Caffeine is a commonly used neurostimulant that also produces cerebral vasoconstriction by antagonizing adenosine receptors. Chronic caffeine use results in an adaptation of the vascular adenosine receptor system presumably to compensate for the vasoconstrictive effects of caffeine. We investigated the effects of caffeine on cerebral blood flow (CBF) in increasing levels of chronic caffeine use. Low (mean = 45 mg/day), moderate (mean = 405 mg/day), and high (mean = 950 mg/day) caffeine users underwent quantitative perfusion magnetic resonance imaging on four separate occasions: twice in a caffeine abstinent state (abstained state) and twice in a caffeinated state following their normal caffeine use (native state). In each state there were two drug conditions: participants received either caffeine (250 mg) or placebo. Gray matter CBF was tested with repeated-measures analysis of variance using caffeine use as a between-subjects factor, and correlational analyses were conducted between CBF and caffeine use. Caffeine reduced CBF by an average of 27% across both caffeine states. In the abstained placebo condition, moderate and high users had similarly greater CBF than low users; but in the native placebo condition, the high users had a trend towards less CBF than the low and moderate users. Our results suggest a limited ability of the cerebrovascular adenosine system to compensate for high amounts of daily caffeine use.

Brain health, or cognitive health, refers to skills such as remembering, learning new things, planning, concentrating, or making decisions. When cognitive health is impaired (referred to as cognitive impairment), a person has trouble with these skills that affect the things he or she can do in everyday life. People of all ages can experience cognitive impairment, which can range from mild to severe. A person with mild cognitive impairment may be aware of increased difficulty remembering, but it may not be obvious to others. Most likely, these individuals can still do their everyday activities. Individuals with more severe cognitive impairment usually have difficulty expressing themselves and understanding others. They may be unable to complete tasks such as preparing meals or managing finances. They may require help to manage their medicines and medical conditions. Ultimately, individuals may become unable to care for themselves, lose their independence, and require institutional care.

We examined the contribution of the nigrostriatal DA system to instrumental learning and behavior using optogenetics in awake, behaving mice. Using Cre-inducible channelrhodopsin-2 (ChR2) in mice expressing Cre recombinase driven by the tyrosine hydroxylase promoter (Th-Cre), we tested whether selective stimulation of DA neurons in the substantia nigra pars compacta (SNC), in the absence of any natural rewards, was sufficient to promote instrumental learning in naive mice. Mice expressing ChR2 in SNC DA neurons readily learned to press a lever to receive laser stimulation, but unlike natural food rewards the lever pressing did not decline with satiation. When the number of presses required to receive a stimulation was altered, mice adjusted their rate of pressing accordingly, suggesting that the rate of stimulation was a controlled variable. Moreover, extinction, i.e. the cessation of action-contingent stimulation, and the complete reversal of the relationship between action and outcome by the imposition of an omission contingency, rapidly abolished lever pressing. Together these results suggest that selective activation of SNC DA neurons can be sufficient for acquisition and maintenance of a new instrumental action.

In 2007, the Food and Drug Administration requested that manufacturers of all approved gadolinium-based contrast agents (GBCAs), drugs widely used in magnetic resonance imaging, use nearly identical text in their product labeling to describe the risk of nephrogenic systemic fibrosis (NSF). Accumulating information about NSF risks led to revision of the labeling text for all of these drugs in 2010. The present report summarizes the basis and purpose of this class-labeling approach and describes some of the related challenges, given the evolutionary nature of the NSF risk evidence. The class-labeling approach for presentation of product risk is designed to decrease the occurrence of NSF and to enhance the safe use of GBCAs in radiologic practice.

Background: Electronic cigarettes (e-cigarettes) represent an emerging public health issue. These devices deliver nicotine along with other constituents, including flavorants, via an inhalable aerosol. Their uptake is rapidly increasing in both adults and youths, primarily among current smokers. Public debate is increasing on how these devices should be regulated and used, yet only limited peer-reviewed research exists. To develop a informed policy for e-cigarettes, their effects on human behavior, physiology, and health need to be understood.

Purpose To evaluate the effectiveness of a fully automated postprocessing filter algorithm in pulsed arterial spin labeling (PASL) MRI perfusion images in a large clinical population. Materials and Methods A mean and standard deviation-based filter was implemented to remove outliers in the set of perfusion-weighted images (control – label) before being averaged and scaled to quantitative cerebral blood flow (CBF) maps. Filtered and unfiltered CBF maps from 200 randomly selected clinical cases were assessed by four blinded raters to evaluate the effectiveness of the filter. Results The filter salvaged many studies deemed uninterpretable as a result of motion artifacts, transient gradient, and/or radiofrequency instabilities, and unexpected disruption of data acquisition by the technologist to communicate with the patient. The filtered CBF maps contained significantly (P < 0.05) fewer artifacts and were more interpretable than unfiltered CBF maps as determined by one-tail paired t-test. Conclusion Variations in MR perfusion signal related to patient motion, system instability, or disruption of the steady state can introduce artifacts in the CBF maps that can be significantly reduced by postprocessing filtering. Diagnostic quality of the clinical perfusion images can be improved by performing selective averaging without a significant loss in perfusion signal-to-noise ratio.

In fMRI data analysis it has been shown that for a wide range of situations the hemodynamic response function (HRF) can be reasonably characterized as the impulse response function of a linear and time invariant system. An accurate and robust extraction of the HRF is essential to infer quantitative information about the relative timing of the neuronal events in different brain regions. When no assumptions are made about the HRF shape, it is most commonly estimated using time windowed averaging or a least squares estimated general linear model based on either Fourier or delta basis functions. Recently, regularization methods have been employed to increase the estimation efficiency of the HRF; typically these methods produce more accurate HRF estimates than the least squares approach [Goutte, C., Nielsen, F.A., Hansen, L.K., 2000. Modeling the Haemodynamic Response in fMRI Using Smooth FIR Filters. IEEE Trans. Med. Imag. 19(12), 1188-1201.]. Here, we use simulations to clarify the relative merit of temporal regularization based methods compared to the least squares methods with respect to the accuracy of estimating certain characteristics of the HRF such as time to peak (TTP), height (HR) and width (W) of the response. We implemented a Bayesian approach proposed by Marrelec et al. [Marrelec, G., Benali, H., Ciuciu, P., Pelegrini-Issac, M., Poline, J.-B., 2003. Robust Estimation of the Hemodynamic Response Function in Event-Related BOLD fMRI Using Basic Physiological Information. Hum. Brain Mapp. 19, 1-17., Marrelec, G., Benali, H., Ciuciu, P., Poline, J.B. Bayesian estimation of the hemodynamic of the hemodynamic response function in functional MRI. In: R. F, editor; 2001; Melville. p 229-247.] and its deterministic counterpart based on a combination of Tikhonov regularization [Tikhonov, A.N., Arsenin, V.Y., 1977. Solution of ill-posed problems. Washington DC: W.H. Winston.] and generalized cross-validation (GCV) [Wahba, G., 1990. Spline Models for Observational Data. Philadelphia: SIAM.] for selecting the regularization parameter. The performance of both methods is compared with least square estimates as a function of temporal resolution, color and strength of the noise, and the type of stimulus sequences used. In almost all situations, under the considered assumptions (e.g. linearity, time invariance and smooth HRF), the regularization-based techniques more accurately characterize the HRF compared to the least-squares method. Our results clarify the effects of temporal resolution, noise color, and experimental design on the accuracy of HRF estimation.

Rationale-The field of research regarding the effects of habitual caffeine use is immense and frequently utilizes self-report measures of caffeine use. However, various self-report measures have different methodologies, and the accuracy of these different methods has not been compared.Materials and methods-Self-reported caffeine use was estimated from two methods (a retrospective interview of weekly caffeine use and a 7-day prospective diary; n=79). These estimates were then tested against salivary caffeine concentrations in a subset of participants (n=55).Results-The estimates of caffeine use (mg/day) from the interview-and diary-based methods correlated with one another (r=0.77) and with salivary caffeine concentrations (r=0.61 and 0.68, respectively). However, almost half of the subjects who reported more than 600 mg/day in the interview reported significantly less caffeine use in the diary.Conclusions-Self-report measures of caffeine use are a valid method of predicting actual caffeine levels. Estimates of high caffeine use levels may need to be corroborated by more than one method.