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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.

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.

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.

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.