Prognosis in functional and recognised pathophysiological neurological disorders - a shared basis.

OBJECTIVE: To compare self-reported outcomes, clinical trajectory and utility of baseline questionnaire responses in predicting prognosis in functional and recognised pathophysiological neurological disorders. METHODS: Baseline data on 2581 patients included health-related quality of life, psychological and physical symptoms, illness perceptions, consultation satisfaction and demographics. The prospective cohort included neurology outpatients classified with a functional (reporting symptoms 'not at all' or 'somewhat explained' by 'organic disease'; n = 716) or recognised pathophysiological disorder ('largely' or 'completely explained'; n = 1865). Logistic regression and deep neural network models were used to predict self-reported global clinical improvement (CGI) at 12-months. RESULTS: Patients with functional and recognised pathophysiological disorders reported near identical outcomes at 12-months with 67% and 66% respectively reporting unchanged or worse CGI. In multivariable modelling 'negative expectation of recovery' and 'disagreement with psychological attribution' predicted same or worse outcome in both groups. Receipt of disability-related state benefit predicted same or worse CGI outcome in the functional disorder group only (OR = 2.28 (95%-CI: 1.36-3.84) in a group-stratified model) and was not related to a measure of economic deprivation. Deep neural network models trained on all 92 baseline features predicted poor outcome with area under the receiver-operator curve of 0.67 in both groups. CONCLUSIONS: Those with functional and recognised pathophysiological neurological disorder share similar outcomes, clinical trajectories, and poor prognostic markers in multivariable models. Prediction of outcome at a patient level was not possible using the baseline data in this study.

International online survey of 1048 individuals with functional neurological disorder.

BACKGROUND AND PURPOSE: Functional neurological disorder (FND) is common, and symptoms can be severe. There have been no international large-scale studies of patient experiences of FND. METHODS: A patient questionnaire was created to assess FND patient characteristics, symptom comorbidities and illness perceptions. Respondents were recruited internationally through an open access questionnaire via social media and patient groups over a month-long period. RESULTS: In total, 1048 respondents from 16 countries participated. Mean age was 42 years (86% female). Median FND symptom duration was 5 years, and median time from first symptom to diagnosis was 2 years. Mean number of current symptoms (core FND and associated) was 9.9. Many respondents had associated symptoms, for example fatigue (93%), memory difficulties (80%) and headache (70%). Self-reported psychiatric comorbidities were relatively common (depression, 43%; anxiety, 51%; panic, 20%; and post-traumatic stress disorder, 22%). Most respondents reported that FND had multiple causes, including physical and psychological. CONCLUSIONS: This large survey adds further evidence that people with FND typically have high levels of multiple symptom comorbidity with resultant distress. It also supports the notion that associated physical symptoms are of particular clinical significance in FND patients. Dualistic ideas of FND were not supported by respondents, who generally preferred to conceptualize the disorder as one at the interface of mind and brain. The need for a broad approach to this poorly served patient group is highlighted. Potential selection and response biases due to distribution of the survey online, mostly via FND patient groups, are a key limitation.

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.