A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals.

An unusual ionic cocrystal of ponatinib hydrochloride: characterization by single-crystal X-ray diffraction and ultra-high field NMR spectroscopy

This study describes the discovery of a unique ionic cocrystal of the active pharmaceutical ingredient (API) ponatinib hydrochloride (pon·HCl), and characterization using single-crystal X-ray diffraction (SCXRD) and solid-state NMR (SSNMR) spectroscopy. Pon·HCl is a multicomponent crystal that features an unusual stoichiometry, with an asymmetric unit containing both monocations and dications of the ponatinib molecule, three water molecules, and three chloride ions. Structural features include (i) a charged imidazopyridazine moiety that forms a hydrogen bond between the ponatinib monocations and dications and (ii) a chloride ion that does not feature hydrogen bonds involving any organic moiety, instead being situated in a "square" arrangement with three water molecules. Multinuclear SSNMR, featuring high and ultra-high fields up to 35.2 T, provides the groundwork for structural interpretation of complex multicomponent crystals in the absence of diffraction data. A 13C CP/MAS spectrum confirms the presence of two crystallographically distinct ponatinib molecules, whereas 1D 1H and 2D 1H-1H DQ-SQ spectra identify and assign the unusually deshielded imidazopyridazine proton. 1D 35Cl spectra obtained at multiple fields confirm the presence of three distinct chloride ions, with density functional theory calculations providing key relationships between the SSNMR spectra and H⋯Cl- hydrogen bonding arrangements. A 2D 35Cl → 1H D-RINEPT spectrum confirms the spatial proximities between the chloride ions, water molecules, and amine moieties. This all suggests future application of multinuclear SSNMR at high and ultra-high fields to the study of complex API solid forms for which SCXRD data are unavailable, with potential application to heterogeneous mixtures or amorphous solid dispersions.

Constructing functional models from biophysically-detailed neurons

Improving biological plausibility and functional capacity are two important goals for brain models that connect low-level neural details to high-level behavioral phenomena. We develop a method called “oracle-supervised Neural Engineering Framework” (osNEF) to train biologically-detailed spiking neural networks that realize a variety of cognitively-relevant dynamical systems. Specifically, we train networks to perform computations that are commonly found in cognitive systems (communication, multiplication, harmonic oscillation, and gated working memory) using four distinct neuron models (leaky-integrate-and-fire neurons, Izhikevich neurons, 4-dimensional nonlinear point neurons, and 4-compartment, 6-ion-channel layer-V pyramidal cell reconstructions) connected with various synaptic models (current-based synapses, conductance-based synapses, and voltage-gated synapses). We show that osNEF networks exhibit the target dynamics by accounting for nonlinearities present within the neuron models: performance is comparable across all four systems and all four neuron models, with variance proportional to task and neuron model complexity. We also apply osNEF to build a model of working memory that performs a delayed response task using a combination of pyramidal cells and inhibitory interneurons connected with NMDA and GABA synapses. The baseline performance and forgetting rate of the model are consistent with animal data from delayed match-to-sample tasks (DMTST): we observe a baseline performance of 95% and exponential forgetting with time constant τ = 8.5s, while a recent meta-analysis of DMTST performance across species observed baseline performances of 58 − 99% and exponential forgetting with time constants of τ = 2.4 − 71s. These results demonstrate that osNEF can train functional brain models using biologically-detailed components and open new avenues for investigating the relationship between biophysical mechanisms and functional capabilities.

Computational models of biologically realistic neural networks help scientists understand and recreate a wide variety of brain processes, responsible for everything from fish locomotion to human cognition. To be useful, these models must both recreate features of the brain, such as the electrical, chemical, and geometric properties of neurons, and perform useful functional operations, such as storing and retrieving information from a short term memory. Here, we develop a new method for training networks built from biologically detailed components. We simulate networks that contain a variety of complex neurons and synapses, then show that our method successfully trains them to perform a variety of cognitive operations. Most notably, we train a working memory model that contains detailed reconstructions of cortical neurons, and demonstrate that it performs a memory task with performance that is comparable to simple animals. Researchers can use our method to train detailed brain models and investigate how biological features (or deficits thereof) relate to cognition, which may provide insights into the biological basis of mental disorders such as Parkinson’s disease.

Determining the optimal dose of reactive balance training after stroke: study protocol for a pilot randomised controlled trial

INTRODUCTION

Falls risk poststroke is highest soon after discharge from rehabilitation. Reactive balance training (RBT) aims to improve control of reactions to prevent falling after a loss of balance. In healthy older adults, a single RBT session can lead to lasting improvements in reactive balance control and prevent falls in daily life. While increasing the dose of RBT does not appear to lead to additional benefit for healthy older adults, stroke survivors, who have more severely impaired balance control, may benefit from a higher RBT dose. Our long-term goal is to determine the optimal dose of RBT in people with subacute stroke. This assessor-blinded pilot randomised controlled trial aims to inform the design of a larger trial to address this long-term goal.

METHODS AND ANALYSIS

Participants (n=36) will be attending out-patient stroke rehabilitation, and will be randomly allocated to one of three groups: one, three or six RBT sessions. RBT will replace a portion of participants' regular physiotherapy so that the total physical rehabilitation time will be the same for the three groups. Balance and balance confidence will be assessed at: (1) study enrolment; (2) out-patient rehabilitation discharge; and (3) 6 months postdischarge. Participants will report falls and physical activity for 6 months postdischarge. Pilot data will be used to plan the larger trial (ie, sample size estimate using fall rates, and which groups should be included based on between-group trends in pre-to-post training effect sizes for reactive balance control measures). Pilot data will also be used to assess the feasibility of the larger trial (ie, based on the accrual rate, outcome completion rate and feasibility of prescribing specific training doses).

ETHICS AND DISSEMINATION

Institutional research ethics approval has been received. Study participants will receive a lay summary of results. We will also publish our findings in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT04219696; Pre results.

Submaximal exercise cardiac output is increased by 4 weeks of sprint interval training in young healthy males with low initial Q̇-V̇O2: Importance of cardiac response phenotype

Cardiovascular adaptations to exercise, particularly at the individual level, remain poorly understood. Previous group level research suggests the relationship between cardiac output and oxygen consumption (Q˙-V˙O2) is unaffected by training as submaximal Q˙ is unchanged. We recently identified substantial inter-individual variation in the exercise Q˙-V˙O2 relationship that was correlated to stroke volume (SV) as opposed to arterial oxygen content. Therefore we explored the effects of sprint interval training (SIT) on modulating Q˙-V˙O2 given an individual’s specific Q˙-V˙O2 relationship. 22 (21±2 yrs) healthy, recreationally active males participated in a 4-week SIT (8, 20 second sprints; 4x/week, 170% of the work rate at V˙O2 peak) study with progressive exercise tests (PET) until exhaustion. Cardiac output (Q˙ L/min; inert gas rebreathe, Finometer Modelflow™), oxygen consumption (V˙O2 L/min; breath-by-breath pulmonary gas exchange), quadriceps oxygenation (near infrared spectroscopy) and exercise tolerance (6–20; Borg Scale RPE) were measured throughout PET both before and after training. Data are mean Δ from bsl±SD. Higher Q˙ (HQ˙) and lower Q˙ (LQ˙) responders were identified post hoc (n = 8/group). SIT increased the Q˙-V˙O2 post-training in LQ˙ (3.8±0.2 vs. 4.7±0.2; P = 0.02) while HQ˙ was unaffected (5.8±0.1 vs. 5.3±0.6; P = 0.5). ΔQ˙ was elevated beyond 80 watts in LQ˙ due to a greater increase in SV (all P<0.04). Peak V˙O2 (ml/kg/min) was increased in LQ˙ (39.7±6.7 vs. 44.5±7.3; P = 0.015) and HQ˙ (47.2±4.4 vs. 52.4±6.0; P = 0.009) following SIT, with HQ˙ having a greater peak V˙O2 both pre (P = 0.02) and post (P = 0.03) training. Quadriceps muscle oxygenation and RPE were not different between groups (all P>0.1). In contrast to HQ˙, LQ˙ responders are capable of improving submaximal Q˙-V˙O2 in response to SIT via increased SV. However, the increased submaximal exercise Q˙ does not benefit exercising muscle oxygenation.

Investigating the feasibility and acceptability of real-time visual feedback in reducing compensatory motions during self-administered stroke rehabilitation exercises: A pilot study with chronic stroke survivors

INTRODUCTION

Homework-based rehabilitation programs can help stroke survivors restore upper extremity function. However, compensatory motions can develop without therapist supervision, leading to sub-optimal recovery. We developed a visual feedback system using a live video feed or an avatar reflecting users' movements so users are aware of compensations. This pilot study aimed to evaluate validity (how well the avatar characterizes different types of compensations) and acceptability of the system.

METHODS

Ten participants with chronic stroke performed upper-extremity exercises under three feedback conditions: none, video, and avatar. Validity was evaluated by comparing agreement on compensations annotated using video and avatar images. A usability survey was administered to participants after the experiment to obtain information on acceptability.

RESULTS

There was substantial agreement between video and avatar images for shoulder elevation and hip extension (Cohen's κ: 0.6-0.8) and almost perfect agreement for trunk rotation and flexion (κ: 0.80-1). Acceptability was low due to lack of corrective prompts and occasional noise with the avatar display. Most participants suggested that an automatic compensation detection feature with visual and auditory cuing would improve the system.

CONCLUSION

The avatar characterized four types of compensations well. Future work will involve increasing sensitivity for shoulder elevation and implementing a method to detect compensations.

Does perturbation-based balance training prevent falls among individuals with chronic stroke? A randomised controlled trial

OBJECTIVES

No intervention has been shown to prevent falls poststroke. We aimed to determine if perturbation-based balance training (PBT) can reduce falls in daily life among individuals with chronic stroke.

DESIGN

Assessor-blinded randomised controlled trial.

SETTING

Two academic hospitals in an urban area.

INTERVENTIONS

Participants were allocated using stratified blocked randomisation to either 'traditional' balance training (control) or PBT. PBT focused on improving responses to instability, whereas traditional balance training focused on maintaining stability during functional tasks. Training sessions were 1 hour twice/week for 6 weeks. Participants were also invited to complete 2 'booster' training sessions during the follow-up.

PARTICIPANTS

Eighty-eight participants with chronic stroke (>6 months poststroke) were recruited and randomly allocated one of the two interventions. Five participants withdrew; 42 (control) and 41 (PBT group) were included in the analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was rate of falls in the 12 months post-training. Negative binomial regression was used to compare fall rates between groups. Secondary outcomes were measures of balance, mobility, balance confidence, physical activity and social integration.

RESULTS

PBT participants reported 53 falls (1.45 falls/person-year) and control participants reported 64 falls (1.72 falls/person-year; rate ratio: 0.85(0.42 to 1.69); p=0.63). Per-protocol analysis included 32 PBT and 34 control participants who completed at least 10/12 initial training sessions and 1 booster session. Within this subset, PBT participants reported 32 falls (1.07 falls/person-year) and control participants reported 57 falls (1.75 falls/person-year; rate ratio: 0.62(0.29 to 1.30); p=0.20). PBT participants had greater improvement in reactive balance control than the control group, and these improvements were sustained 12 months post-training. There were no intervention-related serious adverse effects.

CONCLUSIONS

The results are inconclusive. PBT may help to prevent falls in daily life poststroke, but ongoing training may be required to maintain the benefits.

TRIAL REGISTRATION NUMBER

ISRCTN05434601; Results.