"Use it and improve it, or lose it" is one of the axioms of motor therapy after stroke. There is, however, little understanding of the interactions between arm function and use in humans post-stroke. Here, we explored putative non-linear interactions between upper extremity function and use by developing a first-order dynamical model of stroke recovery with longitudinal data from participants receiving constraint induced movement therapy (CIMT) in the EXCITE clinical trial. Using a Bayesian regression framework, we systematically compared this model with competitive models that included, or not, interactions between function and use. Model comparisons showed that the model with the predicted interactions between arm function and use was the best fitting model. Furthermore, by comparing the model parameters before and after CIMT intervention in participants receiving the intervention one year after randomization, we found that therapy increased the parameter that controls the effect of arm function on arm use. Increase in this parameter, which can be thought of as the confidence to use the arm for a given level of function, lead to increase in spontaneous use after therapy compared to before therapy.
by
Chethan Pandarinath;
Daniel J. O'Shea;
Jasmine Collins;
Rafal Jozefowicz;
Sergey D. Stavisky;
Jonathan C. Kao;
Eric M. Trautmann;
Matthew T. Kaufman;
Stephen I. Ryu;
Leigh R. Hochberg;
Jaimie M. Henderson;
Krishna V. Shenoy;
L. F. Abbott;
David Sussillo
Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.
Muscle spindle proprioceptive receptors play a primary role in encoding the effects of external mechanical perturbations to the body. During externally-imposed stretches of passive, i.e. electrically-quiescent, muscles, the instantaneous firing rates (IFRs) of muscle spindles are associated with characteristics of stretch such as length and velocity. However, even in passive muscle, there are history-dependent transients of muscle spindle firing that are not uniquely related to muscle length and velocity, nor reproduced by current muscle spindle models. These include acceleration-dependent initial bursts, increased dynamic response to stretch velocity if a muscle has been isometric, and rate relaxation, i.e., a decrease in tonic IFR when a muscle is held at a constant length after being stretched. We collected muscle spindle spike trains across a variety of muscle stretch kinematic conditions, including systematic changes in peak length, velocity, and acceleration. We demonstrate that muscle spindle primary afferents in passive muscle fire in direct relationship to muscle force-related variables, rather than length-related variables. Linear combinations of whole muscle-tendon force and the first time derivative of force (dF/dt) predict the entire time course of transient IFRs in muscle spindle Ia afferents during stretch (i.e., lengthening) of passive muscle, including the initial burst, the dynamic response to lengthening, and rate relaxation following lengthening. Similar to acceleration scaling found previously in postural responses to perturbations, initial burst amplitude scaled equally well to initial stretch acceleration or dF/dt, though later transients were only described by dF/dt. The transient increase in dF/dt at the onset of lengthening reflects muscle short-range stiffness due to cross-bridge dynamics. Our work demonstrates a critical role of muscle cross-bridge dynamics in history-dependent muscle spindle IFRs in passive muscle lengthening conditions relevant to the detection and sensorimotor response to mechanical perturbations to the body, and to previously-described history-dependence in perception of limb position.
Electrical stimulation of the central and peripheral nervous systems - such as deep brain stimulation, spinal cord stimulation, and epidural cortical stimulation are common therapeutic options increasingly used to treat a large variety of neurological and psychiatric conditions. Despite their remarkable success, there are limitations which if overcome, could enhance outcomes and potentially reduce common side-effects. Micromagnetic stimulation (μMS) was introduced to address some of these limitations. One of the most remarkable properties is that μMS is theoretically capable of activating neurons with specific axonal orientations. Here, we used computational electromagnetic models of the μMS coils adjacent to neuronal tissue combined with axon cable models to investigate μMS orientation-specific properties. We found a 20-fold reduction in the stimulation threshold of the preferred axonal orientation compared to the orthogonal direction. We also studied the directional specificity of μMS coils by recording the responses evoked in the inferior colliculus of rodents when a pulsed magnetic stimulus was applied to the surface of the dorsal cochlear nucleus. The results confirmed that the neuronal responses were highly sensitive to changes in the μMS coil orientation. Accordingly, our results suggest that μMS has the potential of stimulating target nuclei in the brain without affecting the surrounding white matter tracts.
The accumulation of pathologic protein fragments is common in neurodegenerative disorders. We have recently identified in Alzheimers disease (AD) the aggregation of the U1-70K splicing factor and abnormal RNA processing. Here, we present that U1-70K can be cleaved into an N-terminal truncation (N40K) in ∼50% of AD cases, and the N40K abundance is inversely proportional to the total level of U1-70K. To map the cleavage site, we compared tryptic peptides of N40K and stable isotope labeled U1-70K by liquid chromatography-tandem mass spectrometry (MS), revealing that the proteolysis site is located in a highly repetitive and hydrophilic domain of U1-70K. We then adapted Western blotting to map the cleavage site in two steps: (i) mass spectrometric analysis revealing that U1-70K and N40K share the same N-termini and contain no major modifications; (ii) matching N40K with a series of six recombinant U1-70K truncations to define the cleavage site within a small region (Arg300 ± 6 residues). Finally, N40K expression led to substantial degeneration of rat primary hippocampal neurons. In summary, we combined multiple approaches to identify the U1-70K proteolytic site and found that the N40K fragment might contribute to neuronal toxicity in Alzheimers disease.
Despite a key role of amyloid-beta (Aβ) in Alzheimer's disease (AD), mechanisms that link Aβ plaques to tau neurofibrillary tangles and cognitive decline still remain poorly understood. The purpose of this study was to quantify proteins in the sarkosyl-insoluble brain proteome correlated with Aβ and tau insolubility in the asymptomatic phase of AD (AsymAD) and through mild cognitive impairment (MCI) and symptomatic AD. Employing label-free mass spectrometry-based proteomics, we quantified 2711 sarkosyl-insoluble proteins across the prefrontal cortex from 35 individual cases representing control, AsymAD, MCI and AD. Significant enrichment of Aβ and tau in AD was observed, which correlated with neuropathological measurements of plaque and tau tangle density, respectively. Pairwise correlation coefficients were also determined for all quantified proteins to Aβ and tau, across the 35 cases. Notably, six of the ten most correlated proteins to Aβ were U1 small nuclear ribonucleoproteins (U1 snRNPs). Three of these U1 snRNPs (U1A, SmD and U1-70K) also correlated with tau consistent with their association with tangle pathology in AD. Thus, proteins that cross-correlate with both Aβ and tau, including specific U1 snRNPs, may have potential mechanistic roles in linking Aβ plaques to tau tangle pathology during AD progression.
Huntington’s disease (HD) is a neurodegenerative disease caused by an expansion of CAG trinucleotide repeat (polyglutamine [polyQ]) in the huntingtin (HTT) gene, which leads to the formation of mutant HTT (mHTT) protein aggregates. In the nervous system, an accumulation of mHTT protein results in glutamate-mediated excitotoxicity, proteosome instability, and apoptosis. Although HD pathogenesis has been extensively studied, effective treatment of HD has yet to be developed. Therapeutic discovery research in HD has been reported using yeast, cells derived from transgenic animal models and HD patients, and induced pluripotent stem cells from patients. A transgenic nonhuman primate model of HD (HD monkey) shows neuropathological, behavioral, and molecular changes similar to an HD patient. In addition, neural progenitor cells (NPCs) derived from HD monkeys can be maintained in culture and differentiated to neural cells with distinct HD cellular phenotypes including the formation of mHTT aggregates, intranuclear inclusions, and increased susceptibility to oxidative stress. Here, we evaluated the potential application of HD monkey NPCs and neural cells as an in vitro model for HD drug discovery research.
The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI). Because patients with MCI often progress to Alzheimer's Disease (AD), the VPC may be useful in predicting the onset of AD. VPC uses noninvasive eye tracking to identify how subjects view novel and repeated visual stimuli. Healthy control subjects demonstrate memory for the repeated stimuli by spending more time looking at the novel images, i.e., novelty preference. Here, we report an application of machine learning methods from computer science to improve the accuracy of detecting MCI by modeling eye movement characteristics such as fixations, saccades, and re-fixations during the VPC task. These characteristics are represented as features provided to automatic classification algorithms such as Support Vector Machines (SVMs). Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information. These results demonstrate the effectiveness of applying machine-learning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.
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Christy L. Ludlow;
Rickie Domangue;
Dinesh Sharma;
Hyder A Jinnah;
Joel S. Perlmutter;
Gerald Berke;
Christine Sapienza;
Marshall E. Smith;
Joel H. Blumin;
Carrie E. Kalata;
Karen Blindauer;
Michael Johns;
Edie Hapner;
Archie Harmon;
Randal Paniello;
Charles H. Adler;
Lisa Crujido;
David G. Lott;
Stephen F. Bansberg;
Nicholas Barone;
Teresa Drulia;
Glenn Stebbins
IMPORTANCE A roadblock for research on adductor spasmodic dysphonia (ADSD), abductor SD (ABSD), voice tremor (VT), and muscular tension dysphonia (MTD) is the lack of criteria for selecting patients with these disorders.
OBJECTIVE To determine the agreement among experts not using standard guidelines to classify patients with ABSD, ADSD, VT, and MTD, and develop expert consensus attributes for classifying patients for research.
DESIGN, SETTING AND PARTICIPANTS From 2011 to 2016, a multicenter observational study examined agreement among blinded experts when classifying patients with ADSD, ABSD, VT or MTD (first study). Subsequently, a 4-stage Delphi method study used reiterative stages of review by an expert panel and 46 community experts to develop consensus on attributes to be used for classifying patients with the 4 disorders (second study). The study used a convenience sample of 178 patients clinically diagnosed with ADSD, ABSD, VT MTD, vocal fold paresis/paralysis, psychogenic voice disorders, or hypophonia secondary to Parkinson disease. Participants were aged 18 years or older, without laryngeal structural disease or surgery for ADSD and underwent speech and nasolaryngoscopy video recordings following a standard protocol. EXPOSURES Speech and nasolaryngoscopy video recordings following a standard protocol.
MAIN OUTCOMES AND MEASURES Specialists at 4 sites classified 178 patients into 11 categories. Four international experts independently classified 75 patients using the same categories without guidelines after viewing speech and nasolaryngoscopy video recordings. Each member from the 4 sites also classified 50 patients from other sites after viewing video clips of voice/laryngeal tasks. Interrater κ less than 0.40 indicated poor classification agreement among rater pairs and across recruiting sites. Consequently, a Delphi panel of 13 experts identified and ranked speech and laryngeal movement attributes for classifying ADSD, ABSD, VT, and MTD, which were reviewed by 46 community specialists. Based on the median attribute rankings, a final attribute list was created for each disorder. RESULTS When classifying patients without guidelines, raters differed in their classification distributions (likelihood ratio, χ2 = 107.66), had poor interrater agreement, and poor agreement with site categories. For 11 categories, the highest agreement was 34%, with no κ values greater than 0.26. In external rater pairs, the highest κ was 0.23 and the highest agreement was 38.5%. Using 6 categories, the highest percent agreement was 73.3%and the highest κ was 0.40. The Delphi method yielded 18 attributes for classifying disorders from speech and nasolaryngoscopic examinations.
CONCLUSIONS AND RELEVANCE Specialists without guidelines had poor agreement when classifying patients for research, leading to a Delphi-based development of the Spasmodic Dysphonia Attributes Inventory for classifying patients with ADSD, ABSD, VT, and MTD for research.
Social support is associated with improved self-management for people with chronic conditions, such as epilepsy; however, little is known about the perceived ease or difficulty of receiving and providing support for epilepsy self-management. We examined patterns of epilepsy self-management support from the perspectives of both people with epilepsy and their support persons. Fifty-three people with epilepsy and 48 support persons completed a survey on epilepsy self-management support. Of these individuals, 22 people with epilepsy and 16 support persons completed an in-depth interview. Rasch measurement models were used to evaluate the degree of difficulty of receiving or providing support often for nine self-management tasks. We analyzed model-data fit, person and item location along the support latent variable and differential person and item functioning. Qualitative methods were used to provide context and insight into the quantitative results. The results demonstrated good model-data fit. Help with seizures was the easiest type of support to receive or provide more often, followed by rides to a doctor's appointments and help avoiding seizure triggers. The most difficult types of support to receive or provide more often were reminders, particularly for taking and refilling medications. While most participants' responses fit the model, responses of several individuals misfit the model. Person misfit generally occurred because the scale items did not adequately capture some individuals' behaviors. These results could be useful in designing interventions that use support as a means of improving self-management. Additionally, the results provide information to improve or expand current measures of support for epilepsy self-management to better assess the experiences of people with epilepsy and their support persons.