Faculty Bibliography

Positive mood can affect a person's tendency to gamble, possibly because positive mood fosters unrealistic optimism. At the same time, unexpected positive outcomes, often called prediction errors, influence mood. However, a linkage between positive prediction errors-the difference between expected and obtained outcomes-and consequent risk taking has yet to be demonstrated. Using a large data set of New York City lottery gambling and a model inspired by computational accounts of reward learning, we found that people gamble more when incidental outcomes in the environment (e.g., local sporting events and sunshine) are better than expected. When local sports teams performed better than expected, or a sunny day followed a streak of cloudy days, residents gambled more. The observed relationship between prediction errors and gambling was ubiquitous across the city's socioeconomically diverse neighborhoods and was specific to sports and weather events occurring locally in New York City. Our results suggest that unexpected but incidental positive outcomes influence risk taking.

The dentate gyrus (DG) and area CA3 of the hippocampus are highly organized lamellar structures which have been implicated in specific cognitive functions such as pattern separation and pattern completion. Here we describe how the anatomical organization and physiology of the DG and CA3 are consistent with structures that perform pattern separation and completion. We then raise a new idea related to the complex circuitry of the DG and CA3 where CA3 pyramidal cell 'backprojections' play a potentially important role in the sparse firing of granule cells (GCs), considered important in pattern separation. We also propose that GC axons, the mossy fibers, already known for their highly specialized structure, have a dynamic function that imparts variance - 'mossy fiber variance' - which is important to pattern separation and completion. Computational modeling is used to show that when a subset of GCs become 'dominant,' one consequence is loss of variance in the activity of mossy fiber axons and a reduction in pattern separation and completion in the model. Empirical data are then provided using an example of 'dominant' GCs - subsets of GCs that develop abnormally and have increased excitability. Notably, these abnormal GCs have been identified in animal models of disease where DG-dependent behaviors are impaired. Together these data provide insight into pattern separation and completion, and suggest that behavioral impairment could arise from dominance of a subset of GCs in the DG-CA3 network.

The 'one neuron, one neurotransmitter' doctrine states that synaptic communication between two neurons occurs through the release of a single chemical transmitter. However, recent findings suggest that neurons that communicate using more than one classical neurotransmitter are prevalent throughout the adult mammalian CNS. In particular, several populations of neurons previously thought to release only glutamate, acetylcholine, dopamine or histamine also release the major inhibitory neurotransmitter GABA. Here, we review these findings and discuss the implications of GABA co-release for synaptic transmission and plasticity.

Working memory (Work-Mem), the capacity to hold and manipulate information, activates the anterior cingulate cortex (ACC), especially its caudal subregion. Impaired Work-Mem and structural and functional abnormalities of the ACC are reported in schizophrenia. This study aims to elucidate the pathogenesis of Work-Mem dysfunction in schizophrenia by comparing metabolite concentrations across ACC subregions. This retrospective study of 18 schizophrenia cases and 10 matched controls used proton magnetic resonance spectroscopic imaging (1H-MRSI, TR/TE = 1800/35 ms, 0.5 cm3 spatial resolution) to test whether the Work-Mem Index of the Wechsler Adult Intelligence Scale, third edition is associated with differences in the rostral to caudal ACC ratios of N-acetylaspartate (NAA) and creatine (Cr). Higher caudal:rostral ACC Cr (but not NAA) concentrations were associated with decreased Work-Mem Index in cases (r = -0.6, p = 0.02), with a similar trend in controls (r = -0.56, p = 0.10), although caudal:rostral ACC Cr correlated with NAA in cases and controls (r = 0.67 and 0.62, p < 0.05 for both). NAA and Cr ratios did not correlate with myo-inositol, excluding gliosis as the underlying process. Subjects' sex and age had no effects on these relationships. The findings suggest that rostral ACC energy hypo-metabolism, possibly arising from neurodevelopmental processes, is associated with working memory impairment in schizophrenia. Changes in the rostral (not the expected caudal) subregion underscore the interconnections between the ACC subregions and may offer laboratory markers for treatment trials, etiology studies, and perhaps even enhanced identification of prodromal "at risk" subjects.

The resolution of an imaging apparatus is ideally limited by the diffraction properties of the light passing through the system aperture, but in many practical cases, inhomogeneities in the light propagating medium or imperfections in the optics degrade the image resolution. Here we introduce a powerful and practical new approach for estimating the point spread function (PSF) of an imaging system on the basis of PSF Estimation from Projected Speckle Illumination (PEPSI). PEPSI uses the fact that the speckles' phase randomness cancels the effects of the aberrations in the illumination path, thereby providing an objective pattern for measuring the deformation of the imaging path. Using this approach, both wide-field-of-view and local-PSF estimation can be obtained by calibration-free, single-speckle-pattern projection. Finally, we demonstrate the feasibility of using PEPSI estimates for resolution improvement in iterative maximum likelihood deconvolution.

Adaptation of fingertip forces to friction at the grasping surface is necessary to prevent use of inadequate or excessive grip forces. Here we investigated the effect of blocking tactile information from the fingertips non-invasively on the adaptation and efficiency of grip forces to surface friction during precision grasp. Ten neurologically intact subjects grasped and lifted an instrumented grip device with 18 different frictional surfaces under three conditions: with bare hands, with a thin layer of plastic (Tegaderm), and with an additional layer of foam affixed to the fingertips. The coefficient of friction at the finger-object interface of each surface was obtained for each subject with bare hands and Tegaderm by measuring the slip ratio (grip force/ load force) at the moment of slip. We found that the foam layer reduced sensibility for two-point discrimination and pressure sensitivity at the fingertips, but Tegaderm did not. However, Tegaderm reduced static, but not dynamic, tactile discrimination. Adaptation of fingertip grip forces to surface friction measured by the rate of change of peak grip force, and grip force efficiency measured by the grip-load force ratio at lift, showed a proportional relationship with bare hands, but were impaired with Tegaderm and foam. Activation of muscles engaged in precision grip also varied with the frictional surface with bare hands, but not with Tegaderm and foam. The results suggest that sensitivity for static tactile discrimination is necessary for feedforward and feedback control of grip forces and for adaptive modulation of muscle activity during precision grasp.

Brain-derived neurotrophic factor (BDNF) plays an important role in neurodevelopment, synaptic plasticity, learning and memory, and in preventing neurodegeneration. Despite decades of investigations into downstream signaling cascades and changes in cellular processes, the mechanisms of how BDNF reshapes circuits in vivo remain unclear. This informational gap partly arises from the fact that the bulk of studies into the molecular actions of BDNF have been performed in dissociated neuronal cultures, while the majority of studies on synaptic plasticity, learning and memory were performed in acute brain slices or in vivo. A recent study by Bowling-Bhattacharya et al., measured the proteomic changes in acute adult hippocampal slices following treatment and reported changes in proteins of neuronal and non-neuronal origin that may in concert modulate synaptic release and secretion in the slice. In this paper, we place these findings into the context of existing literature and discuss how they impact our understanding of how BDNF can reshape the brain.

BACKGROUND AND SCOPE: Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. FINDINGS: A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. CONCLUSION: We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.

PURPOSE: Development of a quantitative transverse relaxation time (T2 )-mapping platform that operates at clinically feasible timescales by employing advanced image reconstruction of radially undersampled multi spin-echo (MSE) datasets. METHODS: Data was acquired on phantom and in vivo at 3 Tesla using MSE protocols employing radial k-space sampling trajectories. In order to overcome the nontrivial spin evolution associated with MSE protocols, a numerical signal model was precalculated based on Bloch simulations of the actual pulse-sequence scheme used in the acquisition process. This signal model was subsequently incorporated into an iterative model-based image reconstruction process, producing T2 and proton-density maps. RESULTS: T2 maps of phantom and in vivo brain were successfully constructed, closely matching values produced by a single spin-echo reference scan. High-resolution mapping was also performed for the spinal cord in vivo, differentiating the underlying gray/white matter morphology. CONCLUSION: The presented MSE data-processing framework offers reliable mapping of T2 relaxation values in a approximately 5-minute timescale, free of user- and scanner-dependent variations. The use of radial k-space sampling provides further advantages in the form of high immunity to irregular physiological motion, as well as enhanced spatial resolutions, owing to its inherent ability to perform alias-free limited field-of-view imaging. Magn Reson Med, 2015. (c) 2015 Wiley Periodicals, Inc.