A Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment

Perception involves two types of decisions about the sensory world: identification of stimulus features as analog quantities, or discrimination of the same stimulus features among a set of discrete alternatives. Veridical judgment and categorical discrimination have traditionally been conceptualized as two distinct computational problems. Here, we found that these two types of decision making can be subserved by a shared cortical circuit mechanism. We used a continuous recurrent network model to simulate two monkey experiments in which subjects were required to make either a two-alternative forced choice or a veridical judgment about the direction of random-dot motion. The model network is endowed with a continuum of bell-shaped population activity patterns, each representing a possible motion direction. Slow recurrent excitation underlies accumulation of sensory evidence, and its interplay with strong recurrent inhibition leads to decision behaviors. The model reproduced the monkey's performance as well as single-neuron activity in the categorical discrimination task. Furthermore, we examined how direction identification is determined by a combination of sensory stimulation and microstimulation. Using a population-vector measure, we found that direction judgments instantiate winner-take-all (with the population vector coinciding with either the coherent motion direction or the electrically elicited motion direction) when two stimuli are far apart, or vector averaging (with the population vector falling between the two directions) when two stimuli are close to each other. Interestingly, for a broad range of intermediate angular distances between the two stimuli, the network displays a mixed strategy in the sense that direction estimates are stochastically produced by winner-take-all on some trials and by vector averaging on the other trials, a model prediction that is experimentally testable. This work thus lends support to a common neurodynamic framework for both veridical judgment and categorical discrimination in perceptual decision making

Evaluation and Recommendations on Good Clinical Laboratory Practice Guidelines for Phase I–III Clinical Trials

Marcella Sarzotti-Kelsoe and colleagues harmonize various approaches to Good Clinical Laboratory Practice for clinical trials into a single set of recommendations

A Functional Architecture of Optic Flow in the Inferior Parietal Lobule of the Behaving Monkey

The representation of navigational optic flow across the inferior parietal lobule was assessed using optical imaging of intrinsic signals in behaving monkeys. The exposed cortex, corresponding to the dorsal-most portion of areas 7a and dorsal prelunate (DP), was imaged in two hemispheres of two rhesus monkeys. The monkeys actively attended to changes in motion stimuli while fixating. Radial expansion and contraction, and rotation clockwise and counter-clockwise optic flow stimuli were presented concentric to the fixation point at two angles of gaze to assess the interrelationship between the eye position and optic flow signal. The cortical response depended upon the type of flow and was modulated by eye position. The optic flow selectivity was embedded in a patchy architecture within the gain field architecture. All four optic flow stimuli tested were represented in areas 7a and DP. The location of the patches varied across days. However the spatial periodicity of the patches remained constant across days at ∼950 and 1100 µm for the two animals examined. These optical recordings agree with previous electrophysiological studies of area 7a, and provide new evidence for flow selectivity in DP and a fine scale description of its cortical topography. That the functional architectures for optic flow can change over time was unexpected. These and earlier results also from inferior parietal lobule support the inclusion of both static and dynamic functional architectures that define association cortical areas and ultimately support complex cognitive function

An integrative paradigm to impart quality to correlative science

Correlative studies are a primary mechanism through which insights can be obtained about the bioactivity and potential efficacy of candidate therapeutics evaluated in early-stage clinical trials. Accordingly, well designed and performed early-stage correlative studies have the potential to strongly influence further clinical development of candidate therapeutic agents, and correlative data obtained from early stage trials has the potential to provide important guidance on the design and ultimate successful evaluation of products in later stage trials, particularly in the context of emerging clinical trial paradigms such as adaptive trial design

The new cardioprotector Monohydroxyethylrutoside protects against doxorubicin-induced inflammatory effects in vitro

The new cardioprotector Monohydroxyethylrutoside protects against doxorubicin-induced inflammatory effects in vitro. Abou El Hassan MA, Verheul HM, Jorna AS, Schalkwijk C, van Bezu J, van der Vijgh WJ, Bast A. Department of Medical Oncology, Free University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands. [email protected] Besides its cardiotoxic effect, doxorubicin also elicits inflammatory effects in vivo. 7-Monohydroxyethylrutoside (monoHER) has recently been used as a protector against doxorubicin-induced cardiotoxicity in vivo. It is not known yet whether monoHER can also protect against doxorubicin-induced inflammatory effects. The aim of the present study was (1) to illustrate the inflammatory effects of doxorubicin in vitro and (2) to evaluate a possibly protective effect of monoHER. In order to demonstrate the inflammatory effects of doxorubicin and the possible protection of monoHER, proliferating human umbilical cord vascular endothelial cells (HUVECs) were incubated with different concentrations of doxorubicin ranging from 12.5 to 600 nM with(out) 200 micro M monoHER. Resting (confluent) HUVECs were incubated with (0.5-25 micro M) doxorubicin with(out) monoHER (0.2-1.2 mM) and the viability of endothelial cells and their propensity to adhere to neutrophils were measured 24 h after treatment. The localisation of adhered neutrophils was determined with immunofluorescence microscopy. To further characterise the mechanism of doxorubicin-induced neutrophil adhesion, the expression of the HUVECs surface adhesion molecules was determined after doxorubicin treatment. Doxorubicin decreased the viability and proliferation capacity of HUVECs in a concentration-dependent manner. The proliferating HUVECs were much more sensitive to doxorubicin (IC(50)=60.0+/-20.8 nM) than resting cells (LC(50)=4.0+/-0.3 micro M). Doxorubicin also increased the adhesion of neutrophils reaching a plateau value at a doxorubicin concentration of > or =0.4 micro M (P=0.0113). The induced neutrophil adhesion was accompanied by overexpression of VCAM and E-selectin but not ICAM. Although monoHER did not reverse the effect of doxorubicin on the proliferation of endothelial cells, it significantly protected resting HUVECs against the cytotoxic effect of doxorubicin (< or =25 micro M, P<0.0015). In addition, monoHER completely protected against the stimulatory effect of doxorubicin on neutrophil adhesion, and inhibited the doxorubin-induced expression of VCAM and E-selectin on the surface of treated HUVECs. This study illustrates that monoHER, which protects against doxorubicin's cardiotoxic effect, can also protect against doxorubicin-induced inflammatory effects. These data prompt further investigation about the possible link between doxorubicin-induced inflammatory effects and its cardiotoxicity in viv

A Fluctuation-Driven Mechanism for Slow Decision Processes in Reverberant Networks

The spike activity of cells in some cortical areas has been found to be correlated with reaction times and behavioral responses during two-choice decision tasks. These experimental findings have motivated the study of biologically plausible winner-take-all network models, in which strong recurrent excitation and feedback inhibition allow the network to form a categorical choice upon stimulation. Choice formation corresponds in these models to the transition from the spontaneous state of the network to a state where neurons selective for one of the choices fire at a high rate and inhibit the activity of the other neurons. This transition has been traditionally induced by an increase in the external input that destabilizes the spontaneous state of the network and forces its relaxation to a decision state. Here we explore a different mechanism by which the system can undergo such transitions while keeping the spontaneous state stable, based on an escape induced by finite-size noise from the spontaneous state. This decision mechanism naturally arises for low stimulus strengths and leads to exponentially distributed decision times when the amount of noise in the system is small. Furthermore, we show using numerical simulations that mean decision times follow in this regime an exponential dependence on the amplitude of noise. The escape mechanism provides thus a dynamical basis for the wide range and variability of decision times observed experimentally

Neurobiological Models of Two-Choice Decision Making Can Be Reduced to a One-Dimensional Nonlinear Diffusion Equation

The response behaviors in many two-alternative choice tasks are well described by so-called sequential sampling models. In these models, the evidence for each one of the two alternatives accumulates over time until it reaches a threshold, at which point a response is made. At the neurophysiological level, single neuron data recorded while monkeys are engaged in two-alternative choice tasks are well described by winner-take-all network models in which the two choices are represented in the firing rates of separate populations of neurons. Here, we show that such nonlinear network models can generally be reduced to a one-dimensional nonlinear diffusion equation, which bears functional resemblance to standard sequential sampling models of behavior. This reduction gives the functional dependence of performance and reaction-times on external inputs in the original system, irrespective of the system details. What is more, the nonlinear diffusion equation can provide excellent fits to behavioral data from two-choice decision making tasks by varying these external inputs. This suggests that changes in behavior under various experimental conditions, e.g. changes in stimulus coherence or response deadline, are driven by internal modulation of afferent inputs to putative decision making circuits in the brain. For certain model systems one can analytically derive the nonlinear diffusion equation, thereby mapping the original system parameters onto the diffusion equation coefficients. Here, we illustrate this with three model systems including coupled rate equations and a network of spiking neurons

Action planning and the timescale of evidence accumulation

Perceptual decisions are based on the temporal integration of sensory evidence for different states of the outside world. The timescale of this integration process varies widely across behavioral contexts and individuals, and it is diagnostic for the underlying neural mechanisms. In many situations, the decision-maker knows the required mapping between perceptual evidence and motor response (henceforth termed “sensory-motor contingency”) before decision formation. Here, the integrated evidence can be directly translated into a motor plan and, indeed, neural signatures of the integration process are evident as build-up activity in premotor brain regions. In other situations, however, the sensory-motor contingencies are unknown at the time of decision formation. We used behavioral psychophysics and computational modeling to test if knowledge about sensory-motor contingencies affects the timescale of perceptual evidence integration. We asked human observers to perform the same motion discrimination task, with or without trial-to-trial variations of the mapping between perceptual choice and motor response. When the mapping varied, it was either instructed before or after the stimulus presentation. We quantified the timescale of evidence integration under these different sensory-motor mapping conditions by means of two approaches. First, we analyzed subjects’ discrimination threshold as a function of stimulus duration. Second, we fitted a dynamical decision-making model to subjects’ choice behavior. The results from both approaches indicated that observers (i) integrated motion information for several hundred ms, (ii) used a shorter than optimal integration timescale, and (iii) used the same integration timescale under all sensory-motor mappings. We conclude that the mechanisms limiting the timescale of perceptual decisions are largely independent from long-term learning (under fixed mapping) or rapid acquisition (under variable mapping) of sensory-motor contingencies. This conclusion has implications for neurophysiological and neuroimaging studies of perceptual decision-making

Modeling the asymmetric evolution of a mouse and rat-specific microRNA gene cluster intron 10 of the Sfmbt2 gene

<p>Abstract</p> <p>Background</p> <p>The total number of miRNA genes in a genome, expression of which is responsible for the miRNA repertoire of an organism, is not precisely known. Moreover, the question of how new miRNA genes arise during evolution is incompletely understood. Recent data in humans and opossum indicate that retrotranspons of the class of short interspersed nuclear elements have contributed to the growth of microRNA gene clusters.</p> <p>Method</p> <p>We studied a large miRNA gene cluster in intron 10 of the mouse Sfmbt2 gene using bioinformatic tools.</p> <p>Results</p> <p>Mice and rats are unique to harbor a 55-65 Kb DNA sequence in intron 10 of the Sfmbt2 gene. This intronic region is rich in regularly repeated B1 retrotransposons together with inverted self-complementary CA/TG microsatellites. The smallest repeats unit, called MSHORT1 in the mouse, was duplicated 9 times in a tandem head-to-tail array to form 2.5 Kb MLONG1 units. The center of the mouse miRNA gene cluster consists of 13 copies of MLONG1. BLAST analysis of MSHORT1 in the mouse shows that the repeat unit is unique for intron 10 of the Sfmbt2 gene and suggest a dual phase model for growth of the miRNA gene cluster: arrangment of 10 MSHORT1 units into MLONG1 and further duplication of 13 head-to-tail MLONG1 units in the center of the miRNA gene cluster. Rats have a similar arrangment of repeat units in intron 10 of the Sfmbt2 gene. The discrepancy between 65 miRNA genes in the mouse cluster as compared to only 1 miRNA gene in the corresponding rat repeat cluster is ascribed to sequence differences between MSHORT1 and RSHORT1 that result in lateral-shifted, less-stable miRNA precursor hairpins for RSHORT1.</p> <p>Conclusion</p> <p>Our data provides new evidence for the emerging concept that lineage-specific retroposons have played an important role in the birth of new miRNA genes during evolution. The large difference in the number of miRNA genes in two closely related species (65 versus 1, mice versus rats) indicates that this species-specific evolution can be a rapid process.</p