Measuring and filtering reactive inhibition is essential for assessing serial decision making and learning

Learning complex structures from stimuli requires extended exposure and often repeated observation of the same stimuli. Learning induces stimulus-dependent changes in specific performance measures. The same performance measures, however, can also be affected by processes that arise due to extended training (e.g. fatigue) but are otherwise independent from learning. Thus, a thorough assessment of the properties of learning can only be achieved by identifying and accounting for the effects of such processes. Reactive inhibition is a process that modulates behavioral performance measures on a wide range of time scales and often has opposite effects than learning. Here we develop a tool to disentangle the effects of reactive inhibition from learning in the context of an implicit learning task, the alternating serial reaction time task. Our method highlights that the magnitude of the effect of reactive inhibition on measured performance is larger than that of the acquisition of statistical structure from stimuli. We show that the effect of reactive inhibition can be identified not only in population measures but also at the level of performance of individuals, revealing varying degrees of contribution of reactive inhibition. Finally, we demonstrate that a higher proportion of behavioral variance can be explained by learning once the effects of reactive inhibition are eliminated. These results demonstrate that reactive inhibition has a fundamental effect on the behavioral performance that can be identified in individual participants and can be separated from other cognitive processes like learning

Essential thalamic contribution to slow waves of natural sleep

Slow waves represent one of the prominent EEG signatures of non-rapid eye movement (non-REM) sleep and are thought to play an important role in the cellular and network plasticity that occurs during this behavioral state. These slow waves of natural sleep are currently considered to be exclusively generated by intrinsic and synaptic mechanisms within neocortical territories, although a role for the thalamus in this key physiological rhythm has been suggested but never demonstrated. Combining neuronal ensemble recordings, microdialysis, and optogenetics, here we show that the block of the thalamic output to the neocortex markedly (up to 50%) decreases the frequency of slow waves recorded during non-REM sleep in freely moving, naturally sleeping-waking rats. A smaller volume of thalamic inactivation than during sleep is required for observing similar effects on EEG slow waves recorded during anesthesia, a condition in which both bursts and single action potentials of thalamocortical neurons are almost exclusively dependent on T-type calcium channels. Thalamic inactivation more strongly reduces spindles than slow waves during both anesthesia and natural sleep. Moreover, selective excitation of thalamocortical neurons strongly entrains EEG slow waves in a narrow frequency band (0.75-1.5 Hz) only when thalamic T-type calcium channels are functionally active. These results demonstrate that the thalamus finely tunes the frequency of slow waves during non-REM sleep and anesthesia, and thus provide the first conclusive evidence that a dynamic interplay of the neocortical and thalamic oscillators of slow waves is required for the full expression of this key physiological EEG rhythm

Tracking the contribution of inductive bias to individualised internal models

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence

Evolutionary Constraint Helps Unmask a Splicing Regulatory Region in BRCA1 Exon 11

BACKGROUND: Alternative splicing across exon 11 produces several BRCA1 isoforms. Their proportion varies during the cell cycle, between tissues and in cancer suggesting functional importance of BRCA1 splicing regulation around this exon. Although the regulatory elements driving exon 11 splicing have never been identified, a selective constraint against synonymous substitutions (silent nucleotide variations that do not alter the amino acid residue sequence) in a critical region of BRCA1 exon 11 has been reported to be associated with the necessity to maintain regulatory sequences. METHODOLOGY/PRINCIPAL FINDINGS: Here we have designed a specific minigene to investigate the possibility that this bias in synonymous codon usage reflects the need to preserve the BRCA1 alternative splicing program. We report that in-frame deletions and translationally silent nucleotide substitutions in the critical region affect splicing regulation of BRCA1 exon 11. CONCLUSIONS/SIGNIFICANCE: Using a hybrid minigene approach, we have experimentally validated the hypothesis that the need to maintain correct alternative splicing is a selective pressure against translationally silent sequence variations in the critical region of BRCA1 exon 11. Identification of the trans-acting factors involved in regulating exon 11 alternative splicing will be important in understanding BRCA1-associated tumorigenesis

A quantitative explanation of the observed population of Milky Way satellite galaxies

We revisit the well known discrepancy between the observed number of Milky Way (MW) dwarf satellite companions and the predicted population of cold dark matter (CDM) sub-halos, in light of the dozen new low luminosity satellites found in SDSS imaging data and our recent calibration of the SDSS satellite detection efficiency, which implies a total population far larger than these dozen discoveries. We combine a dynamical model for the CDM sub-halo population with simple, physically motivated prescriptions for assigning stellar content to each sub-halo, then apply observational selection effects and compare to the current observational census. As expected, models in which the stellar mass is a constant fraction F(Omega_b/Omega_m) of the sub-halo mass M_sat at the time it becomes a satellite fail for any choice of F. However, previously advocated models that invoke suppression of gas accretion after reionization in halos with circular velocity v_c <~ 35 km/s can reproduce the observed satellite counts for -15 < M_V < 0, with F ~ 10^{-3}. Successful models also require strong suppression of star formation BEFORE reionization in halos with v_c <~ 10 km/s; models without pre-reionization suppression predict far too many satellites with -5 < M_V < 0. Our models also reproduce the observed stellar velocity dispersions ~ 5-10 km/s of the SDSS dwarfs given the observed sizes of their stellar distributions, and model satellites have M(<300 pc) ~ 10^7 M_sun as observed even though their present day total halo masses span more than two orders of magnitude. Our modeling shows that natural physical mechanisms acting within the CDM framework can quantitatively explain the properties of the MW satellite population as it is presently known, thus providing a convincing solution to the `missing satellite' problem.Comment: 18 pages, 14 figures, accepted to ApJ. Minor changes following referees repor

Increased Global Functional Connectivity Correlates with LSD-Induced Ego Dissolution.

Lysergic acid diethylamide (LSD) is a non-selective serotonin-receptor agonist that was first synthesized in 1938 and identified as (potently) psychoactive in 1943. Psychedelics have been used by indigenous cultures for millennia [1]; however, because of LSD's unique potency and the timing of its discovery (coinciding with a period of major discovery in psychopharmacology), it is generally regarded as the quintessential contemporary psychedelic [2]. LSD has profound modulatory effects on consciousness and was used extensively in psychological research and psychiatric practice in the 1950s and 1960s [3]. In spite of this, however, there have been no modern human imaging studies of its acute effects on the brain. Here we studied the effects of LSD on intrinsic functional connectivity within the human brain using fMRI. High-level association cortices (partially overlapping with the default-mode, salience, and frontoparietal attention networks) and the thalamus showed increased global connectivity under the drug. The cortical areas showing increased global connectivity overlapped significantly with a map of serotonin 2A (5-HT2A) receptor densities (the key site of action of psychedelic drugs [4]). LSD also increased global integration by inflating the level of communication between normally distinct brain networks. The increase in global connectivity observed under LSD correlated with subjective reports of "ego dissolution." The present results provide the first evidence that LSD selectively expands global connectivity in the brain, compromising the brain's modular and "rich-club" organization and, simultaneously, the perceptual boundaries between the self and the environment.This research received financial support from the Safra Foundation (who fund DJN as the Edmond J. Safra Professor of Neuropsychopharmacology) and the Beckley Foundation (it was conducted as part of the Beckley-Imperial research programme). ET is supported by a postdoctoral fellowship of the AXA Research Fund. RCH is supported by an MRC clinical development scheme grant. SDM is supported by a Royal Society of New Zealand Rutherford Discovery Fellowship. KM is supported by a Wellcome Trust Fellowship (WT090199). The researchers would like to thank supporters of the Walacea.com crowd-funding campaign for helping to secure the funds required to complete the study. This report presents independent research carried out at the NIHR/Wellcome Trust Imperial Clinical Research Facility. Authors declare no conflict of interest.This is the author accepted manuscript. The final version is available from Cell Press via http://dx.doi.org/10.1016/j.cub.2016.02.01

Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG

This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia

LLAMA : stellar populations in the nuclei of ultra-hard X-ray-selected AGN and matched inactive galaxies

The relation between nuclear (.50 pc) star formation and nuclear galactic activity is still elusive; theoretical models predict a link between the two, but it is unclear whether active galactic nuclei (AGNs) should appear at the same time, before, or after nuclear star formation activity. We present a study of this relation in a complete, volume-limited sample of nine of the most luminous (log L14−195 keV > 1042.5 erg s−1 ) local AGNs (the LLAMA sample), including a sample of 18 inactive control galaxies (six star-forming; 12 passive) that are matched by Hubble type, stellar mass (9.5 . log M?/M . 10.5), inclination, and distance. This allows us to calibrate our methods on the control sample and perform a differential analysis between the AGN and control samples. We performed stellar population synthesis on VLT/X-shooter spectra in an aperture corresponding to a physical radius of ≈150 pc. We find young (.30 Myr) stellar populations in seven out of nine AGNs and in four out of six star-forming control galaxies. In the non-star-forming control population, in contrast, only two out of 12 galaxies show such a population. We further show that these young populations are not indicative of ongoing star formation, providing evidence for models that see AGN activity as a consequence of nuclear star formation. Based on the similar nuclear star formation histories of AGNs and star-forming control galaxies, we speculate that the latter may turn into the former for some fraction of their time. Under this assumption, and making use of the volume completeness of our sample, we infer that the AGN phase lasts for about 5% of the nuclear starburst phase

Specific human leukocyte antigen DQ influence on expression of antiislet autoantibodies and progression to type 1 diabetes

Human leukocyte antigen (HLA) DQ haplotypes have the strongest genetic association with type 1 diabetes (T1DM) risk. OBJECTIVE: The objective of the study was to analyze whether HLA DQ alleles influence the development of antiislet autoantibodies, the progression to T1DM among autoantibody-positive relatives, or both. DESIGN: The Diabetes Prevention Trial-1 screened more than 90,000 nondiabetic relatives of patients for cytoplasmic islet-cell autoantibody (ICA) expression between 1994 and 2002. SETTING: The study was conducted in the general community. PARTICIPANTS: The Diabetes Prevention Trial-1 found 2817 ICA-positive relatives who were tested for biochemical autoantibodies (GAD65, ICA512, and insulin) and HLA-DQ haplotypes, and 2796 of them were followed up for progression to diabetes for up to 8 yr (median, 3.6 yr). MAIN OUTCOME MEASURE: Progression to T1DM was measured. RESULTS: High-risk DQ haplotypes and genotypes were associated with a higher percentage of relatives expressing multiple biochemical autoantibodies and higher T1DM risk (e.g., respectively, 59 and 36% at 5 yr for carriers of the DQA1*0301-DQB1*0302/DQA1*0501-DQB1*0201 genotype). The number of autoantibodies expressed significantly increased T1DM risk and across different DQ genotypes, autoantibody positivity directly correlated with diabetes risk. However, multivariate analyses indicated that the influence of most genotypes on T1DM risk was not independent from autoantibody expression, with the possible exception of DQA1*0102-DQB1*0602. Specific genotypic combinations conferred 5-yr diabetes risks significantly lower (e.g. 7%-DQA1*0201-DQB1*0201/DQA1*0501-DQB1*0201 and 14%-DQA1*0301-DQB1*0301/DQA1*0501-DQB1*0201) than when those haplotypes were found in other combinations. CONCLUSION: HLA DQ alleles determine autoantibody expression, which is correlated with diabetes progression. Among autoantibody-positive relatives, most HLA DQ genotypes did not further influence T1DM risk