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  • 1
    Online Resource
    Online Resource
    Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning
    SAGE Publications ; 2021
    In:  Multiple Sclerosis Journal Vol. 27, No. 1 ( 2021-01), p. 107-116
    Details
    In: Multiple Sclerosis Journal, SAGE Publications, Vol. 27, No. 1 ( 2021-01), p. 107-116
    Abstract: To build a model to predict cognitive status reflecting structural, functional, and white matter integrity changes in early multiple sclerosis (MS). Methods: Based on Symbol Digit Modalities Test (SDMT) performance, 183 early MS patients were assigned “lower” or “higher” performance groups. Three-dimensional (3D)-T2, T1, diffusion weighted, and resting-state magnetic resonance imaging (MRI) data were acquired in 3T. Using Random Forest, five models were trained to classify patients into two groups based on 1—demographic/clinical, 2—lesion volume/location, 3—local/global tissue volume, 4—local/global diffusion tensor imaging, and 5—whole-brain resting-state-functional-connectivity measures. In a final model, all important features from previous models were concatenated. Area under the receiver operating characteristic curve (AUC) values were calculated to evaluate classifier performance. Results: The highest AUC value (0.90) was achieved by concatenating all important features from neuroimaging models. The top 10 contributing variables included volumes of bilateral nucleus accumbens and right thalamus, mean diffusivity of left cingulum-angular bundle, and functional connectivity among hubs of seven large-scale networks. Conclusion: These results provide an indication of a non-random brain pattern mostly compromising areas involved in attentional processes specific to patients who perform worse in SDMT. High accuracy of the final model supports this pattern as a potential neuroimaging biomarker of subtle cognitive changes in early MS.
    Type of Medium: Online Resource
    ISSN: 1352-4585 , 1477-0970
    URL: Article
    DOI: 10.1177/1352458520958362
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
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  • 2
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    Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke
    SAGE Publications ; 2020
    In:  Neurorehabilitation and Neural Repair Vol. 34, No. 5 ( 2020-05), p. 428-439
    Details
    In: Neurorehabilitation and Neural Repair, SAGE Publications, Vol. 34, No. 5 ( 2020-05), p. 428-439
    Abstract: Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R 2 . Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median [Formula: see text] P 〈 .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.
    Type of Medium: Online Resource
    ISSN: 1545-9683 , 1552-6844
    URL: Article
    DOI: 10.1177/1545968320909796
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
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    detail.hit.zdb_id: 1491637-X
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  • 3
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    Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery
    SAGE Publications ; 2024
    In:  Neurorehabilitation and Neural Repair Vol. 38, No. 5 ( 2024-05), p. 386-398
    Details
    In: Neurorehabilitation and Neural Repair, SAGE Publications, Vol. 38, No. 5 ( 2024-05), p. 386-398
    Abstract: Stroke is a leading cause of disability worldwide which can cause significant and persistent upper limb (UL) impairment. It is difficult to predict UL motor recovery after stroke and to forecast the expected outcomes of rehabilitation interventions during the acute and subacute phases when using clinical data alone. Accurate prediction of response to treatment could allow for more timely and targeted interventions, thereby improving recovery, resource allocation, and reducing the economic impact of post-stroke disability. Initial motor impairment is currently the strongest predictor of post-stroke motor recovery. Despite significant progress, current prediction models could be refined with additional predictors, and an emphasis on the time dependency of patient-specific predictions of UL recovery profiles. In the current paper a panel of experts provide their opinion on additional predictors and aspects of the literature that can help advance stroke outcome prediction models. Potential strategies include close attention to post-stroke data collection timeframes and adoption of individual-computerized modeling methods connected to a patient’s health record. These models should account for the non-linear and the variable recovery pattern of spontaneous neurological recovery. Additionally, input data should be extended to include cognitive, genomic, sensory, neural injury, and function measures as additional predictors of recovery. The accuracy of prediction models may be further improved by including standardized measures of outcome. Finally, we consider the potential impact of refined prediction models on healthcare costs.
    Type of Medium: Online Resource
    ISSN: 1545-9683 , 1552-6844
    URL: Article
    DOI: 10.1177/15459683241237975
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2024
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    detail.hit.zdb_id: 1491637-X
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  • 4
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    Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis
    Wiley ; 2021
    In:  Brain and Behavior Vol. 11, No. 10 ( 2021-10)
    Details
    In: Brain and Behavior, Wiley, Vol. 11, No. 10 ( 2021-10)
    Abstract: In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim‐ lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim‐ lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim‐ lesions. Methods Ninety‐six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) 〈 2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim‐ or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim‐ lesions. Classification performance was assessed using the area under ROC curve (AUC). Results The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim‐ lesions (AUC = 0.67 vs 0.63, p ‐value  〈  .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories. Conclusion rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim‐ lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS‐related disability.
    Type of Medium: Online Resource
    ISSN: 2162-3279 , 2162-3279
    URL: Issue
    URL: Article
    DOI: 10.1002/brb3.v11.10
    DOI: 10.1002/brb3.2353
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2623587-0
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  • 5
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    Quantifying the Relationship Between Multiple Sclerosis Lesions and Depression
    Elsevier BV ; 2024
    In:  Biological Psychiatry Vol. 95, No. 12 ( 2024-06), p. 1058-1059
    Details
    In: Biological Psychiatry, Elsevier BV, Vol. 95, No. 12 ( 2024-06), p. 1058-1059
    Type of Medium: Online Resource
    ISSN: 0006-3223
    URL: Article
    DOI: 10.1016/j.biopsych.2024.04.008
    RVK:
    XA 10000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 209434-4
    SSG: 12
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  • 6
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    Comparison of classification methods for tissue outcome after ischaemic stroke
    Wiley ; 2019
    In:  European Journal of Neuroscience Vol. 50, No. 10 ( 2019-11), p. 3590-3598
    Details
    In: European Journal of Neuroscience, Wiley, Vol. 50, No. 10 ( 2019-11), p. 3590-3598
    Abstract: In acute ischaemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision‐making. This tissue may be identified with suitable classification methods from magnetic resonance imaging data. The aim of the present study was to assess and compare the performance of five popular classification methods (adaptive boosting, logistic regression, artificial neural networks, random forest and support vector machine) in identifying tissue at high risk of infarction on human voxel‐based brain imaging data. The classification methods were used with eight MRI parameters, including diffusion‐weighted imaging and perfusion‐weighted imaging obtained in 55 patients. The five criteria used to assess the performance of the methods were the area under the receiver operating curve (AUC roc ), the area under the precision–recall curve (AUC pr ), sensitivity, specificity and the Dice coefficient. The methods performed equally in terms of sensitivity and specificity, while the results of AUC roc and the Dice coefficient were significantly better for adaptive boosting, logistic regression, artificial neural networks and random forest. However, there was no statistically significant difference between the performances of these five classification methods regarding AUC pr , which was the main comparison metric. Machine learning methods can provide valuable prognostic information using multimodal imaging data in acute ischaemic stroke, which in turn can assist in developing personalized treatment decision for clinicians after a thorough validation of methods with an independent data set.
    Type of Medium: Online Resource
    ISSN: 0953-816X , 1460-9568
    URL: Issue
    URL: Article
    DOI: 10.1111/ejn.v50.10
    DOI: 10.1111/ejn.14507
    Language: English
    Publisher: Wiley
    Publication Date: 2019
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    SSG: 12
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  • 7
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    Investigating Superficial White Matter Integrity in Early MS Using Machine Learning (1955)
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Neurology Vol. 96, No. 15_supplement ( 2021-04-13)
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 96, No. 15_supplement ( 2021-04-13)
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.96.15_supplement.1955
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2021
    detail.hit.zdb_id: 1491874-2
    detail.hit.zdb_id: 207147-2
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  • 8
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    Data_Sheet_1.PDF
    Frontiers Media SA ; 2021
    In:  Frontiers in Neuroscience Vol. 15 ( 2021-12-13)
    Details
    In: Frontiers in Neuroscience, Frontiers Media SA, Vol. 15 ( 2021-12-13)
    Abstract: Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status. Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks. Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS & lt;2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC). Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 ( p & lt;0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 ( p & lt; 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability. Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.
    Type of Medium: Online Resource
    ISSN: 1662-453X
    URL: Article
    URL: Article
    DOI: 10.3389/fnins.2021.763966
    DOI: 10.3389/fnins.2021.763966.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2411902-7
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  • 9
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    Machine learning to investigate superficial white matter integrity in early multiple sclerosis
    Wiley ; 2022
    In:  Journal of Neuroimaging Vol. 32, No. 1 ( 2022-01), p. 36-47
    Details
    In: Journal of Neuroimaging, Wiley, Vol. 32, No. 1 ( 2022-01), p. 36-47
    Abstract: This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC). Methods Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age‐ and sex‐matched HC. Thickness of 68 cortical regions and resting‐state functional‐connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank‐sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1‐SWM, 2‐DWM, 3‐SWM and DWM, 4‐cortical thickness, or 5‐RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5‐folds cross‐validation. Area under the receiver operating characteristic curve (AUC roc ) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUC roc values. Results Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre‐ and post‐central cortices ( p 〈 .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUC roc = 0.75). The SWM model provided higher AUC roc (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all‐features (0.68) models ( p 〈 .001 for all). Conclusion Our results reveal a non‐random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.
    Type of Medium: Online Resource
    ISSN: 1051-2284 , 1552-6569
    URL: Issue
    URL: Article
    DOI: 10.1111/jon.v32.1
    DOI: 10.1111/jon.12934
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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  • 10
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    Identifying The Role Of A Hippocampal-thalamic Memory Network In Ms Using A Machine Learning Approach (556)
    Ovid Technologies (Wolters Kluwer Health) ; 2020
    In:  Neurology Vol. 94, No. 15_supplement ( 2020-04-14)
    Details
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 94, No. 15_supplement ( 2020-04-14)
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    URL: Article
    DOI: 10.1212/WNL.94.15_supplement.556
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
    detail.hit.zdb_id: 1491874-2
    detail.hit.zdb_id: 207147-2
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