Brain-derived neurotrophic factor Val66Met genotype and ovarian steroids interactively modulate working memory-related hippocampal function in women: a multimodal neuroimaging study.

Preclinical evidence suggests that the actions of ovarian steroid hormones and brain-derived neurotrophic factor (BDNF) are highly convergent on brain function. Studies in humanized mice document an interaction between estrus cycle-related changes in estradiol secretion and BDNF Val66Met genotype on measures of hippocampal function and anxiety-like behavior. We believe our multimodal imaging data provide the first demonstration in women that the effects of the BDNF Val/Met polymorphism on hippocampal function are selectively modulated by estradiol. In a 6-month pharmacological hormone manipulation protocol, healthy, regularly menstruating, asymptomatic women completed positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) scans while performing the n-back working memory task during three hormone conditions: ovarian suppression induced by the gonadotropin-releasing hormone agonist, leuprolide acetate; leuprolide plus estradiol; and leuprolide plus progesterone. For each of the three hormone conditions, a discovery data set was obtained with oxygen-15 water regional cerebral blood flow PET in 39 healthy women genotyped for BDNF Val66Met, and a confirmatory data set was obtained with fMRI in 27 women. Our results, in close agreement across the two imaging platforms, demonstrate an ovarian hormone-by-BDNF interaction on working memory-related hippocampal function (PET: F2,37=9.11, P=0.00026 uncorrected, P=0.05, familywise error corrected with small volume correction; fMRI: F2,25=5.43, P=0.01, uncorrected) that reflects differential hippocampal recruitment in Met carriers but only in the presence of estradiol. These findings have clinical relevance for understanding the neurobiological basis of individual differences in the cognitive and behavioral effects of ovarian steroids in women, and may provide a neurogenetic framework for understanding neuropsychiatric disorders related to reproductive hormones as well as illnesses with sex differences in disease expression and course.

Variation in the Williams syndrome GTF2I gene and anxiety proneness interactively affect prefrontal cortical response to aversive stimuli.

Characterizing the molecular mechanisms underlying the heritability of complex behavioral traits such as human anxiety remains a challenging endeavor for behavioral neuroscience. Copy-number variation (CNV) in the general transcription factor gene, GTF2I, located in the 7q11.23 chromosomal region that is hemideleted in Williams syndrome and duplicated in the 7q11.23 duplication syndrome (Dup7), is associated with gene-dose-dependent anxiety in mouse models and in both Williams syndrome and Dup7. Because of this recent preclinical and clinical identification of a genetic influence on anxiety, we examined whether sequence variation in GTF2I, specifically the single-nucleotide polymorphism rs2527367, interacts with trait and state anxiety to collectively impact neural response to anxiety-laden social stimuli. Two hundred and sixty healthy adults completed the Tridimensional Personality Questionnaire Harm Avoidance (HA) subscale, a trait measure of anxiety proneness, and underwent functional magnetic resonance imaging (fMRI) while matching aversive (fearful or angry) facial identity. We found an interaction between GTF2I allelic variations and HA that affects brain response: in individuals homozygous for the major allele, there was no correlation between HA and whole-brain response to aversive cues, whereas in heterozygotes and individuals homozygous for the minor allele, there was a positive correlation between HA sub-scores and a selective dorsolateral prefrontal cortex (DLPFC) responsivity during the processing of aversive stimuli. These results demonstrate that sequence variation in the GTF2I gene influences the relationship between trait anxiety and brain response to aversive social cues in healthy individuals, supporting a role for this neurogenetic mechanism in anxiety.

Neural-network classification of normal and Alzheimer's disease subjects using high-resolution and low-resolution PET cameras.

UNLABELLED: Neural-network classification methods were applied to studies of FDG-PET images of the brain acquired from a total of 77 "probable" Alzheimer's disease and 124 normal subjects at two different centers. METHODS: Classification performances, as determined by relative-operating-characteristic (ROC) analyses of cross-validation experiments, were measured for FDG PET images obtained with either a 15-mm FWHM PETT V or a 6-mm FWHM Scanditronix PC-1024-7B camera for various methods of data representation. Neural networks were trained to distinguish between normal and abnormal subjects on the basis of regional metabolic patterns. For both databases, classification performance could be improved by increasing the "resolution" of the representation (decreasing the region size) and by normalizing the regional metabolic values to the value of a reference region (occipital region). RESULTS: The optimal classification performance for Scanditronix data (ROC area = 0.95) was higher than that for PETT V data (ROC area = 0.87). Under Bayesian theory, the classification performance with Scanditronix data corresponded to an ability to change a pre-test probability of disease of 50% to a post-test probability of either 90% for a positive classification or 10% for a negative classification. CONCLUSION: This classification can be used to either strongly confirm or rule out the presence of abnormalities.

Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects.

The value of PET as an objective diagnostic tool for dementia may depend on the degree to which abnormal metabolic patterns can be detected by quantitative classification methods. In these studies, a neural-network classifier based on coarse region of interest analyses was used to classify normal and abnormal FDG-PET scans. The performance of neural networks and of an expert reader were evaluated by cross-validation testing. When the "abnormal" class was represented by subjects with clinical diagnoses of "Probable Alzheimer's," the areas under the relative-operating-characteristic (ROC) curves were 0.85 and 0.89 for the neural network and the expert reader, respectively. When testing with abnormal subjects represented by "Possible AD" cases, ROC areas for both the network and the expert were 0.81. The neural network out-performed discriminant analysis. It is concluded that PET has potential for the detection of abnormal brain function in dementing diseases, and that the combination of neural networks and PET is a useful diagnostic tool. Despite the low-resolution "view" afforded the neural network, its performance was nearly equivalent to that of an expert reader.

A freeze-capture method for the study of platelet-sized particle distributions.

A rapid freezing method was developed to study the distributions of fluorescent platelet-sized particles in flows of blood suspensions through thin-walled capillary tubes. Segments of frozen tubes were mounted in a refrigerated microtome on the stage of an epifluorescence microscope. Sections of tube were cut away, images of newly exposed cross-sections were recorded on video tape, and distances of the particles from the wall were measured from recorded images. The distance data were used to construct histograms that were proportional to the local concentration. Results indicated that this method is suitable for the study of the distribution of platelet-sized particles over a wide range of hematocrit, that the basic profile is reproducible to within 15%, and that the non-uniform profile is not a result of events at the tube entrance.

Transport of platelets in flowing blood.

Distribution and transport of platelets in flowing blood were studied experimentally using suspensions of washed red cells and fluorescent latex beads as platelet analogues. Distributions of the platelet analogues were obtained from stroboscopic epifluorescence photomicrographs of flow in 50-micron channels and from images of the cut cross sections of cryogenically frozen thin-walled 200-micron tubes. Concentration profiles of platelet analogues had a substantial near-wall excess for situations with a substantial hematocrit (greater than 10%) and a substantial wall shear rate (greater than 400 s-1). The viscosity of the suspending fluid was found to affect the size of the near-wall excess and its shear-dependent onset. Additionally, the shear-rate dependence of the near-wall excess did not occur with suspensions of hardened red cells. The excess extended a substantial distance from the wall in the 200-micron tubes and a portion of the profile could be fitted to an exponential curve. The random walk model that is used to describe enhanced platelet diffusion is envisioned as a walk (lateral platelet motion) caused by shear-induced collisions with red cells. A more comprehensive random walk model that includes biased collisions produces an effective lateral motion of convective nature in addition to a diffusional motion; it is used to explain the observed nonuniform distributions of platelet analogues.