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The cholinergic system has consistently been implicated in Pavlovian fear conditioning. Considerable work has been done to localize specific nicotinic receptor subtypes in the hippocampus and determine their functional importance; however, the specific function of many of these subtypes has yet to be determined. An alpha7 nicotinic antagonist methyllycaconitine (MLA) (35 microg), and a broad spectrum non-alpha7 nicotinic antagonist mecamylamine (35 microg) was injected directly into the dorsal hippocampus or overlying cortex either 15 min pre-, 1 min post-, or 6h post-fear conditioning. One week after conditioning, retention of contextual and cue (tone) conditioning were assessed. A significant impairment in retention of contextual fear was observed when mecamylamine was injected 15 min pre- and 1 min post-conditioning. No significant impairment was observed when mecamylamine was injected 6h post-conditioning. Likewise, a significant impairment in retention of contextual fear was observed when MLA was injected 1 min post-conditioning; however, in contrast, MLA did not show any significant impairments when injected 15 min pre-conditioning, but did show a significant impairment when injected 6h post-conditioning. There were no significant impairments observed when either drug was injected into overlying cortex. No significant impairments were observed in cue conditioning for either drug. In general, specific temporal dynamics involved in nicotinic receptor function were found relative to time of receptor dysfunction. The results indicate that the greatest deficits in long-term retention (1 week) of contextual fear are produced by central infusion of MLA minutes to hours post-conditioning or mecamylamine within minutes of conditioning.
Neuroimage phenotyping for psychiatric and neurological disorders is performed using voxelwise analyses also known as voxel based analyses or morphometry (VBM). A typical voxelwise analysis treats measurements at each voxel (e.g., fractional anisotropy, gray matter probability) as outcome measures to study the effects of possible explanatory variables (e.g., age, group) in a linear regression setting. Furthermore, each voxel is treated independently until the stage of correction for multiple comparisons. Recently, multi-voxel pattern analyses (MVPA), such as classification, have arisen as an alternative to VBM. The main advantage of MVPA over VBM is that the former employ multivariate methods which can account for interactions among voxels in identifying significant patterns. They also provide ways for computer-aided diagnosis and prognosis at individual subject level. However, compared to VBM, the results of MVPA are often more difficult to interpret and prone to arbitrary conclusions. In this paper, first we use penalized likelihood modeling to provide a unified framework for understanding both VBM and MVPA. We then utilize statistical learning theory to provide practical methods for interpreting the results of MVPA beyond commonly used performance metrics, such as leave-one-out-cross validation accuracy and area under the receiver operating characteristic (ROC) curve. Additionally, we demonstrate that there are challenges in MVPA when trying to obtain image phenotyping information in the form of statistical parametric maps (SPMs), which are commonly obtained from VBM, and provide a bootstrap strategy as a potential solution for generating SPMs using MVPA. This technique also allows us to maximize the use of available training data. We illustrate the empirical performance of the proposed framework using two different neuroimaging studies that pose different levels of challenge for classification using MVPA.