More Like this

1. Shoulder abduction loading affects motor coordination in individuals with chronic stroke, informing targeted rehabilitation

   https://doi.org/10.1109/BioRob49111.2020.9224454  

   Kalinowska, Aleksandra ; Rudy, Kyra ; Schlafly, Millicent ; Fitzsimons, Kathleen ; Dewald, Julius P ; Murphey, Todd D ( November 2020 , 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob))

   null (Ed.)

   Individuals post stroke experience motor impair- ments, such as loss of independent joint control, weakness, and delayed movement initiation, leading to an overall reduction in arm function. Their motion becomes slower and more discoordinated, making it difficult to complete timing- sensitive tasks, such as balancing a glass of water or carrying a bowl with a ball inside it. Understanding how the stroke- induced motor impairments interact with each other can help design assisted training regimens for improved recovery. In this study, we investigate the effects of abnormal joint coupling patterns induced by flexion synergy on timing-sensitive motor coordination in the paretic upper limb. We design a virtual ball-in-bowl task that requires fast movements for optimal performance and implement it on a robotic system, capable of providing varying levels of abduction loading at the shoulder. We recruit 12 participants (6 individuals with chronic stroke and 6 unimpaired controls) and assess their skill at the task at 3 levels of loading, defined by the vertical force applied at the robot end-effector. Our results show that, for individuals with stroke, loading has a significant effect on their ability to generate quick coordinated motion. With increases in loading, their overall task performance decreases and they are less able to compensate for ball dynamics—frequency analysis of their motion indicates that abduction loading weakens their ability to generate movements at the resonant frequency of the dynamic task. This effect is likely due to an increased reliance on lower resolution indirect motor pathways in individuals post stroke. Given the inter-dependency of loading and dynamic task performance, we can create targeted robot-aided training protocols focused on improving timing-sensitive motor control, similar to existing progressive loading therapies, which have shown efficacy for expanding reachable workspace post stroke. 

   more » « less
2. Data-Driven Quantitation of Movement Abnormality after Stroke

   https://doi.org/10.3390/bioengineering10060648  

   Parnandi, Avinash ; Kaku, Aakash ; Venkatesan, Anita ; Pandit, Natasha ; Fokas, Emily ; Yu, Boyang ; Kim, Grace ; Nilsen, Dawn ; Fernandez-Granda, Carlos ; Schambra, Heidi ( June 2023 , Bioengineering)

   Stroke commonly affects the ability of the upper extremities (UEs) to move normally. In clinical settings, identifying and measuring movement abnormality is challenging due to the imprecision and impracticality of available assessments. These challenges interfere with therapeutic tracking, communication, and treatment. We thus sought to develop an approach that blends precision and pragmatism, combining high-dimensional motion capture with out-of-distribution (OOD) detection. We used an array of wearable inertial measurement units to capture upper body motion in healthy and chronic stroke subjects performing a semi-structured, unconstrained 3D tabletop task. After data were labeled by human coders, we trained two deep learning models exclusively on healthy subject data to classify elemental movements (functional primitives). We tested these healthy subject-trained models on previously unseen healthy and stroke motion data. We found that model confidence, indexed by prediction probabilities, was generally high for healthy test data but significantly dropped when encountering OOD stroke data. Prediction probabilities worsened with more severe motor impairment categories and were directly correlated with individual impairment scores. Data inputs from the paretic UE, rather than trunk, most strongly influenced model confidence. We demonstrate for the first time that using OOD detection with high-dimensional motion data can reveal clinically meaningful movement abnormality in subjects with chronic stroke. 

   more » « less
3. Serial sarcomere number is substantially decreased within the paretic biceps brachii in individuals with chronic hemiparetic stroke

   https://doi.org/10.1073/pnas.2008597118  

   Adkins, Amy N. ; Dewald, Julius P. A. ; Garmirian, Lindsay P. ; Nelson, Christa M. ; Murray, Wendy M. ( June 2021 , Proceedings of the National Academy of Sciences)

   A muscle’s structure, or architecture, is indicative of its function and is plastic; changes in input to or use of the muscle alter its architecture. Stroke-induced neural deficits substantially alter both input to and usage of individual muscles. We combined in vivo imaging methods (second-harmonic generation microendoscopy, extended field-of-view ultrasound, and fat-suppression MRI) to quantify functionally meaningful architecture parameters in the biceps brachii of both limbs of individuals with chronic hemiparetic stroke and in age-matched, unimpaired controls. Specifically, serial sarcomere number (SSN) and physiological cross-sectional area (PCSA) were calculated from data collected at three anatomical scales: sarcomere length, fascicle length, and muscle volume. The interlimb differences in SSN and PCSA were significantly larger for stroke participants than for participants without stroke (P= 0.0126 andP= 0.0042, respectively), suggesting we observed muscle adaptations associated with stroke rather than natural interlimb variability. The paretic biceps brachii had ∼8,200 fewer serial sarcomeres and ∼2 cm²smaller PCSA on average than the contralateral limb (bothP< 0.0001). This was manifested by substantially smaller muscle volumes (112 versus 163 cm³), significantly shorter fascicles (11.0 versus 14.0 cm;P< 0.0001), and comparable sarcomere lengths (3.55 versus 3.59 μm;P= 0.6151) between limbs. Most notably, this study provides direct evidence of the loss of serial sarcomeres in human muscle observed in a population with neural impairments that lead to disuse and chronically place the affected muscle at a shortened position. This adaptation is consistent with functional consequences (increased passive resistance to elbow extension) that would amplify already problematic, neurally driven motor impairments.

    

   more » « less
4. Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection

   https://doi.org/10.1186/s12984-022-01099-z  

   Jabri, Safa ; Carender, Wendy ; Wiens, Jenna ; Sienko, Kathleen H. ( December 2022 , Journal of NeuroEngineering and Rehabilitation)

   Vestibular deficits can impair an individual’s ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers.

   Thirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task.

   Several models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed.

   These findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.

    

   more » « less
5. A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease

   https://doi.org/10.3389/fnagi.2022.810873  

   Tabarestani, Solale ; Eslami, Mohammad ; Cabrerizo, Mercedes ; Curiel, Rosie E. ; Barreto, Armando ; Rishe, Naphtali ; Vaillancourt, David ; DeKosky, Steven T. ; Loewenstein, David A. ; Duara, Ranjan ; et al ( May 2022 , Frontiers in Aging Neuroscience)

   With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject’s label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other. 

   more » « less