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Background Patients with obsessive-compulsive disorder (OCD) display disrupted performance and abnormal lateral orbitofrontal cortex (LOFC) activity during reversal learning tasks, yet it is unknown whether compulsions and reversal learning deficits share a common neural substrate. To answer this question, we measured neural activity with in vivo calcium imaging in LOFC during compulsive grooming and reversal learning before and after fluoxetine treatment. Methods Sapap3-knockout (KO) mice were used as a model for OCD-relevant behaviors. Sapap3-KOs and control littermates were injected with virus encoding GCaMP6f and implanted with gradient-index lenses to visualize LOFC activity using miniature microscopes. Grooming, reversal learning, and neural activity were measured pre- and post-fluoxetine treatment (18mg/kg, 4 weeks). Results Baseline compulsive grooming and reversal learning impairments in KOs improved after fluoxetine treatment. Additionally, KOs display distinct patterns of abnormal LOFC activity during grooming and reversal learning, both of which normalize after fluoxetine. Finally, reversal learning-associated neurons are distributed randomly amongst grooming-associated neurons (i.e. overlap is what would be expected by chance). Conclusions In OCD, the LOFC is disrupted during both compulsive behaviors and reversal learning, yet whether these behaviors share common neural underpinnings is unknown. We find that the LOFC plays distinct roles in compulsive grooming and impaired reversal learning and their improvement with fluoxetine. These findings suggest that LOFC plays separate roles in pathophysiology and treatment of different perseverative behaviors in OCD.

In this invited review, we argue for the need to determine whether appetitive and aversive behaviors, be they goal-directed or habitual, share overlapping neural circuitry. To motivate our argument, we first summarize what is currently known about the neural circuits governing aversive and appetitive behaviors by focusing first on the three hypothesized phases of avoidance learning, and then on goal-directed and habitual reward seeking. We then provide several reasons to believe that the neural circuits of appetitive and aversive instrumental behaviors are not completely overlapping. We next discuss an experimental strategy to determine the extent of overlap based on a new computational framework that improves the identification of goal-directed and habitual actions regardless of valence. Finally, we discuss recent work in obsessive-compulsive disorder that uses this computational framework to determine whether patients perform appetitive and aversive versions of the same task using the same behavioral strategies and neural circuits.

Sensory processing deficits are increasingly recognized as core symptoms of autism spectrum disorders (ASDs). However the molecular and circuit mechanisms that lead to sensory deficits are unknown. We show that two molecularly disparate mouse models of autism display similar deficits in sensory-evoked responses in the mouse olfactory system. We find that both Cntnap2- and Shank3-deficient mice of both sexes exhibit reduced response amplitude and trial-to-trial reliability during repeated odor presentation. Mechanistically, we show that both mouse models have weaker and fewer synapses between olfactory sensory nerve (OSN) terminals and olfactory bulb tufted cells and weaker synapses between OSN terminals and inhibitory periglomerular cells. Consequently, deficits in sensory processing provide an excellent candidate phenotype for analysis in ASDs. NEW & NOTEWORTHY The genetics of autism spectrum disorder (ASD) are complex. How the many risk genes generate the similar sets of symptoms that define the disorder is unknown. In particular, little is understood about the functional consequences of these genetic alterations. Sensory processing deficits are important aspects of the ASD diagnosis and may be due to unreliable neural circuits. We show that two mouse models of autism, Cntnap2- and Shank3-deficient mice, display reduced odor-evoked response amplitudes and reliability. These data suggest that altered sensory-evoked responses may constitute a circuit phenotype in ASDs.

Lateral inhibition between pairs of olfactory bulb (OB) mitral cells (MCs) and tufted cells (TCs) is linked to a variety of computations including gain control, decorrelation, and gamma-frequency synchronization. Differential effects of lateral inhibition onto MCs and TCs via distinct lateral inhibitory circuits are one of several recently described circuit-level differences between MCs and TCs that allow each to encode separate olfactory features in parallel. Here, using acute OB slices from mice, we tested whether lateral inhibition is affected by prior odor exposure and if these effects differ between MCs and TCs. We found that early postnatal odor exposure to the M72 glomerulus ligand acetophenone increased the strength of interglomerular lateral inhibition onto TCs, but not MCs, when the M72 glomerulus was stimulated. These increases were specific to exposure to M72 ligands because exposure to hexanal did not increase the strength of M72-mediated lateral inhibition. Therefore, early life experiences may be an important factor shaping TC odor responses.SIGNIFICANCE STATEMENTResponses of olfactory (OB) bulb mitral cells (MCs) and tufted cells (TCs) are known to depend on prior odor exposure, yet the specific circuit mechanisms underlying these experience-dependent changes are unknown. Here, we show that odor exposure alters one particular circuit element, interglomerular lateral inhibition, which is known to be critical for a variety of OB computations. Early postnatal odor exposure to acetophenone, a ligand of M72 olfactory sensory neurons, increases the strength of M72-mediated lateral inhibition onto TCs, but not MCs, that project to nearby glomeruli. These findings add to a growing list of differences between MCs and TCs suggesting that that these two cell types play distinct roles in odor coding.

Understanding how each of the many interneuron subtypes affects brain network activity is critical. In the mouse olfactory system, mitral cells (MCs) and tufted cells (TCs) comprise parallel pathways of olfactory bulb output that are thought to play distinct functional roles in odor coding. Here, in acute mouse olfactory bulb slices, we test how the two major classes of olfactory bulb interneurons differentially contribute to differences in MC versus TC response properties. We show that, whereas TCs respond to olfactory sensory neuron (OSN) stimulation with short latencies regardless of stimulation intensity, MC latencies correlate negatively with stimulation intensity. These differences between MCs and TCs are caused in part by weaker excitatory and stronger inhibitory currents onto MCs than onto TCs. These differences in inhibition between MCs and TCs are most pronounced during the first 150 ms after stimulation and are mediated by glomerular layer circuits. Therefore, blocking inhibition originating in the glomerular layer, but not granule-cell-mediated inhibition, reduces MC spike latency at weak stimulation intensities and distinct temporal patterns of odor-evoked responses in MCs and TCs emerge in part due to differences in glomerular-layer-mediated inhibition.SIGNIFICANCE STATEMENTOlfactory bulb mitral and tufted cells display different odor-evoked responses and are thought to form parallel channels of olfactory bulb output. Therefore, determining the circuit-level causes that drive these differences is vital. Here, we find that longer-latency responses in mitral cells, compared with tufted cells, are due to weaker excitation and stronger glomerular-layer-mediated inhibition.

Splitting sensory information into parallel pathways is a common strategy in sensory systems. Yet, how circuits in these parallel pathways are composed to maintain or even enhance the encoding of specific stimulus features is poorly understood. Here, we have investigated the parallel pathways formed by mitral and tufted cells of the olfactory system in mice and characterized the emergence of feature selectivity in these cell types via distinct lateral inhibitory circuits. We find differences in activity-dependent lateral inhibition between mitral and tufted cells that likely reflect newly described differences in the activation of deep and superficial granule cells. Simulations show that these circuit-level differences allow mitral and tufted cells to best discriminate odors in separate concentration ranges, indicating that segregating information about different ranges of stimulus intensity may be an important function of these parallel sensory pathways. DOI: http://dx.doi.org/10.7554/eLife.16039.001, eLife digest The brain often processes different features of sensory information in separate pathways. For example, when seeing an object, information about colour and movement are processed by separate types of neurons in the eye. These neurons in turn relay information to different sets of brain areas, all of which are active at the same time. Such parallel processing was originally not thought to apply to information about smell. This was because in mammals, the two types of neurons in the brain area that processes smell seemed to play the same role. However, more recent work suggests that there are in fact differences in the responses of these two neuron types (called mitral cells and tufted cells) to odors, suggesting that the brain might use parallel processing for information about smells too. Information travels along neurons in the form of electrical signals, and this activity is often seen in the form of a series of “spikes”. In a process called lateral inhibition, the activity of one neuron can feed back and inhibit the activity of its neighbors. This is important for enhancing contrast; in terms of the sense of smell, lateral inhibition is thought to help distinguish between similar odors. A technique called optogenetics allows the activity of particular neurons in an animal’s brain to be controlled by shining light onto them. Geramita et al. have now used this technique in mice to investigate whether there are differences in how lateral inhibition works in mitral cells and tufted cells. This revealed that lateral inhibition affects mitral cells only when they are spiking at intermediate firing rates, whereas tufted cells are only affected by lateral inhibition when spiking at low firing rates. Using computer simulations, Geramita et al. show that these different responses mean that mitral cells are best at distinguishing similar smells when they are present at high concentrations, while tufted cells are best at distinguishing similar smells that are present at low concentrations. These differences also mean that, by working together, mitral and tufted cells can distinguish between smells much better than either type of neuron on its own. These results demonstrate that, as with the other senses, the brain processes information about smell using parallel pathways. Future work is now needed to see what effect switching off the activity of either mitral or tufted cells will have on an animal’s behavior. DOI: http://dx.doi.org/10.7554/eLife.16039.002

The BDNF Val(66)Met polymorphism, a possible risk variant for mental disorders, is a potent modulator of neural plasticity in humans and has been linked to deficits in gray matter structure, function, and cognition. The impact of the variant on brain white matter structure, however, is controversial and remains poorly understood. Here, we used diffusion tensor imaging to examine the effects of BDNF Val(66)Met genotype on white matter microstructure in a sample of 85 healthy Caucasian adults. We demonstrate decreases of fractional anisotropy and widespread increases in radial diffusivity in Val/Val homozygotes compared with Met-allele carriers, particularly in prefrontal and occipital pathways. These data provide an independent confirmation of prior imaging genetics work, are consistent with complex effects of the BDNF Val(66)Met polymorphism on human brain structure, and may serve to generate hypotheses about variation in white matter microstructure in mental disorders associated with this variant.

γ-Aminobutyric acid (GABA) is the chief inhibitory neurotransmitter of the human brain, and GABA-ergic dysfunction has been implicated in a variety of neuropsychiatric disorders. Recent MRS techniques have allowed the quantification of GABA concentrations in vivo, and could therefore provide biologically relevant information. Few reports have formally characterized the reproducibility of these techniques, and differences in field strength, acquisition and processing parameters may result in large differences in measured GABA values. Here, we used a J-edited, single-voxel spectroscopy method of measurement of GABA + macromolecules (GABA + ) in the anterior cingulate cortex (ACC) and right frontal white matter (rFWM) at 3 T. We measured the coefficient of variation within subjects (CVw) and intra-class correlation coefficients on two repeated scans obtained from 10 healthy volunteers with processing procedures developed in-house for the quantification of GABA + and other major metabolites. In addition, by segmenting the spectroscopic voxel into cerebrospinal fluid, gray matter and white matter, and employing a linear regression technique to extrapolate metabolite values to pure gray and white matter, we determined metabolite differences between gray and white matter in ACC and rFWM. CVw values for GABA + /creatine, GABA + /H2O, GABA + , creatine, partially co-edited glutamate + glutamine (Glx)/creatine, partially co-edited Glx and N-acetylaspartic acid (NAA)/creatine were all below 12% in both ACC and rFWM. After extrapolation to pure gray and pure white matter, CVw values for all metabolites were below 16%. We found metabolite ratios between gray and white matter for GABA + /creatine, GABA + , creatine, partially co-edited Glx and NAA/creatine to be 0.88 ± 0.21 (standard deviation), 1.52 ± 0.32, 1.77 ± 0.4, 2.69 ± 0.74 and 0.70 ± 0.05, respectively. This study validates a reproducible method for the quantification of brain metabolites, and provides information on gray/white matter differences that may be important in the interpretation of results in clinical populations. Published in 2011 by John Wiley & Sons, Ltd.

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties ( www.neuroelectro.org ), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta-band frequencies.

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties (www.neuroelectro.org), to better understand neuronal diversity -- both within and across neuron types -- and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across labs than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into 6-9 super-classes. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta-band frequencies.

Shiga toxin-producing Escherichia coli-associated haemolytic uraemic syndrome (STEC-HUS) is characterised by haemolytic anaemia, thrombocytopenia and acute kidney injury. Von Willebrand Factor (vWF) is an important mediator of normal thrombi formation and indirect evidence suggests that vWF may play an important role in Shiga toxin-induced thrombi formation. Clinical evidence supporting the role of vWF in STEC-HUS is lacking. A 10-year-old girl with type 1 von Willebrand Disease (vWD) had a mild case of STEC-HUS, with nadir haemoglobin 7.3 g/dL and platelet count 105×10(9) cells/L and peak serum creatinine 0.56 mg/L and lactate dehydrogenase 741 U/L. This is the first report of STEC-HUS in a patient with vWD. We speculate that the quantitative deficiency of vWF associated with type 1 vWD may have attenuated the course of disease by reducing platelet aggregation, complement activation and thrombi formation. This case adds to a growing literature supporting a link between vWF and STEC-HUS.

γ-Aminobutyric acid (GABA) is the chief inhibitory neurotransmitter of the human brain, and GABA-ergic dysfunction has been implicated in a variety of neuropsychiatric disorders. Recent MRS techniques have allowed the quantification of GABA concentrations in vivo, and could therefore provide biologically relevant information. Few reports have formally characterized the reproducibility of these techniques, and differences in field strength, acquisition and processing parameters may result in large differences in measured GABA values. Here, we used a J-edited, single-voxel spectroscopy method of measurement of GABA + macromolecules (GABA + ) in the anterior cingulate cortex (ACC) and right frontal white matter (rFWM) at 3 T. We measured the coefficient of variation within subjects (CVw) and intra-class correlation coefficients on two repeated scans obtained from 10 healthy volunteers with processing procedures developed in-house for the quantification of GABA + and other major metabolites. In addition, by segmenting the spectroscopic voxel into cerebrospinal fluid, gray matter and white matter, and employing a linear regression technique to extrapolate metabolite values to pure gray and white matter, we determined metabolite differences between gray and white matter in ACC and rFWM. CVw values for GABA + /creatine, GABA + /H(2) O, GABA + , creatine, partially co-edited glutamate + glutamine (Glx)/creatine, partially co-edited Glx and N-acetylaspartic acid (NAA)/creatine were all below 12% in both ACC and rFWM. After extrapolation to pure gray and pure white matter, CVw values for all metabolites were below 16%. We found metabolite ratios between gray and white matter for GABA + /creatine, GABA + , creatine, partially co-edited Glx and NAA/creatine to be 0.88 ± 0.21 (standard deviation), 1.52 ± 0.32, 1.77 ± 0.4, 2.69 ± 0.74 and 0.70 ± 0.05, respectively. This study validates a reproducible method for the quantification of brain metabolites, and provides information on gray/white matter differences that may be important in the interpretation of results in clinical populations.

Gamma-aminobutyric acid (GABA)-ergic transmission is critical for normal cortical function and is likely abnormal in a variety of neuropsychiatric disorders. We tested the in vivo effects of variations in two genes implicated in GABA function on GABA concentrations in prefrontal cortex of living subjects: glutamic acid decarboxylase 1 (GAD1), which encodes GAD67, and catechol-o-methyltransferase (COMT), which regulates synaptic dopamine in the cortex. We studied six single nucleotide polymorphisms (SNPs) in GAD1 previously associated with risk for schizophrenia or cognitive dysfunction and the val158met polymorphism in COMT in 116 healthy volunteers using proton magnetic resonance spectroscopy. Two of the GAD1 SNPs (rs1978340 (p=0.005) and rs769390 (p=0.004)) showed effects on GABA levels as did COMT val158met (p=0.04). We then tested three SNPs in GAD1 (rs1978340, rs11542313, and rs769390) for interaction with COMT val158met based on previous clinical results. In this model, rs11542313 and COMT val158met showed significant main effects (p=0.001 and 0.003, respectively) and a trend toward a significant interaction (p=0.05). Interestingly, GAD1 risk alleles for schizophrenia were associated with higher GABA/Cre, and Val-Val homozygotes had high GABA/Cre levels when on a GAD1 risk genotype background (N=6). These results support the importance of genetic variation in GAD1 and COMT in regulating prefrontal cortical GABA function. The directionality of the effects, however, is inconsistent with earlier evidence of decreased GABA activity in schizophrenia.