1,721,000 research outputs found
Understanding Stroke Rehabilitation Progression in a Robotic Rehabilitation Trial
No full textStroke is one of the leading causes of adult disability worldwide, leaving many individuals requiring rehabilitation to regain independence. A critical component to any rehabilitation program is progression, which is the ability of therapy program to change according to patient improvement. Currently, there is little known about therapy progression, which negatively impacts the optimization of rehabilitation programs. Therefore, the purpose of this thesis was to better understand how stroke survivor’s kinematics change throughout therapy in order to inform future rehabilitation programs. The first step in answering this question was to understand how motor learning contributes to recovery after stroke, which is explored in Chapter Two. Next, a therapy program was needed in order to study how stroke survivors progress during rehabilitation. The motor learning and stroke recovery principles discussed in Chapter Two were then used to inform the development of tasks for a robotic rehabilitation program for stroke survivors. The development, and subsequent testing, of the tasks are discussed in Chapter Three. It was found that this robotic therapy program was feasible after stroke and has the potential to improve clinical outcomes when compared only to standard of care. Using the results from this pilot study, the robotic therapy tasks were refined, as well as the study protocol, and gave rise to a Phase II Clinical Trial (RESTORE). As discussed in Chapter Four, subacute stroke patients were recruited to receive 20 days of robotic therapy for 1 or 2-hours a day, beginning either 5-9 days or 21-25 days post-stroke. Following completion of the intervention, changes in the participants’ kinematics measuring speed, accuracy, and smoothness of movements were examined. It was found that kinematics of directional error and hand path ratio (measures of accuracy), as well as smoothness, predominantly increased during the first 5 days of the intervention. Movement speed and percent time in target (a measure of accuracy), on the other hand, continued to improve throughout the intervention. These findings should be interpreted with caution due to small sample size but may be used to inform the progression of future robotic rehabilitation tasks
Treatment of Persistent Headache Attributed to Mild Traumatic Injury to the Head in Patients with Persistent Post Concussion Symptoms using Repetitive Transcranial Magnetic Stimulation
No full textPersistent post-traumatic headache (PTH) following a mild traumatic brain injury (mTBI) is one of the most prominent and highly-reported persistent post-concussion symptoms (PPCS). Non-pharmacologic treatment alternatives, including non-invasive neurostimulation technologies, have been proposed for use. After a systematic review investigating transcranial magnetic and direct current stimulation (TMS/tDCS) for management of headache, we designed a double-blind, sham-controlled, randomized trial investigating repetitive TMS (rTMS) for treatment of persistent PTH in patients with PPCS. Our primary outcome was a change in headache frequency and severity at one-month post-treatment. Twenty participants underwent rTMS therapy to the left dorsolateral prefrontal cortex (DLPFC). Headache diaries and clinical questionnaires assessing function, cognition, and mood were completed. Headache severity demonstrated a significant time effect, while headache frequency demonstrated a reduction across all time points for both the real and sham groups, based on descriptive analysis. Secondary outcomes demonstrated improvements in function, reduced PPCS, and depression in the real-treatment group, with no serious adverse effects. A phase II study is warranted
Association of Dynamic Functional Cerebral States with Post-Stroke Aphasia Recovery
No full textPost-stroke aphasia, a language impairment affecting one-third of stroke survivors, impacts millions worldwide by hindering communication and social interaction. Despite its prevalence and uncertain recovery outcomes, there has been limited investigation into dynamic functional brain network fluctuations in aphasia recovery. This thesis explores using resting-state fMRI (rs-fMRI) to predict recovery, focusing on dynamic connectivity measures. These capture temporal fluctuations in brain connectivity and may offer more predictive information than static measures, which average connectivity over time. Such insights could enhance treatment and prognosis in aphasia rehabilitation. In this study, 62 subjects with acute post-stroke aphasia were evaluated at two time points (T0: 5.4 days post-stroke; T1: 6–9 months post-stroke). Subjects (30.65% female; mean age 64 ± 15 years) were assessed at the University Medical Centre Freiburg (Germany) between 2011 and 2019. Recovery was measured using the German Aachen Aphasia Test (AAT), with all patients completing at least the Token and Spontaneous Speech subtests. Rs-fMRI assessed functional connectivity between eight regions of interest (ROIs) involved in language and cognition: Broca’s and Wernicke’s areas (left and right hemispheres), and default mode network regions—medial prefrontal cortex, posterior cingulate cortex, and angular gyri (left and right). Static and dynamic connectivity were computed; dynamic measures used 30-second sliding windows and were clustered into ten distinct states using k-means clustering. Regression analyses examined whether static connectivity, dynamic variability, dwell time, state occupancy, and transition frequency predicted AAT scores. Results showed that while connectivity measures were not significantly associated with AAT scores alone, they were significantly related to improvement between T0 and T1 (p < 0.05). This association was also significant when considering only T0 connectivity to predict future improvement. Significant interaction terms indicated that associations depended on initial scores; higher initial scores reduced the potential for large improvements, showing a moderation effect of initial severity. Brain regions and states contributing to these associations differed between static and dynamic measures, reflecting their complementary value. The specific focus of using dynamic states as predictors for post-stroke aphasia recovery is novel. Measures derived from these dynamic states—percentage of time in states, transition frequencies, and dwell times—show great potential to predict behavioural outcomes. This suggests that dynamic rs-fMRI is a promising approach to guide future treatments and assist in the clinical management of post-stroke aphasia patients to improve outcomes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Machine Learning-based Tools for Predicting Neurological Deterioration in Non-operative Degenerative Cervical Myelopathy Patients
No full textBackground
Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. DCM is characterized by the progressive compression of the cervical spinal cord as a result of vertebral column spondylotic degeneration. While surgery is currently the only effective treatment of DCM, clinical guidelines remain unclear on the benefit of surgery for patients with mild forms of DCM. This thesis utilizes machine learning based tools to elucidate clinical and imaging indicators of neurological deterioration in non-operative DCM patients. This thesis is comprised of two independent studies, one focussing on clustering patient groups at risk of deteriorating and the second focusing on developing a supervised machine learning (ML) model capable of predicting neurological deterioration. Methods DCM patients recruited from 2016-2023 underwent MRI scans, including T2w, diffusion tensor imaging (DTI), and magnetization transfer (MT) scans, along with a series of clinical metrics. These were collected every six months, resulting in 124 overall entries. T2w imaging scans were assessed for spinal cord compression, and cervical spinal canal diameter (SCD) was measured. Clustering was achieved through PaCMAP dimensionality reduction and K-Means clustering for the first study. Logistic regressions, support vector classifiers, and random forest classifiers were trained and tested for the second study. Findings We elucidated five patient groups with their associated risks of deterioration, according to both SCD range and cord compression pattern. Furthermore, we found that the compression pattern is unimportant at SCD extremes (≤14.5 mm or >15.75mm). Our best-performing supervised ML model had a testing balanced accuracy of 0.830 and ROC-AUC of 0.87. The three most important metrics for predicting neurological deterioration based on the model were MT ratio above the maximally compressed cervical level in the dorsal and ventral funiculi, and moderate tingling in the arm, shoulder, or hand (quickDASH item 10). Significance and Conclusion SCD and focal cord compression alone do not reliably predict an increased risk of neurological deterioration, their combination does. Furthermore, MT and DTI scans improve the prediction of neurological deterioration in non-operative mild DCM patients
- …
