Arakaki, T. | bioRxiv Science

bioRxiv2019

A High Dose Tango Intervention for People with Parkinson’s disease (PwPD)

BackgroundDance has been used extensively to help supplement ongoing therapies for people with PD, most commonly on a weekly or biweekly basis. A daily dose, however, may provide additional benefits. This study examines the dose effect of a dance intervention delivered within a clinic for movement disorders in which PwPD are paired with experienced studio tango dancers.\n\nObjectiveThe current study aims to examine the dose effects of daily dance for PwPD on motor and non-motor functions directly within a movement disorders clinic.\n\nDesignwithin-subject, pre-post-intervention, mixed-methods evaluation including UPDRS III.\n\nSettingThe intervention was held at the Movement Disorders Department of a General Hospital in Buenos Aires, Argentina over two-weeks.\n\nSubjectsThe class had 21 people in total attendance per class. Two were expert tango dancers and instructors, nine were advanced tango dancers (volunteers), two were caregivers and eight were people with mild and moderate severity [Hoehn and Yahr (H&Y) scale 1-2] idiopathic Parkinsons disease.\n\nInterventionTen dance lessons, each 90-minutes daily within a two-week period.\n\nOutcome measuresThe Movement Disorders Society Unified Parkinsons Disease Rating Scale (UPDRS) part III was used for pre- and post-motor assessment. Psychological questionnaires, a Likert scale examining symptoms, and a pictorial scale were used to assess non-motor aspects. Semi-structured interviews were conducted to assess the impact of the dance intervention on participants experience.\n\nResultsOur study found a significant 18% amelioration in motor symptoms as measured by UPDRS III. We also found improvements on activities of daily living (ADL), sleep, Psychological Needs variables - post high dose dance intervention in Likert Scale.\n\nConclusionsA high dose short-term tango intervention for PwPD improved motor and non-motor aspects of PD such as ADL and sleep with high levels of adherence (97.5%) and enjoyment reported by participants. This dance intervention also improved participants perception of their own skills. The frequency or dosage of dance in rehabilitation suggests that an increased dose from once per week to 5 times per week can ameliorate many symptoms of PD and could be used as a short-term intervention.

neuroscience↗

bioRxiv2017

Capturing the diversity of biological tuning curves using generative adversarial networks

Tuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable to compute. Recent advances in machine learning provide ways for fitting generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GAN) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic models in theoretical neuroscience to datasets of measured tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, e.g., the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or of single-cell properties. Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked a priori by the user. More generally, this framework opens the door to fitting theoretically motivated dynamical network models directly to simultaneously or non-simultaneously recorded neural responses.

neuroscience↗