Screening for functional neurological disorders by questionnaire

Objective: Diagnostic screening for functional neurological disorders (FNDs) continues to pose a challenge. Simple symptom counts fail clearly to discriminate patients with FND but there is increasing recognition of ‘positive’ features which are useful diagnostically during face-to-face assessments. A self-completed questionnaire evaluating specific features of FNDs would be useful for screening purposes in clinical and research settings. Methods: The Edinburgh Neurosymptoms Questionnaire (ENS) is a 30-item survey of presence and nature of: blackouts, weakness, hemisensory syndrome, memory problems, tremor, pain, fatigue, globus, multiple medical problems, and operations constructed via literature review and expert consensus. We conducted a pilot of the ENS on new general neurology clinic attendees at a large regional neuroscience centre. Patients were grouped according to consultant neurologist impression as having symptoms that were ‘Not at all’, 'somewhat’, ’Largely’ or ’Completely’ due to a functional disorder. Results: Blackouts, weakness and memory questions provided reasonable diagnostic utility (AUROC = 0.94, 0.71, 0.74 respectively) in single symptom analysis. All other symptoms lacked discriminating features. A multivariate linear model with all symptoms predicted functional classification with moderate diagnostic utility (AUROC = 0.83), specificity of 0.97, sensitivity of 0.47. Pain and blackout scores provided the most accurate predictor of functional classification. Conclusion: The ENS questionnaire provides some utility in differentiating patients presenting with functional blackouts but failed to provide diagnostic value in other types of FND, highlighting the limitations of this self-report tool

Continuous attractor network models of grid cell firing based on excitatory-inhibitory interactions

Neurons in the medial entorhinal cortex encode location through spatial firing fields that have a grid‐like organisation. The challenge of identifying mechanisms for grid firing has been addressed through experimental and theoretical investigations of medial entorhinal circuits. Here, we discuss evidence for continuous attractor network models that account for grid firing by synaptic interactions between excitatory and inhibitory cells. These models assume that grid‐like firing patterns are the result of computation of location from velocity inputs, with additional spatial input required to oppose drift in the attractor state. We focus on properties of continuous attractor networks that are revealed by explicitly considering excitatory and inhibitory neurons, their connectivity and their membrane potential dynamics. Models at this level of detail can account for theta‐nested gamma oscillations as well as grid firing, predict spatial firing of interneurons as well as excitatory cells, show how gamma oscillations can be modulated independently from spatial computations, reveal critical roles for neuronal noise, and demonstrate that only a subset of excitatory cells in a network need have grid‐like firing fields. Evaluating experimental data against predictions from detailed network models will be important for establishing the mechanisms mediating grid firing. [Image: see text

Computational Models of Grid Cell Firing

International audienceOverview Grid cells in the medial entorhinal cortex (mEC) fire whenever the animal enters a regular triangular array of locations that cover its environment. Since their discovery, several models that can account for these remarkably regular spatial firing patterns have been proposed. These generally fall into one of three classes, generating grid cell firing patterns either by oscillatory interference, through continuous attractor dynamics, or as a result of spatially modulated input from a place cell population. Neural network simulations have been used to explore the implications and predictions made by each class of model, while subsequent experimental data have allowed their architecture to be refined. Here, we describe implementations of two classes of grid cell model-oscillatory interference and continuous attractor dynamics-alongside a hybrid model that incorporates the principal features of each. These models are intended to be both parsimonious and make testable predictions. We discuss the strengths and weaknesses of each model and the predictions they make for future experimental manipulations of the grid cell network in vivo