The cerebral cortex contains an enormous number of neurons, allowing it to perform highly complex neural tasks. Understanding how these neurons develop at the correct time and place and in accurate numbers constitutes a major challenge. Here, we demonstrate a novel role for Gli3, a key regulator of cortical development, in cortical neurogenesis. We show that the onset of neuron formation is delayed in Gli3 conditional mouse mutants. Gene expression profiling and cell cycle measurements indicate that shortening of the G1 and S phases in radial glial cells precedes this delay. Reduced G1 length correlates with an upregulation of the cyclin-dependent kinase gene Cdk6, which is directly regulated by Gli3. Moreover, pharmacological interference with Cdk6 function rescues the delayed neurogenesis in Gli3 mutant embryos. Overall, our data indicate that Gli3 controls the onset of cortical neurogenesis by determining the levels of Cdk6 expression, thereby regulating neuronal output and cortical size.

BackgroundPopulation-based, prospective studies can provide important insights into Parkinson’s disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.MethodsWe searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.ResultsWe identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPV ranged from 56–90% in hospital datasets, 53–87% in prescription datasets, 81–90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPV ranged from 36–88% in hospital datasets, 40–74% in prescription datasets, and was 94% in mortality datasets. Sensitivity ranged from 15–73% in single datasets for PD and 43–63% in single datasets for parkinsonism.ConclusionsIn many settings, routinely collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. However, given the wide range of identified accuracy estimates, we recommend cohorts conduct their own context-specific validation studies if existing evidence is lacking. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.

BackgroundMotor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases.MethodsWe performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy—positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies.ResultsThirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55–92% and sensitivities from 75–93%. The single (UK-based) study of primary care data reported a PPV of 85%.ConclusionsDiagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation.

This study explored whether a human-like feel of touch biases perceived pleasantness and whether such a bias depends on top-down cognitive and/or bottom-up sensory processes. In two experiments, 11 materials were stroked across the forearm at different velocities (bottom-up) and participants rated tactile pleasantness and humanness. Additionally, in Experiment 1, participants identified the materials (top-down), whereas in Experiment 2, they rated each material with respect to its somatosensory properties (bottom-up). Stroking felt most pleasant at velocities optimal for the stimulation of CTafferents, a mechanosensory nerve hypothesized to underpin affective touch. A corresponding effect on perceived humanness was significant in Experiment 1 and marginal in Experiment 2. Whereas material identification was unrelated to both pleasantness and humanness, we observed a robust relation with the somatosensory properties. Materials perceived as smooth, slippery, and soft were also pleasant. A corresponding effect on perceived humanness was significant for the first somatosensory property only.Importantly, humanness positively predicted pleasantness and neither top-down nor bottom-up factors altered this relationship. Thus, perceiving gentle touch as human appears to promote pleasure possibly because this serves to reinforce interpersonal contact as a means for creating and maintaining social bonds.

BackgroundPopulation-based, prospective studies can provide important insights into Parkinson’s disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely-collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.MethodsWe searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely-collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.ResultsWe identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPVs ranged from 56-90% in hospital datasets, 53-87% in prescription datasets, 81-90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPVs ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivities ranged from 15-73% in single datasets for PD and 43-63% in single datasets for parkinsonism.ConclusionsIn many settings, routinely-collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.