) theory offers such a framework. When applied in the rehabilitation gaming system, these principles demonstrate superior results over conventional methods. This impact stems from activating extensive brain networks, particularly the executive control network, focused motor learning, and maintaining excitatory-inhibitory balance, which is essential for neural repair and functional reorganization. The analysis stresses uniting preclinical and clinical research and placing the architecture of the embodied volitional brain at the centre of rehabilitation approaches.
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Affiliation(s)
- Paul F M J Verschure
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), Barcelona, Spain. https://twitter.com/@francpsantos
| | - Vivek Sharma
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
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Meng Z, Guo R, Wang T, Bo B, Lin Z, Li Y, Zhao Y, Yu X, Lin DJ, Nachev P, Liang ZP, Li Y. Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T 2 Mapping. IEEE Trans Biomed Eng 2023; 70:3147-3155. [PMID: 37200119 DOI: 10.1109/tbme.2023.3277546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.
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Schweighofer N, Ye D, Luo H, D’Argenio DZ, Winstein C. Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model. J Neuroeng Rehabil 2023; 20:83. [PMID: 37386512 PMCID: PMC10311775 DOI: 10.1186/s12984-023-01202-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 06/09/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke. METHODS The model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use the Bayesian hierarchical modeling technique to incorporate prior information from similar patients. We use HBDM to re-analyze the Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: (1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-h dose condition (data of 40 participants analyzed), and (2) the EXCITE trial, in which participants were assigned a 60-h dose, in either an immediate or a delayed condition (95 participants analyzed). RESULTS For both datasets, HBDM accounts well for individual dynamics in the MAL during and outside of training: mean RMSE = 0.28 for all 40 DOSE participants (participant-level RMSE 0.26 ± 0.19-95% CI) and mean RMSE = 0.325 for all 95 EXCITE participants (participant-level RMSE 0.32 ± 0.31), which are small compared to the 0-5 range of the MAL. Bayesian leave-one-out cross-validation shows that the model has better predictive accuracy than static regression models and simpler dynamic models that do not account for the effect of supervised training, self-training, or forgetting. We then showcase model's ability to forecast the MAL of "new" participants up to 8 months ahead. The mean RMSE at 6 months post-training was 1.36 using only the baseline MAL and then decreased to 0.91, 0.79, and 0.69 (respectively) with the MAL following the 1st, 2nd, and 3rd bouts of training. In addition, hierarchical modeling improves prediction for a patient early in training. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy. CONCLUSIONS In future work, such forecasting models can be used to simulate different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person. Trial registration This study contains a re-analysis of data from the DOSE clinical trial ID NCT01749358 and the EXCITE clinical trial ID NCT00057018.
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Affiliation(s)
- Nicolas Schweighofer
- Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, USA
| | - Dongze Ye
- Computer Science, University of Southern California, Los Angeles, USA
| | - Haipeng Luo
- Computer Science, University of Southern California, Los Angeles, USA
| | - David Z. D’Argenio
- Biomedical Engineering, University of Southern California, Los Angeles, USA
| | - Carolee Winstein
- Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, USA
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7
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Segmental motor recovery after cervical spinal cord injury relates to density and integrity of corticospinal tract projections. Nat Commun 2023; 14:723. [PMID: 36759606 PMCID: PMC9911610 DOI: 10.1038/s41467-023-36390-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/27/2023] [Indexed: 02/11/2023] Open
Abstract
Cervical spinal cord injury (SCI) causes extensive impairments for individuals which may include dextrous hand function. Although prior work has focused on the recovery at the person-level, the factors determining the recovery of individual muscles are poorly understood. Here, we investigate the muscle-specific recovery after cervical spinal cord injury in a retrospective analysis of 748 individuals from the European Multicenter Study about Spinal Cord Injury (NCT01571531). We show associations between corticospinal tract (CST) sparing and upper extremity recovery in SCI, which improves the prediction of hand muscle strength recovery. Our findings suggest that assessment strategies for muscle-specific motor recovery in acute spinal cord injury are improved by accounting for CST sparing, and complement person-level predictions.
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Goldsmith J, Kitago T, Garcia de la Garza A, Kundert R, Luft A, Stinear C, Byblow WD, Kwakkel G, Krakauer JW. Arguments for the biological and predictive relevance of the proportional recovery rule. eLife 2022; 11:e80458. [PMID: 36255057 PMCID: PMC9648971 DOI: 10.7554/elife.80458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 10/17/2022] [Indexed: 11/13/2022] Open
Abstract
The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values - an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery.
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Affiliation(s)
- Jeff Goldsmith
- Department of Biostatistics, Columbia Mailman School of Public HealthNew YorkUnited States
| | - Tomoko Kitago
- Burke Neurological InstituteWhite PlainsUnited States
- Weill Cornell MedicineNew YorkUnited States
| | | | - Robinson Kundert
- Cereneo, Center for Neurology and RehabilitationVitznauSwitzerland
| | - Andreas Luft
- Cereneo, Center for Neurology and RehabilitationVitznauSwitzerland
- Vascular Neurology and Neurorehabilitation, Department of Neurology, University Hospital Zurich, University of ZurichZurichSwitzerland
| | - Cathy Stinear
- Department of Medicine, University of AucklandAucklandNew Zealand
| | - Winston D Byblow
- Department of Exercise Sciences, University of AucklandAucklandNew Zealand
| | - Gert Kwakkel
- Rehabilitation Research Centre, ReadeAmsterdamNetherlands
- Rehabilitation Medicine, Amsterdam UMC - Location VUMC, Amsterdam Movement SciencesAmsterdamNetherlands
| | - John W Krakauer
- Department of Neurology, Johns Hopkins UniversityBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins UniversityBaltimoreUnited States
- Department of Physical Medicine and RehabilitationBaltimoreUnited States
- Santa Fe InstituteSanta Fe NMUnited States
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9
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Walter HL, Pikhovych A, Endepols H, Rotthues S, Bärmann J, Backes H, Hoehn M, Wiedermann D, Neumaier B, Fink GR, Rüger MA, Schroeter M. Transcranial-Direct-Current-Stimulation Accelerates Motor Recovery After Cortical Infarction in Mice: The Interplay of Structural Cellular Responses and Functional Recovery. Neurorehabil Neural Repair 2022; 36:701-714. [PMID: 36124996 DOI: 10.1177/15459683221124116] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Transcranial direct current stimulation (tDCS) promotes recovery after stroke in humans. The underlying mechanisms, however, remain to be elucidated. Animal models suggest tDCS effects on neuroinflammation, stem cell proliferation, neurogenesis, and neural plasticity. OBJECTIVE In a longitudinal study, we employed tDCS in the subacute and chronic phase after experimental focal cerebral ischemia in mice to explore the relationship between functional recovery and cellular processes. METHODS Mice received photothrombosis in the right motor cortex, verified by Magnetic Resonance Imaging. A composite neuroscore quantified subsequent functional deficits. Mice received tDCS daily: either 5 sessions from day 5 to 9, or 10 sessions with days 12 to 16 in addition. TDCS with anodal or cathodal polarity was compared to sham stimulation. Further imaging to assess proliferation and neuroinflammation was performed by immunohistochemistry at different time points and Positron Emission Tomography at the end of the observation time of 3 weeks. RESULTS Cathodal tDCS at 198 kC/m2 (220 A/m2) between days 5 and 9 accelerated functional recovery, increased neurogenesis, decreased microglial activation, and mitigated CD16/32-expression associated with M1-phenotype. Anodal tDCS exerted similar effects on neurogenesis and microglial polarization but not on recovery of function or microglial activation. TDCS on days 12 to 16 after stroke did not induce any further effects, suggesting that the therapeutic time window was closed by then. CONCLUSION Overall, data suggest that non-invasive neuromodulation by tDCS impacts neurogenesis and microglial activation as critical cellular processes influencing functional recovery during the early phase of regeneration from focal cerebral ischemia.
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Affiliation(s)
- Helene Luise Walter
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anton Pikhovych
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Heike Endepols
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Radiochemistry and Experimental Molecular Imaging, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Nuclear Chemistry (INM-5), Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Steffen Rotthues
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Johannes Bärmann
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Heiko Backes
- Multimodal Imaging Group, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Dirk Wiedermann
- Multimodal Imaging Group, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Bernd Neumaier
- Institute of Radiochemistry and Experimental Molecular Imaging, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Nuclear Chemistry (INM-5), Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Gereon Rudolf Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Maria Adele Rüger
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michael Schroeter
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany
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10
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Bonkhoff AK, Ullberg T, Bretzner M, Hong S, Schirmer MD, Regenhardt RW, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Wu O, Frid P, Rost NS, Wasselius J. Deep profiling of multiple ischemic lesions in a large, multi-center cohort: Frequency, spatial distribution, and associations to clinical characteristics. Front Neurosci 2022; 16:994458. [PMID: 36090258 PMCID: PMC9453031 DOI: 10.3389/fnins.2022.994458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/02/2022] [Indexed: 11/17/2022] Open
Abstract
Background purpose A substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort. Materials and methods Analyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome. Results We analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, p FDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, p FDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL. Conclusion Multiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Teresa Ullberg
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology and Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- U1171 – LilNCog (JPARC) – Lille Neurosciences Cognition and University of Lille, Inserm, CHU Lille, Lille, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Marco J. Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Brandon L. Hancock
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Steven J. T. Mocking
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Elissa C. McIntosh
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - John W. Cole
- Department of Neurology, University of Maryland, School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Amanda Donatti
- School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas (UNICAMP), Campinas, Brazil
| | - Christoph J. Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, United States
- Department of Neurosurgery, Christian Doppler Clinic, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions Mèdiques), Universitat Pompeu Fabra, Barcelona, Spain
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Steven J. Kittner
- Department of Neurology, University of Maryland, School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience, Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium
- Laboratory of Neurobiology, Department of Neurology, Vesalius Research Center (VIB), University Hospitals Leuven, Leuven, Belgium
| | - Christopher R. Levi
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
- Department of Pharmacotherapy, Translational Research, Center for Pharmacogenomics, University of Florida, Gainesville, FL, United States
| | | | - James F. Meschia
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Chia-Ling Phuah
- Department of Neurology, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
| | - Stefan Ropele
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions Mèdiques), Universitat Pompeu Fabra, Barcelona, Spain
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Tatjana Rundek
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Ralph L. Sacco
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Reinhold Schmidt
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Pankaj Sharma
- Institute of Cardiovascular Research, St Peter’s, Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, United Kingdom
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - Alessandro Sousa
- School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas (UNICAMP), Campinas, Brazil
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Division of Stroke, Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Daniel Woo
- Department of Neurology, Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Ramin Zand
- Department of Neurology, Pennsylvania State University, Hershey, PA, United States
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Bradford B. Worrall
- Department of Neurology, University of Virginia, Charlottesville, VA, United States
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Arne G. Lindgren
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- University of Technology, Faculty of Health, Sydney, NSW, Australia
| | - Ona Wu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Petrea Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology and Neuroradiology, Skåne University Hospital, Lund, Sweden
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11
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Scott SH, Lowrey CR, Brown IE, Dukelow SP. Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior. Neurorehabil Neural Repair 2022:15459683221115413. [PMID: 35932111 DOI: 10.1177/15459683221115413] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
While many areas of medicine have benefited from the development of objective assessment tools and biomarkers, there have been comparatively few improvements in techniques used to assess brain function and dysfunction. Brain functions such as perception, cognition, and motor control are commonly measured using criteria-based, ordinal scales which can be coarse, have floor/ceiling effects, and often lack the precision to detect change. There is growing recognition that kinematic and kinetic-based measures are needed to quantify impairments following neurological injury such as stroke, in particular for clinical research and clinical trials. This paper will first consider the challenges with using criteria-based ordinal scales to quantify impairment and recovery. We then describe how kinematic-based measures can overcome many of these challenges and highlight a statistical approach to quantify kinematic measures of behavior based on performance of neurologically healthy individuals. We illustrate this approach with a visually-guided reaching task to highlight measures of impairment for individuals following stroke. Finally, there has been considerable controversy about the calculation of motor recovery following stroke. Here, we highlight how our statistical-based approach can provide an effective estimate of impairment and recovery.
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Affiliation(s)
- Stephen H Scott
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Catherine R Lowrey
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Ian E Brown
- Kinarm, BKIN Technologies Ltd. Kingston, ON, Canada
| | - Sean P Dukelow
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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12
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Li X, Fang F, Li R, Zhang Y. Functional Brain Controllability Alterations in Stroke. Front Bioeng Biotechnol 2022; 10:925970. [PMID: 35832411 PMCID: PMC9271898 DOI: 10.3389/fbioe.2022.925970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/01/2022] [Indexed: 11/17/2022] Open
Abstract
Motor control deficits are very common in stroke survivors and often lead to disability. Current clinical measures for profiling motor control impairments are largely subjective and lack precise interpretation in a “control” perspective. This study aims to provide an accurate interpretation and assessment of the underlying “motor control” deficits caused by stroke, using a recently developed novel technique, i.e., the functional brain controllability analysis. The electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were simultaneously recorded from 16 stroke patients and 11 healthy subjects during a hand-clenching task. A high spatiotemporal resolution fNIRS-informed EEG source imaging approach was then employed to estimate the cortical activity and construct the functional brain network. Subsequently, network control theory was applied to evaluate the modal controllability of some key motor regions, including primary motor cortex (M1), premotor cortex (PMC), and supplementary motor cortex (SMA), and also the executive control network (ECN). Results indicated that the modal controllability of ECN in stroke patients was significantly lower than healthy subjects (p = 0.03). Besides, the modal controllability of SMA in stroke patients was also significant smaller than healthy subjects (p = 0.02). Finally, the baseline modal controllability of M1 was found to be significantly correlated with the baseline FM-UL clinical scores (r = 0.58, p = 0.01). In conclusion, our results provide a new perspective to better understand the motor control deficits caused by stroke. We expect such an analytical methodology can be extended to investigate the other neurological or psychiatric diseases caused by cognitive control or motor control impairment.
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Affiliation(s)
- Xuhong Li
- Department of Rehabilitation Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
- *Correspondence: Feng Fang, , Yingchun Zhang,
| | - Rihui Li
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
- *Correspondence: Feng Fang, , Yingchun Zhang,
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13
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Lee HH, Sohn MK, Kim DY, Shin YI, Oh GJ, Lee YS, Joo MC, Lee SY, Song MK, Han J, Ahn J, Lee YH, Chang WH, Choi SM, Lee SK, Lee J, Kim YH. Understanding of the Lower Extremity Motor Recovery After First-Ever Ischemic Stroke. Stroke 2022; 53:3164-3172. [PMID: 35713003 DOI: 10.1161/strokeaha.121.038196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND We aimed to verify the validity of the proportional recovery model for the lower extremity. METHODS We reviewed clinical data of patients enrolled in the Korean Stroke Cohort for Functioning and Rehabilitation between August 2012 and May 2015. Recovery proportion was calculated as the amount of motor recovery over initial motor impairment, measured as the Fugl-Meyer Assessment of Lower Extremity score. We used the logistic regression method to model the probability of achieving the full Fugl-Meyer Assessment of Lower Extremity score, whereby we considered the ceiling effect of the score. To show the difference in the prevalence of achieving the full Fugl-Meyer Assessment of Lower Extremity score between 3 and 6 months poststroke, we constructed a marginal model through the generalized estimating equation method. We also performed the propensity score matching analysis to show the dependency of recovery proportion on the initial motor deficit at 3 and 6 months poststroke. RESULTS We evaluated 1085 patients. The recovery proportions at 3 and 6 months poststroke were 0.67±0.42 and 0.75±0.39, respectively. A 1-unit decrease in the initial neurological impairment and the age at stroke onset increased the probability of achieving the full Fugl-Meyer Assessment of Lower Extremity score, which occurred at both 3 and 6 months poststroke. The prevalence of those who reach full lower limb motor recovery differs significantly between 3 and 6 months poststroke. We also found out that the recovery proportion at both 3 and 6 months poststroke is determined by the initial motor deficits of the lower limb. These results are not consistent with the proportional recovery model. CONCLUSIONS Our results demonstrated that the proportional recovery model for the lower limb is invalid.
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Affiliation(s)
- Hyun Haeng Lee
- Department of Rehabilitation Medicine, Konkuk University Medical Center and Konkuk University School of Medicine, Seoul, South Korea (H.H.L., J.L.)
| | - Min Kyun Sohn
- Department of Rehabilitation Medicine, Chungnam National University School of Medicine, Daejeon, South Korea (M.K.S.)
| | - Deog Young Kim
- Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, South Korea (D.Y.K.)
| | - Yong-Il Shin
- Department of Rehabilitation Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, South Korea (Y.-I.S.)
| | - Gyung-Jae Oh
- Department of Preventive Medicine, Wonkwang University School of Medicine, Iksan, South Korea. (G.-J.O., Y.-H.L.)
| | - Yang-Soo Lee
- Department of Rehabilitation Medicine, Kyungpook National University School of Medicine, Kyungpook National University Hospital, Daegu, South Korea (Y.-S.L.)
| | - Min Cheol Joo
- Department of Rehabilitation Medicine, Wonkwang University School of Medicine, Iksan, South Korea. (M.C.J.)
| | - So Young Lee
- Department of Rehabilitation Medicine, Jeju National University Hospital, Jeju National University School of Medicine, South Korea (S.Y.L.)
| | - Min-Keun Song
- Department of Physical and Rehabilitation Medicine, Chonnam National University Medical School, Gwangju, South Korea (M.K.S.)
| | - Junhee Han
- Department of Statistics and Institute of Statistics, Hallym University, Chuncheon, South Korea (J.H.)
| | - Jeonghoon Ahn
- Department of Health Convergence, Ewha Womans University, Seoul, South Korea (J.A.)
| | - Young-Hoon Lee
- Department of Preventive Medicine, Wonkwang University School of Medicine, Iksan, South Korea. (G.-J.O., Y.-H.L.)
| | - Won Hyuk Chang
- Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (W.H.C., Y.-H.K.)
| | - Soo Mi Choi
- Division of Chronic Disease Prevention, Korea Disease Control and Prevention Agency, Cheongju, South Korea (S.M.C., S.K.L.)
| | - Seon Kui Lee
- Division of Chronic Disease Prevention, Korea Disease Control and Prevention Agency, Cheongju, South Korea (S.M.C., S.K.L.)
| | - Jongmin Lee
- Department of Rehabilitation Medicine, Konkuk University Medical Center and Konkuk University School of Medicine, Seoul, South Korea (H.H.L., J.L.)
| | - Yun-Hee Kim
- Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (W.H.C., Y.-H.K.).,Department of Health Science and Technology, Department of Medical Device Management and Research, Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea (Y.-H.K.)
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14
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Bowren M, Bruss J, Manzel K, Edwards D, Liu C, Corbetta M, Tranel D, Boes AD. Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping. Brain 2022; 145:1338-1353. [PMID: 35025994 PMCID: PMC9630711 DOI: 10.1093/brain/awac010] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/10/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.
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Affiliation(s)
- Mark Bowren
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Joel Bruss
- Department of Neurology, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Kenneth Manzel
- Department of Neurology, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Dylan Edwards
- Moss Rehabilitation Research Institute, Elkins Park, PA 19027, USA
- Edith Cowan University, Joondalup, WA 6027, Australia
| | - Charles Liu
- Neurorestoration Center and Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine and Padova Neuroscience Center, University of Padua, Padova, PD 32122, Italy
| | - Daniel Tranel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
- Department of Neurology, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Aaron D Boes
- Departments of Neurology, Psychiatry, and Pediatrics, Carver College of Medicine, Iowa City, IA 52242, USA
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15
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Hommelsen M, Viswanathan S, Daun S. Robustness of individualized inferences from longitudinal resting state EEG dynamics. Eur J Neurosci 2022; 56:3613-3644. [PMID: 35445438 DOI: 10.1111/ejn.15673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 03/21/2022] [Accepted: 04/08/2022] [Indexed: 11/27/2022]
Abstract
Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification. We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-EEG was acquired over five consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision-rule, an individual could be re-identified from 2-second samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded-RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day vs same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of longitudinal RS for robust individualized inferences about neurophysiological change in health and disease.
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Affiliation(s)
- Maximilian Hommelsen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | | | - Silvia Daun
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany.,Institute of Zoology, University of Cologne, Cologne, Germany
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16
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Bonkhoff AK, Grefkes C. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 2022; 145:457-475. [PMID: 34918041 PMCID: PMC9014757 DOI: 10.1093/brain/awab439] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 11/02/2021] [Accepted: 11/21/2021] [Indexed: 11/16/2022] Open
Abstract
Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.
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Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
- Medical Faculty, University of Cologne, Cologne, Germany
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17
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Bonkhoff AK, Hope T, Bzdok D, Guggisberg AG, Hawe RL, Dukelow SP, Chollet F, Lin DJ, Grefkes C, Bowman H. Recovery after stroke: the severely impaired are a distinct group. J Neurol Neurosurg Psychiatry 2022; 93:369-378. [PMID: 34937750 DOI: 10.1136/jnnp-2021-327211] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 12/06/2021] [Indexed: 01/13/2023]
Abstract
INTRODUCTION Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently. METHODS We designed a Bayesian hierarchical model to estimate 3-6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5-30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range. RESULTS Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3-6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%). CONCLUSIONS Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.
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Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tom Hope
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada.,Mila - Quebec Artificial Intelligence Institute, Montreal, Québec, Canada.,Canadian Institute for Advanced Research (CIFAR), Montreal, Québec, Canada
| | - Adrian G Guggisberg
- Department of Clinical Neurosciences, Hopitaux Universitaires de Geneve Hopital de Beau-Sejour, Geneva, Switzerland
| | - Rachel L Hawe
- School of Kinesiology, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Sean P Dukelow
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - François Chollet
- Neurology, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - David J Lin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christian Grefkes
- Department of Neurology, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
| | - Howard Bowman
- School of Psychology, University of Birmingham, Birmingham, UK.,School of Computing, University of Kent, Canterbury, UK
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18
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Heras-Romero Y, Morales-Guadarrama A, Santana-Martínez R, Ponce I, Rincón-Heredia R, Poot-Hernández AC, Martínez-Moreno A, Urrieta E, Bernal-Vicente BN, Campero-Romero AN, Moreno-Castilla P, Greig NH, Escobar ML, Concha L, Tovar-Y-Romo LB. Improved post-stroke spontaneous recovery by astrocytic extracellular vesicles. Mol Ther 2022; 30:798-815. [PMID: 34563674 PMCID: PMC8821969 DOI: 10.1016/j.ymthe.2021.09.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/08/2021] [Accepted: 09/20/2021] [Indexed: 02/04/2023] Open
Abstract
Spontaneous recovery after a stroke accounts for a significant part of the neurological recovery in patients. However limited, the spontaneous recovery is mechanistically driven by axonal restorative processes for which several molecular cues have been previously described. We report the acceleration of spontaneous recovery in a preclinical model of ischemia/reperfusion in rats via a single intracerebroventricular administration of extracellular vesicles released from primary cortical astrocytes. We used magnetic resonance imaging and confocal and multiphoton microscopy to correlate the structural remodeling of the corpus callosum and striatocortical circuits with neurological performance during 21 days. We also evaluated the functionality of the corpus callosum by repetitive recordings of compound action potentials to show that the recovery facilitated by astrocytic extracellular vesicles was both anatomical and functional. Our data provide compelling evidence that astrocytes can hasten the basal recovery that naturally occurs post-stroke through the release of cellular mediators contained in extracellular vesicles.
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Affiliation(s)
- Yessica Heras-Romero
- Department of Molecular Neuropathology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Axayacatl Morales-Guadarrama
- Departmento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana Iztapalapa, Mexico City, Mexico; National Center for Medical Imaging and Instrumentation Research, Mexico City, Mexico
| | - Ricardo Santana-Martínez
- Department of Molecular Neuropathology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Isaac Ponce
- Department of Molecular Neuropathology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Ruth Rincón-Heredia
- Microscopy Core Unit, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Augusto César Poot-Hernández
- Bioinformatics Core Unit, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Araceli Martínez-Moreno
- Divisíon de Investigación y Estudios de Posgrado, Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Esteban Urrieta
- Divisíon de Investigación y Estudios de Posgrado, Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Berenice N Bernal-Vicente
- Department of Molecular Neuropathology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Aura N Campero-Romero
- Department of Molecular Neuropathology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Perla Moreno-Castilla
- Laboratory of Neurocognitive Aging, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nigel H Greig
- Drug Design & Development Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Martha L Escobar
- Divisíon de Investigación y Estudios de Posgrado, Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Luis Concha
- Department of Behavioral and Cognitive Neurobiology, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, Mexico
| | - Luis B Tovar-Y-Romo
- Department of Molecular Neuropathology, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Mexico City, Mexico.
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19
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Park CH, Ohn SH. The predictive value of lesion and disconnectome loads for upper limb motor impairment after stroke. Neurol Sci 2021; 43:3097-3104. [PMID: 34843018 DOI: 10.1007/s10072-021-05600-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 09/06/2021] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The putative effect of lesion-induced brain damage on post-stroke upper limb motor impairment can be estimated by overlaying a patient's lesion or its surrogate with key motor areas. We assessed the predictive value of imaging-based brain damage measures for cross-sectional upper limb motor impairment and subsequent upper limb motor outcome after stroke. METHODS In 47 stroke patients, upper limb motor impairment was evaluated with the Upper-Extremity Fugl-Meyer Assessment (UE-FMA) at 2 weeks (2W) and 3 months (3M) post-stroke. Given each patient's lesion identified at 2W, we considered the disconnectome, estimated as an ensemble of structural and functional connections passing through the lesion, as a surrogate of the lesion. The lesion load and the disconnectome load were measured by overlaying the lesion and disconnectome with the corticospinal tract (CST) and motor cortex (MC), and their association with the UE-FMA score at 2W and 3M was assessed. RESULTS Whereas the disconnectome loads on the CST and MC were better in predicting the UE-FMA score at 2W, the lesion load on the CST was better in predicting the UE-FMA score at 3M. Furthermore, when the CST lesion load was combined with the UE-FMA score at 2W, the UE-FMA score at 3M was better predicted, with smaller generalization error, than by using either measure alone. CONCLUSIONS The combination of the CST lesion load and baseline upper limb motor impairment would provide a tailored fusion of imaging and clinical measures for more accurate motor outcome prediction.
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Affiliation(s)
- Chang-Hyun Park
- Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Chemin des Mines 9, 1202, Geneva, Switzerland
| | - Suk Hoon Ohn
- Department of Physical Medicine and Rehabilitation, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 22 Gwanpyeong-ro 170 Beon-gil Dongan-gu, Anyang, Gyeonggi-do, 14068, Republic of Korea.
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20
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Bonkhoff AK, Rehme AK, Hensel L, Tscherpel C, Volz LJ, Espinoza FA, Gazula H, Vergara VM, Fink GR, Calhoun VD, Rost NS, Grefkes C. Dynamic connectivity predicts acute motor impairment and recovery post-stroke. Brain Commun 2021; 3:fcab227. [PMID: 34778761 PMCID: PMC8578497 DOI: 10.1093/braincomms/fcab227] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/29/2021] [Accepted: 08/12/2021] [Indexed: 11/13/2022] Open
Abstract
Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later. A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patients with and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.67 ± 0.01). In contrast, patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on the variability of dynamic connectivity (0.83 ± 0.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortex and putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivity configurations (0.89 ± 0.01) in combination with the initial impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acute impairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery.
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Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, 52425 Juelich, Germany
| | - Anne K Rehme
- Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, 50937 Cologne, Germany
| | - Lukas Hensel
- Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, 50937 Cologne, Germany
| | - Caroline Tscherpel
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, 52425 Juelich, Germany.,Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, 50937 Cologne, Germany
| | - Lukas J Volz
- Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, 50937 Cologne, Germany
| | - Flor A Espinoza
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Harshvardhan Gazula
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA.,Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, 52425 Juelich, Germany.,Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, 50937 Cologne, Germany
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, 52425 Juelich, Germany.,Medical Faculty, University of Cologne, and Department of Neurology, University Hospital Cologne, 50937 Cologne, Germany
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21
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Moore MJ, Vancleef K, Riddoch MJ, Gillebert CR, Demeyere N. Recovery of Visuospatial Neglect Subtypes and Relationship to Functional Outcome Six Months After Stroke. Neurorehabil Neural Repair 2021; 35:823-835. [PMID: 34269128 PMCID: PMC8414826 DOI: 10.1177/15459683211032977] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background/Objective. This study aims to investigate how complex visuospatial neglect behavioural phenotypes predict long-term outcomes, both in terms of neglect recovery and broader functional outcomes after 6 months post-stroke. Methods. This study presents a secondary cohort study of acute and 6-month follow-up data from 400 stroke survivors who completed the Oxford Cognitive Screen's Cancellation Task. At follow-up, patients also completed the Stroke Impact Scale questionnaire. These data were analysed to identify whether any specific combination of neglect symptoms is more likely to result in long-lasting neglect or higher levels of functional impairment, therefore warranting more targeted rehabilitation. Results. Overall, 98/142 (69%) neglect cases recovered by follow-up, and there was no significant difference in the persistence of egocentric/allocentric (X2 [1] = .66 and P = .418) or left/right neglect (X2 [2] = .781 and P = .677). Egocentric neglect was found to follow a proportional recovery pattern with all patients demonstrating a similar level of improvement over time. Conversely, allocentric neglect followed a non-proportional recovery pattern with chronic neglect patients exhibiting a slower rate of improvement than those who recovered. A multiple regression analysis revealed that the initial severity of acute allocentric, but not egocentric, neglect impairment acted as a significant predictor of poor long-term functional outcomes (F [9,300] = 4.742, P < .001 and adjusted R2 = .098). Conclusions. Our findings call for systematic neuropsychological assessment of both egocentric and allocentric neglect following stroke, as the occurrence and severity of these conditions may help predict recovery outcomes over and above stroke severity alone.
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Affiliation(s)
- Margaret J. Moore
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Kathleen Vancleef
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - M. Jane Riddoch
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | | | - Nele Demeyere
- Department of Experimental Psychology, University of Oxford, Oxford, UK
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22
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Bonkhoff AK, Lim JS, Bae HJ, Weaver NA, Kuijf HJ, Biesbroek JM, Rost NS, Bzdok D. Generative lesion pattern decomposition of cognitive impairment after stroke. Brain Commun 2021; 3:fcab110. [PMID: 34189457 PMCID: PMC8233115 DOI: 10.1093/braincomms/fcab110] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/21/2021] [Accepted: 05/20/2021] [Indexed: 01/28/2023] Open
Abstract
Cognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modelling to enable a hemisphere-aware analysis of 1080 acute ischaemic stroke patients with deep profiling ∼3 months after stroke. We show the relevance of the left hemisphere in the prediction of language and memory assessments and relevance of the right hemisphere in the prediction of visuospatial functioning. Global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left was particularly informative about lost naming and memory functions, while damage to these regions on the right was linked to lost visuospatial functioning. Global cognitive impairment was predominantly linked to lesioned tissue in the supramarginal and angular gyrus, the post-central gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischaemic brain tissue damage.
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Affiliation(s)
- Anna K Bonkhoff
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, MA, Boston, USA
| | - Jae-Sung Lim
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Hee-Joon Bae
- Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Nick A Weaver
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - J Matthijs Biesbroek
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, MA, Boston, USA
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada.,Mila-Quebec Artificial Intelligence Institute, Montreal, Canada
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23
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Mossa-Basha M. Using CT and MRI Scans after Intervention for Stroke to Predict Patient Outcomes. Radiology 2021; 300:160-161. [PMID: 33973844 DOI: 10.1148/radiol.2021210754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Mahmud Mossa-Basha
- From the University of Washington Department of Radiology, 1959 NE Pacific St., F-664 Health Sciences Building, Seattle, WA 98195
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24
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Bonkhoff AK, Schirmer MD, Bretzner M, Etherton M, Donahue K, Tuozzo C, Nardin M, Giese A, Wu O, D. Calhoun V, Grefkes C, Rost NS. Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke. Hum Brain Mapp 2021; 42:2278-2291. [PMID: 33650754 PMCID: PMC8046120 DOI: 10.1002/hbm.25366] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 12/30/2022] Open
Abstract
The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of Population Health SciencesGerman Centre for Neurodegenerative Diseases (DZNE)Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Neurosciences and CognitionUniversity of LilleLilleFrance
| | - Mark Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Kathleen Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Carissa Tuozzo
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Marco Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Anne‐Katrin Giese
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Christian Grefkes
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
- Department of NeurologyUniversity Hospital CologneCologneGermany
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
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25
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Bowman H, Bonkhoff A, Hope T, Grefkes C, Price C. Inflated Estimates of Proportional Recovery From Stroke: The Dangers of Mathematical Coupling and Compression to Ceiling. Stroke 2021; 52:1915-1920. [PMID: 33827246 PMCID: PMC7610699 DOI: 10.1161/strokeaha.120.033031] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The proportional recovery rule states that most survivors recover a fixed proportion (≈70%) of lost function after stroke. A strong (negative) correlation between the initial score and subsequent change (outcome minus initial; ie, recovery) is interpreted as empirical support for the proportional recovery rule. However, this rule has recently been critiqued, with a central observation being that the correlation of initial scores with change over time is confounded in the situations in which it is typically assessed. This critique has prompted reassessments of patients' behavioral trajectory following stroke in 2 prominent papers. The first of these, by van der Vliet et al presented an impressive modeling of upper limb deficits following stroke, which avoided the confounded correlation of initial scores with change. The second by Kundert et al reassessed the value of the proportional recovery rule, as classically formulated as the correlation between initial scores and change. They argued that while effective prediction of recovery trajectories of individual patients is not supported by the available evidence, group-level inferences about the existence of proportional recovery are reliable. In this article, we respond to the van der Vliet and Kundert papers by distilling the essence of the argument for why the classic assessment of proportional recovery is confounded. In this respect, we reemphasize the role of mathematical coupling and compression to ceiling in the confounded nature of the correlation of initial scores with change. We further argue that this confound will be present for both individual-level and group-level inference. We then focus on the difficulties that can arise from ceiling effects, even when initial scores are not being correlated with change/recovery. We conclude by emphasizing the need for new techniques to analyze recovery after stroke that are not confounded in the ways highlighted here.
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Affiliation(s)
- Howard Bowman
- School of Psychology, University of Birmingham, United Kingdom (H.B.)
- School of Computing, University of Kent, Canterbury, United Kingdom (H.B.)
| | - Anna Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston (A.B.)
| | - Tom Hope
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom (T.H., C.P.)
| | - Christian Grefkes
- Department of Neurology, University Hospital Cologne, Germany (C.G.)
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Germany (C.G.)
| | - Cathy Price
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom (T.H., C.P.)
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26
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Kline DK, Lin DJ, Cloutier A, Sloane K, Parlman K, Ranford J, Picard-Fraser M, Fox AB, Hochberg LR, Kimberley TJ. Arm Motor Recovery After Ischemic Stroke: A Focus on Clinically Distinct Trajectory Groups. J Neurol Phys Ther 2021; 45:70-78. [PMID: 33707402 DOI: 10.1097/npt.0000000000000350] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Recovery of arm function poststroke is highly variable with some people experiencing rapid recovery but many experiencing slower or limited functional improvement. Current stroke prediction models provide some guidance for clinicians regarding expected motor outcomes poststroke but do not address recovery rates, complicating discharge planning. This study developed a novel approach to defining recovery groups based on arm motor recovery trajectories poststroke. In addition, between-group differences in baseline characteristics and therapy hours were explored. METHODS A retrospective cohort analysis was conducted where 40 participants with arm weakness were assessed 1 week, 6 weeks, 3 months, and 6 months after an ischemic stroke. Arm recovery trajectory groups were defined on the basis of timing of changes in the Fugl-Meyer Assessment Upper Extremity (FMA-UE), at least the minimal clinically important difference (MCID), 1 week to 6 weeks or 6 weeks to 6 months. Three recovery trajectory groups were defined: Fast (n = 19), Extended (n = 12), and Limited (n = 9). Between-group differences in baseline characteristics and therapy hours were assessed. Associations between baseline characteristics and group membership were also determined. RESULTS Three baseline characteristics were associated with trajectory group membership: FMA-UE, NIH Stroke Scale, and Barthel Index. The Fast Recovery group received the least therapy hours 6 weeks to 6 months. No differences in therapy hours were observed between Extended and Limited Recovery groups at any time points. DISCUSSION AND CONCLUSIONS Three clinically relevant recovery trajectory groups were defined using the FMA-UE MCID. Baseline impairment, overall stroke severity, and dependence in activities of daily living were associated with group membership and therapy hours differed between groups. Stratifying individuals by recovery trajectory early poststroke could offer additional guidance to clinicians in discharge planning. (See Supplemental Digital Content 1 for Video Abstract, available at: http://links.lww.com/JNPT/A337.).
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Affiliation(s)
- Danielle K Kline
- Department of Physical Therapy (D.K.K., M.P.-F., T.J.K.) and Center for Interprofessional Studies and Innovation (A.B.F.), MGH Institute of Health Professions, Boston, Massachusetts; Center for Neurotechnology and Neurorecovery, Department of Neurology (D.J.L., A.C., K.S., L.R.H.), Divisions of Neurocritical Care and Stroke, Department of Neurology (D.J.L., L.R.H.), Department of Physical Therapy (K.P.), and Department of Occupational Therapy (J.R.), Massachusetts General Hospital, Boston; VA RR&D Center for Neurotechnology and Neurorecovery, Providence, Rhode Island (L.R.H.); and School of Engineering, Brown University, Providence, Rhode Island (L.R.H.)
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Grefkes C, Fink GR. Recovery from stroke: current concepts and future perspectives. Neurol Res Pract 2020; 2:17. [PMID: 33324923 PMCID: PMC7650109 DOI: 10.1186/s42466-020-00060-6] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 04/22/2020] [Indexed: 12/31/2022] Open
Abstract
Stroke is a leading cause of acquired, permanent disability worldwide. Although the treatment of acute stroke has been improved considerably, the majority of patients to date are left disabled with a considerable impact on functional independence and quality of life. As the absolute number of stroke survivors is likely to further increase due to the demographic changes in our aging societies, new strategies are needed in order to improve neurorehabilitation. The most critical driver of functional recovery post-stroke is neural reorganization. For developing novel, neurobiologically informed strategies to promote recovery of function, an improved understanding of the mechanisms enabling plasticity and recovery is mandatory. This review provides a comprehensive survey of recent developments in the field of stroke recovery using neuroimaging and non-invasive brain stimulation. We discuss current concepts of how the brain reorganizes its functional architecture to overcome stroke-induced deficits, and also present evidence for maladaptive effects interfering with recovery. We demonstrate that the combination of neuroimaging and neurostimulation techniques allows a better understanding of how brain plasticity can be modulated to promote the reorganization of neural networks. Finally, neurotechnology-based treatment strategies allowing patient-tailored interventions to achieve enhanced treatment responses are discussed. The review also highlights important limitations of current models, and finally closes with possible solutions and future directions.
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Affiliation(s)
- Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, 52425 Jülich, Germany
- Medical Faculty, University of Cologne & Department of Neurology, University Hospital Cologne, 50924 Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, 52425 Jülich, Germany
- Medical Faculty, University of Cologne & Department of Neurology, University Hospital Cologne, 50924 Cologne, Germany
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