A machine learning approach to support triaging of primary versus secondary headache patients using complete blood count.

Headaches account for up to 4.5% of emergency department visits, where they present a significant diagnostic challenge. While primary headaches are benign, secondary headaches can be life-threatening. It is essential to rapidly differentiate between primary and secondary headaches as the latter require immediate diagnostic work-up. Current assessment relies on subjective measures; time constraints can result in overuse of diagnostic neuroimaging, prolonging diagnosis, and adding to economic burden. There is therefore an unmet need for a time- and cost-efficient, quantitative triaging tool to guide further diagnostic testing. Routine blood tests may provide important diagnostic and prognostic biomarkers indicating underlying headache causes. In this retrospective study (approved by the UK Medicines and Healthcare products Regulatory Agency Independent Scientific Advisory Committee for Clinical Practice Research Datalink (CPRD) research [20_000173]), UK CPRD real-world data from patients (n = 121,241) presenting with headache from 1993-2021 were used to generate a predictive model based on a machine learning (ML) approach for primary versus secondary headaches. A ML-based predictive model was constructed using two different methods (logistic regression and random forest) and the following predictors were evaluated: ten standard measurements of complete blood count (CBC) test, 19 ratios of the ten CBC test parameters, and patient demographic and clinical characteristics. The model's predictive performance was assessed using a set of cross-validated model performance metrics. The final predictive model showed modest predictive accuracy using the random forest method (balanced accuracy: 0.7405). The sensitivity, specificity, false negative rate (incorrect prediction of secondary headache as primary headache), and false positive rate (incorrect prediction of primary headache as secondary headache) were 58%, 90%, 10%, and 42%, respectively. The ML-based prediction model developed could provide a useful time- and cost-effective quantitative clinical tool to facilitate the triaging of patients presenting to the clinic with headache.

Variability and directionality of inferior olive neuron dendrites revealed by detailed 3D characterization of an extensive morphological library.

The inferior olive (IO) is an evolutionarily conserved brain stem structure and its output activity plays a major role in the cerebellar computation necessary for controlling the temporal accuracy of motor behavior. The precise timing and synchronization of IO network activity has been attributed to the dendro-dendritic gap junctions mediating electrical coupling within the IO nucleus. Thus, the dendritic morphology and spatial arrangement of IO neurons governs how synchronized activity emerges in this nucleus. To date, IO neuron structural properties have been characterized in few studies and with small numbers of neurons; these investigations have described IO neurons as belonging to two morphologically distinct types, "curly" and "straight". In this work we collect a large number of individual IO neuron morphologies visualized using different labeling techniques and present a thorough examination of their morphological properties and spatial arrangement within the olivary neuropil. Our results show that the extensive heterogeneity in IO neuron dendritic morphologies occupies a continuous range between the classically described "curly" and "straight" types, and that this continuum is well represented by a relatively simple measure of "straightness". Furthermore, we find that IO neuron dendritic trees are often directionally oriented. Combined with an examination of cell body density distributions and dendritic orientation of adjacent IO neurons, our results suggest that the IO network may be organized into groups of densely coupled neurons interspersed with areas of weaker coupling.

Directional hearing in a silicon cricket.

Phonotaxis is the ability to orient towards or away from sound sources. Crickets can locate conspecifics by phonotaxis to the calling (mating) song they produce, and can evade bats by negative phonotaxis from echolocation calls. The behaviour and underlying physiology have been studied in some depth, and the auditory system solves this complex problem in a unique manner. Experiments conducted on a simulation model of the system indicated that the mechanism output a directional signal to sounds ahead at calling song frequency and to sounds behind at echolocation frequencies. We suggest that this combination of responses helps simplify later processing in the cricket. To further explore this result, an analogue, very large scale integrated (aVLSI) circuit model of the mechanism was designed and built; results from testing this agreed with the simulation. The aVLSI circuit was used to test a further hypothesis about the potential advantages of the positioning of the acoustic inputs for sound localisation during walking. There was no clear advantage to the directionality of the system in their location. The aVLSI circuitry is now being extended to use on a robot along with previously modelled neural circuitry to better understand the complete sensorimotor pathway.