Multiple tests based on a gaussian approximation of the unitary events method with delayed coincidence count.

The unitary events (UE) method is one of the most popular and efficient methods used over the past decade to detect patterns of coincident joint spike activity among simultaneously recorded neurons. The detection of coincidences is usually based on binned coincidence count (Grün, 1996 ), which is known to be subject to loss in synchrony detection (Grün, Diesmann, Grammont, Riehle, & Aertsen, 1999 ). This defect has been corrected by the multiple shift coincidence count (Grün et al., 1999 ). The statistical properties of this count have not been further investigated until this work, the formula being more difficult to deal with than the original binned count. First, we propose a new notion of coincidence count, the delayed coincidence count, which is equal to the multiple shift coincidence count when discretized point processes are involved as models for the spike trains. Moreover, it generalizes this notion to nondiscretized point processes, allowing us to propose a new gaussian approximation of the count. Since unknown parameters are involved in the approximation, we perform a plug-in step, where unknown parameters are replaced by estimated ones, leading to a modification of the approximating distribution. Finally the method takes the multiplicity of the tests into account via a Benjamini and Hochberg approach (Benjamini & Hochberg, 1995 ), to guarantee a prescribed control of the false discovery rate. We compare our new method, MTGAUE (multiple tests based on a gaussian approximation of the unitary events) and the UE method proposed in Grün et al. ( 1999 ) over various simulations, showing that MTGAUE extends the validity of the previous method. In particular, MTGAUE is able to detect both profusion and lack of coincidences with respect to the independence case and is robust to changes in the underlying model. Furthermore MTGAUE is applied on real data.

Mild cognitive impairment and anti-Alzheimer disease medications: A cross sectional study of the French National Alzheimer Databank (BNA).

Unlike Alzheimer's disease (AD), there are no drugs approved for the treatment of mild cognitive impairment (MCI). The objective of this study was to evaluate real world prescriptions of anti-AD medications in patients with MCI in France and to determine characteristics associated with treatment. A cross sectional study of the French National Alzheimer Databank (BNA) was conducted. Patients diagnosed with MCI by physicians of the BNA network in 2010 and 2011 were included in this study. We included 16,236 patients with a diagnosis of MCI in the study. Mean age was 76.4 years old and females were 59.5%. Nine hundred eighty five patients (6.1%) were taking an anti-AD medication. Results of a multivariate analysis show that use was associated with older age, lower MMSE score, amnestic MCI subtype, living at home, and higher education. Treatment with antidepressant drugs was also associated with anti-AD medication use (odd ratio: 1.68; 95% confidence interval: 1.44 to 1.96). "Off label" prescription of anti-AD drugs is low in France and seems to be limited to a population at risk of conversion to AD. Similar analysis will be required to monitor this practice in the future.