A computational model of altered gait patterns in parkinson's disease patients negotiating narrow doorways

A novel treatment strategy to prevent Parkinson's disease: focus on iron regulatory protein 1 (IRP1)

We propose that neural damage in Parkinson's disease (PD) is due to dysregulation of iron utilization rather than to high iron levels per se. Iron deposits are associated with neuronal cell death in substantia nigra (SN) resulting in PD where high levels of iron in SNs are due to dysregulation of iron utilization. Cytosolic aconitase (ACO1) upon losing an iron-sulfur cluster becomes iron regulatory protein 1 (IRP1). Rotenone increases levels of IRP1 and induces PD in rats. An increase in iron leads to inactivation of IRP1. We propose a novel treatment strategy to prevent PD. Specifically in rats given rotenone by subcutaneous injections, iron, from iron carbonyl from which iron is slowly absorbed, given three times a day by gavage will keep iron levels constant in the gut whereby iron levels and iron utilization systematically can be tightly regulated. Rotenone adversely affects complex 1 iron-sulfur proteins. Iron supplementation will increase iron-sulfur cluster formation switching IRP1 to ACO1. With IRP1 levels kept constantly low, iron utilization will systematically be tightly regulated stopping dysregulation of complex 1 and the neural damage done by rotenone preventing PD.

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.

Ankle Push-Off Based Mathematical Model for Freezing of Gait in Parkinson's Disease

Freezing is an involuntary stopping of gait observed in late-stage Parkinson's disease (PD) patients. This is a highly debilitating symptom lacking a clear understanding of its causes. Walking in these patients is also associated with high variability, making both prediction of freezing and its understanding difficult. A neuromechanical model describes the motion of the mechanical (motor) aspects of the body under the action of neuromuscular forcing. In this work, a simplified neuromechanical model of gait is used to infer the causes for both the observed variability and freezing in PD. The mathematical model consists of the stance leg (during walking) modeled as a simple inverted pendulum acted upon by the ankle-push off forces from the trailing leg and pathological forces by the plantar-flexors of the stance leg. We model the effect on walking of the swing leg in the biped model and provide a rationale for using an inverted pendulum model. Freezing and irregular walking is demonstrated in the biped model as well as the inverted pendulum model. The inverted pendulum model is further studied semi-analytically to show the presence of horseshoe and chaos. While the plantar flexors of the swing leg push the center of mass (CoM) forward, the plantar flexors of the stance leg generate an opposing torque. Our study reveals that these opposing forces generated by the plantar flexors can induce freezing. Other gait abnormalities nearer to freezing such as a reduction in step length, and irregular walking patterns can also be explained by the model.

Mathematics anxiety and cognition: an integrated neural network model

Many students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.

The beta oscillation conditions in a simplified basal ganglia network

Parkinson's disease is a type of motor dysfunction disease that is induced mainly by abnormal interactions between the subthalamic nucleus (STN) and globus pallidus (GP) neurons. Periodic oscillatory activities with frequencies of 13-30 Hz are the main physiological characteristics of Parkinson's disease. In this paper, we built a class of STN-GP networks to explore beta oscillation conditions. A theoretical formula was obtained for generating oscillations without internal GP connections. Based on this formula, we studied the effects of cortex inputs, striatum inputs, coupling weights and delays on oscillation conditions, and the theoretical results are in good agreement with the numerical results. The onset mechanism can be explained by the model, and the internal GP connection has little effect on oscillations. Finally, we compared oscillation conditions with those in previous studies and found that the delays and coupling weights required for generating oscillations may decrease as the number of nuclei increases. We hope that the results obtained will inspire future theoretical and experimental studies.

A Neurocomputational Model of the Effect of Cognitive Load on Freezing of Gait in Parkinson's Disease

Experimental data show that perceptual cues can either exacerbate or ameliorate freezing of gait (FOG) in Parkinson's Disease (PD). For example, simple visual stimuli like stripes on the floor can alleviate freezing whereas complex stimuli like narrow doorways can trigger it. We present a computational model of the cognitive and motor cortico-basal ganglia loops that explains the effects of sensory and cognitive processes on FOG. The model simulates strong causative factors of FOG including decision conflict (a disagreement of various sensory stimuli in their association with a response) and cognitive load (complexity of coupling a stimulus with downstream mechanisms that control gait execution). Specifically, the model simulates gait of PD patients (freezers and non-freezers) as they navigate a series of doorways while simultaneously responding to several Stroop word cues in a virtual reality setup. The model is based on an actor-critic architecture of Reinforcement Learning involving Utility-based decision making, where Utility is a weighted sum of Value and Risk functions. The model accounts for the following experimental data: (a) the increased foot-step latency seen in relation to high conflict cues, (b) the high number of motor arrests seen in PD freezers when faced with a complex cue compared to the simple cue, and (c) the effect of dopamine medication on these motor arrests. The freezing behavior arises as a result of addition of task parameters (doorways and cues) and not due to inherent differences in the subject group. The model predicts a differential role of risk sensitivity in PD freezers and non-freezers in the cognitive and motor loops. Additionally this first-of-its-kind model provides a plausible framework for understanding the influence of cognition on automatic motor actions in controls and Parkinson's Disease.

Probing the Role of Medication, DBS Electrode Position, and Antidromic Activation on Impulsivity Using a Computational Model of Basal Ganglia

Everyday, we encounter situations where available choices are nearly equally rewarding (high conflict) calling for some tough decision making. Experimental recordings showed that the activity of Sub Thalamic Nucleus (STN) increases during such situations providing the extra time needed to make the right decision, teasing apart the most rewarding choice from the runner up closely trailing behind. This prolonged deliberation necessary for decision making under high conflict was absent in Parkinson's disease (PD) patients who underwent Deep Brain Stimulation (DBS) surgery of STN. In an attempt to understand the underlying cause of such adverse response, we built a 2D spiking network model (50 × 50 lattice) of Basal ganglia incorporating the key nuclei. Using the model we studied the Probabilistic learning task (PLT) in untreated, treated (L-Dopa and Dopamine Agonist) and STN-DBS PD conditions. Based on the experimental observation that dopaminergic activity is analogous to temporal difference (TD) and induces cortico-striatal plasticity, we introduced learning in the cortico-striatal weights. The results show that healthy and untreated conditions of PD model were able to more or less equally select (avoid) the rewarding (punitive) choice, a behavior that was absent in treated PD condition. The time taken to select a choice in high conflict trials was high in normal condition, which is in agreement with experimental results. The treated PD (Dopamine Agonist) patients made impulsive decisions (small reaction time) which in turn led to poor performance. The underlying cause of the observed impulsivity in DBS patients was studied in the model by (1) varying the electrode position within STN, (2) causing antidromic activation of GPe neurons. The effect of electrode position on reaction time was analyzed by studying the activity of STN neurons where, a decrease in STN neural activity was observed for certain electrode positions. We also observed that a higher antidromic activation of GPe neurons does not impact the learning ability but decreases reaction time as reported in DBS patients. These results suggest a probable role of electrode and antidromic activation in modulating the STN activity and eventually affecting the patient's performance on PLT.

On the Complexity of Brain Disorders: A Symptom-Based Approach

Mounting evidence shows that brain disorders involve multiple and different neural dysfunctions, including regional brain damage, change to cell structure, chemical imbalance, and/or connectivity loss among different brain regions. Understanding the complexity of brain disorders can help us map these neural dysfunctions to different symptom clusters as well as understand subcategories of different brain disorders. Here, we discuss data on the mapping of symptom clusters to different neural dysfunctions using examples from brain disorders such as major depressive disorder (MDD), Parkinson’s disease (PD), schizophrenia, posttraumatic stress disorder (PTSD) and Alzheimer’s disease (AD). In addition, we discuss data on the similarities of symptoms in different disorders. Importantly, computational modeling work may be able to shed light on plausible links between various symptoms and neural damage in brain disorders.

Identifying the Basal Ganglia network model markers for medication-induced impulsivity in Parkinson's disease patients

Impulsivity, i.e. irresistibility in the execution of actions, may be prominent in Parkinson's disease (PD) patients who are treated with dopamine precursors or dopamine receptor agonists. In this study, we combine clinical investigations with computational modeling to explore whether impulsivity in PD patients on medication may arise as a result of abnormalities in risk, reward and punishment learning. In order to empirically assess learning outcomes involving risk, reward and punishment, four subject groups were examined: healthy controls, ON medication PD patients with impulse control disorder (PD-ON ICD) or without ICD (PD-ON non-ICD), and OFF medication PD patients (PD-OFF). A neural network model of the Basal Ganglia (BG) that has the capacity to predict the dysfunction of both the dopaminergic (DA) and the serotonergic (5HT) neuromodulator systems was developed and used to facilitate the interpretation of experimental results. In the model, the BG action selection dynamics were mimicked using a utility function based decision making framework, with DA controlling reward prediction and 5HT controlling punishment and risk predictions. The striatal model included three pools of Medium Spiny Neurons (MSNs), with D1 receptor (R) alone, D2R alone and co-expressing D1R-D2R. Empirical studies showed that reward optimality was increased in PD-ON ICD patients while punishment optimality was increased in PD-OFF patients. Empirical studies also revealed that PD-ON ICD subjects had lower reaction times (RT) compared to that of the PD-ON non-ICD patients. Computational modeling suggested that PD-OFF patients have higher punishment sensitivity, while healthy controls showed comparatively higher risk sensitivity. A significant decrease in sensitivity to punishment and risk was crucial for explaining behavioral changes observed in PD-ON ICD patients. Our results highlight the power of computational modelling for identifying neuronal circuitry implicated in learning, and its impairment in PD. The results presented here not only show that computational modelling can be used as a valuable tool for understanding and interpreting clinical data, but they also show that computational modeling has the potential to become an invaluable tool to predict the onset of behavioral changes during disease progression.

A spiking Basal Ganglia model of synchrony, exploration and decision making

To make an optimal decision we need to weigh all the available options, compare them with the current goal, and choose the most rewarding one. Depending on the situation an optimal decision could be to either “explore” or “exploit” or “not to take any action” for which the Basal Ganglia (BG) is considered to be a key neural substrate. In an attempt to expand this classical picture of BG function, we had earlier hypothesized that the Indirect Pathway (IP) of the BG could be the subcortical substrate for exploration. In this study we build a spiking network model to relate exploration to synchrony levels in the BG (which are a neural marker for tremor in Parkinson's disease). Key BG nuclei such as the Sub Thalamic Nucleus (STN), Globus Pallidus externus (GPe) and Globus Pallidus internus (GPi) were modeled as Izhikevich spiking neurons whereas the Striatal output was modeled as Poisson spikes. The model is cast in reinforcement learning framework with the dopamine signal representing reward prediction error. We apply the model to two decision making tasks: a binary action selection task (similar to one used by Humphries et al., 2006) and an n-armed bandit task (Bourdaud et al., 2008). The model shows that exploration levels could be controlled by STN's lateral connection strength which also influenced the synchrony levels in the STN-GPe circuit. An increase in STN's lateral strength led to a decrease in exploration which can be thought as the possible explanation for reduced exploratory levels in Parkinson's patients. Our simulations also show that on complete removal of IP, the model exhibits only Go and No-Go behaviors, thereby demonstrating the crucial role of IP in exploration. Our model provides a unified account for synchronization, action section, and explorative behavior.

Early Freezing of Gait: Atypical versus Typical Parkinson Disorders

In 18 months, 850 patients were referred to Muhammad Ali Parkinson Center (MAPC). Among them, 810 patients had typical Parkinson disease (PD) and 212 had PD for ≤5 years. Among the 212 patients with early PD, 27 (12.7%) had freezing of gait (FOG). Forty of the 850 had atypical parkinsonism. Among these 40 patients, all of whom had symptoms for ≤5 years, 12 (30.0%) had FOG. FOG improved with levodopa in 21/27 patients with typical PD but did not improve in the 12 patients with atypical parkinsonism. FOG was associated with falls in both groups of patients. We believe that FOG unresponsive to levodopa in typical PD resembles FOG in atypical parkinsonism. We thus compared the 6 typical PD patients with FOG unresponsive to levodopa plus the 12 patients with atypical parkinsonism with the 21 patients with typical PD responsive to levodopa. We compared them by tests of locomotion and postural stability. Among the patients with FOG unresponsive to levodopa, postural stability was more impaired than locomotion. This finding leads us to believe that, in these patients, postural stability, not locomotion, is the principal problem underlying FOG.