A circuit mechanism for independent modulation of excitatory and inhibitory firing rates after sensory deprivation.

Diverse interneuron subtypes shape sensory processing in mature cortical circuits. During development, sensory deprivation evokes powerful synaptic plasticity that alters circuitry, but how different inhibitory subtypes modulate circuit dynamics in response to this plasticity remains unclear. We investigate how deprivation-induced synaptic changes affect excitatory and inhibitory firing rates in a microcircuit model of the sensory cortex with multiple interneuron subtypes. We find that with a single interneuron subtype (parvalbumin-expressing [PV]), excitatory and inhibitory firing rates can only be comodulated-increased or decreased together. To explain the experimentally observed independent modulation, whereby one firing rate increases and the other decreases, requires strong feedback from a second interneuron subtype (somatostatin-expressing [SST]). Our model applies to the visual and somatosensory cortex, suggesting a general mechanism across sensory cortices. Therefore, we provide a mechanistic explanation for the differential role of interneuron subtypes in regulating firing rates, contributing to the already diverse roles they serve in the cortex.

Re-emergent Tremor in Parkinson's Disease: Evidence of Pathologic ß and Prokinetic γ Activity.

BACKGROUND: Re-emergent tremor is characterized as a continuation of resting tremor and is often highly therapy refractory. This study examines variations in brain activity and oscillatory responses between resting and re-emergent tremors in Parkinson's disease. METHODS: Forty patients with Parkinson's disease (25 males, mean age, 66.78 ± 5.03 years) and 40 age- and sex-matched healthy controls were included in the study. Electroencephalogram and electromyography signals were simultaneously recorded during resting and re-emergent tremors in levodopa on and off states for patients and mimicked by healthy controls. Brain activity was localized using the beamforming technique, and information flow between sources was estimated using effective connectivity. Cross-frequency coupling was used to assess neuronal oscillations between tremor frequency and canonical frequency oscillations. RESULTS: During levodopa on, differences in brain activity were observed in the premotor cortex and cerebellum in both the patient and control groups. However, Parkinson's disease patients also exhibited additional activity in the primary sensorimotor cortex. On withdrawal of levodopa, different source patterns were observed in the supplementary motor area and basal ganglia area. Additionally, levodopa was found to suppress the strength of connectivity (P < 0.001) between the identified sources and influence the tremor frequency-related coupling, leading to a decrease in ß (P < 0.001) and an increase in γ frequency coupling (P < 0.001). CONCLUSIONS: Distinct variations in cortical-subcortical brain activity are evident in tremor phenotypes. The primary sensorimotor cortex plays a crucial role in the generation of re-emergent tremor. Moreover, oscillatory neuronal responses in pathological ß and prokinetic γ activity are specific to tremor phenotypes. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A developmental component to Huntington's disease.

Huntington's disease is a dominantly inherited disorder characterized by the dysfunction and death of cortical and striatal neurons. Striatal degeneration in Huntington's disease is due, at least in part, to defective cortical signalling to the striatum. Although Huntington's disease generally manifests at the adult stage, mouse and neuroimaging studies of presymptomatic mutation carriers suggest that it may affect neurodevelopment. In support of this notion, the development of the cortex is altered in mice with Huntington's disease and the foetuses of human Huntington's disease gene carriers. We will discuss these studies and the contribution of abnormal brain development to the later appearance of the disease.

Object Boundary Detection in Natural Images May Depend on "Incitatory" Cell-Cell Interactions.

Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional "simple cells," we labeled 30,000 natural image patches and used Bayes' rule to help determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified the following three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, and nonmonotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models, we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition-a compound effect we call "incitation"-can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a single-layer "delta" rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems.SIGNIFICANCE STATEMENT Simple cells in primary visual cortex (V1) respond to oriented edges and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell-a divisively normalized linear filter-is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

Disrupted Timing of MET Signaling Derails the Developmental Maturation of Cortical Circuits and Leads to Altered Behavior in Mice.

The molecular regulation of the temporal dynamics of circuit maturation is a key contributor to the emergence of normal structure-function relations. Developmental control of cortical MET receptor tyrosine kinase, expressed early postnatally in subpopulations of excitatory neurons, has a pronounced impact on the timing of glutamatergic synapse maturation and critical period plasticity. Here, we show that using a controllable overexpression (cto-Met) transgenic mouse, extending the duration of MET signaling after endogenous Met is switched off leads to altered molecular constitution of synaptic proteins, persistent activation of small GTPases Cdc42 and Rac1, and sustained inhibitory phosphorylation of cofilin. These molecular changes are accompanied by an increase in the density of immature dendritic spines, impaired cortical circuit maturation of prefrontal cortex layer 5 projection neurons, and altered laminar excitatory connectivity. Two photon in vivo imaging of dendritic spines reveals that cto-Met enhances de novo spine formation while inhibiting spine elimination. Extending MET signaling for two weeks in developing cortical circuits leads to pronounced repetitive activity and impaired social interactions in adult mice. Collectively, our data revealed that temporally controlled MET signaling as a critical mechanism for controlling cortical circuit development and emergence of normal behavior.

Barrels XXXV: barrels in-person.

PURPOSE OF THE STUDY: After two years of virtual meetings, the Barrels Meeting resumed to an in-person format on 10 and 11 November 2022 in La Jolla California. MATERIALS AND METHODS: The meeting focused on the rodent sensorimotor system, with a focus on integrated information from the cellular to the systems level. A series of invited and selected oral presentations were delivered in addition to a poster session. RESULTS: The latest results in the whisker-to-barrel pathway were discussed. Presentations included how the system encodes peripheral information, motor planning, and is disrupted in neurodevelopmental disorders. CONCLUSION: The 36th Annual Barrels Meeting brought together the research community to effectively discuss the latest advances in the field.

Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics.

Homeostasis is indispensable to counteract the destabilizing effects of Hebbian plasticity. Although it is commonly assumed that homeostasis modulates synaptic strength, membrane excitability, and firing rates, its role at the neural circuit and network level is unknown. Here, we identify changes in higher-order network properties of freely behaving rodents during prolonged visual deprivation. Strikingly, our data reveal that functional pairwise correlations and their structure are subject to homeostatic regulation. Using a computational model, we demonstrate that the interplay of different plasticity and homeostatic mechanisms can capture the initial drop and delayed recovery of firing rates and correlations observed experimentally. Moreover, our model indicates that synaptic scaling is crucial for the recovery of correlations and network structure, while intrinsic plasticity is essential for the rebound of firing rates, suggesting that synaptic scaling and intrinsic plasticity can serve distinct functions in homeostatically regulating network dynamics.

The dynamic shaping of local cortical circuitry by sex and age, and its relation to pattern comparison processing speed.

Previous resting-state functional magnetic resonance imaging (fMRI) studies have shown that the strength of local neural interactions decreases with distance. Here, we extend that line of research to evaluate effects of sex and age on local cortical circuitry in six cortical areas (superior frontal, precentral, postcentral, superior parietal, inferior parietal, and lateral occipital) using data acquired from 1,054 healthy young adults who participated in the Human Connectome Project. We confirmed previous findings that the strength of zero-lag correlations between prewhitened, resting-state, blood level oxygenation-dependent (BOLD) fMRI time series decreased with distance locally and documented that the rate of decrease with distance (spatial steepness) 1) was progressively lower from anterior to posterior areas, 2) was greater in women, especially in anterior areas, 3) increased with age, particularly for women, 4) was significantly correlated with percent inhibition, and 5) was positively and highly significantly correlated with pattern comparison processing speed (PCPS). A hierarchical tree clustering analysis of this dependence of PCPS on spatial steepness revealed a differential organization in processing that information between the two hemispheres, namely, a more independent vs. a more integrative processing in the left and right hemispheres, respectively. These findings document sex and age differences in dynamic local cortical interactions and provide evidence that spatial sharpening of these interactions may underlie cognitive processing speed differently organized in the two hemispheres.NEW & NOTEWORTHY Sex and age significantly affect shaping of local cortical interactions that are more limited in women and older brains. The net result is an increase in local spatial steepness of interactions, leading to a reduction of overlap among local ensembles and, hence, a more efficient information processing and an increase in the number of independent local cortical "processors." Remarkably, cognitive processing speed was positively associated with local spatial steepness, in keeping with the reasoning earlier.

Cerebral perfusion mediated by thalamo-cortical functional connectivity in non-dominant thalamus affects naming ability in aphasia.

Naming is a commonly impaired language domain in various types of aphasia. Emerging evidence supports the cortico-subcortical circuitry subserving naming processing, although neurovascular regulation of the non-dominant thalamic and basal ganglia subregions underlying post-stroke naming difficulty remains unclear. Data from 25 subacute stroke patients and 26 age-, sex-, and education-matched healthy volunteers were analyzed. Region-of-interest-wise functional connectivity (FC) was calculated to measure the strength of cortico-subcortical connections. Cerebral blood flow (CBF) was determined to reflect perfusion levels. Correlation and mediation analyses were performed to identify the relationship between cortico-subcortical connectivity, regional cerebral perfusion, and naming performance. We observed increased right-hemispheric subcortical connectivity in patients. FC between the right posterior superior temporal sulcus (pSTS) and lateral/medial prefrontal thalamus (lPFtha/mPFtha) exhibited significantly negative correlations with total naming score. Trend-level increased CBF in subcortical nuclei, including that in the right lPFtha, and significant negative correlations between naming and regional perfusion of the right lPFtha were observed. The relationship between CBF in the right lPFtha and naming was fully mediated by the lPFtha-pSTS connectivity in the non-dominant hemisphere. Our findings suggest that perfusion changes in the right thalamic subregions affect naming performance through thalamo-cortical circuits in post-stroke aphasia. This study highlights the neurovascular pathophysiology of the non-dominant hemisphere and demonstrates thalamic involvement in naming after stroke.

Chemogenetic inhibition of corticostriatal circuits reduces cued reinstatement of methamphetamine seeking.

Methamphetamine (meth) causes enduring changes within the medial prefrontal cortex (mPFC) and the nucleus accumbens (NA). Projections from the mPFC to the NA have a distinct dorsal-ventral distribution, with the prelimbic (PL) mPFC projecting to the NAcore, and the infralimbic (IL) mPFC projecting to the NAshell. Inhibition of these circuits has opposing effects on cocaine relapse. Inhibition of PL-NAcore reduces cued reinstatement of cocaine seeking and IL-NAshell inhibition reinstates cocaine seeking. Meth, however, exhibits a different profile, as pharmacological inhibition of either the PL or IL decrease cued reinstatement of meth-seeking. The potentially opposing roles of the PL-NAcore and IL-NAshell projections remain to be explored in the context of cued meth seeking. Here we used an intersectional viral vector approach that employs a retrograde delivery of Cre from the NA and Cre-dependent expression of DREADD in the mPFC, in both male and female rats to inhibit or activate these parallel pathways. Inhibition of the PL-NAcore circuit reduced cued reinstatement of meth seeking under short and long-access meth self-administration and after withdrawal with and without extinction. Inhibition of the IL-NAshell also decreased meth cued reinstatement. Activation of the parallel circuits was without an effect. These studies show that inhibition of the PL-NAcore or the IL-NAshell circuits can inhibit reinstated meth seeking. Thus, the neural circuitry mediating cued reinstatement of meth seeking is similar to cocaine in the dorsal, but not ventral, mPFC-NA circuit.

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain-machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain's structure.

Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition.

The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the "higher-order" information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure-function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.

Conditional knockout of MET receptor tyrosine kinase in cortical excitatory neurons leads to enhanced learning and memory in young adult mice but early cognitive decline in older adult mice.

Human genetic studies established MET gene as a risk factor for autism spectrum disorders. We have previously shown that signaling mediated by MET receptor tyrosine kinase, expressed in early postnatal developing forebrain circuits, controls glutamatergic neuron morphological development, synapse maturation, and cortical critical period plasticity. Here we investigated how MET signaling affects synaptic plasticity, learning and memory behavior, and whether these effects are age-dependent. We found that in young adult (postnatal 2-3 months) Met conditional knockout (Metfx/fx:emx1cre, cKO) mice, the hippocampus exhibits elevated plasticity, measured by increased magnitude of long-term potentiation (LTP) and depression (LTD) in hippocampal slices. Surprisingly, in older adult cKO mice (10-12 months), LTP and LTD magnitudes were diminished. We further conducted a battery of behavioral tests to assess learning and memory function in cKO mice and littermate controls. Consistent with age-dependent LTP/LTD findings, we observed enhanced spatial memory learning in 2-3 months old young adult mice, assessed by hippocampus-dependent Morris water maze test, but impaired spatial learning in 10-12 months mice. Contextual and cued learning were further assessed using a Pavlovian fear conditioning test, which also revealed enhanced associative fear acquisition and extinction in young adult mice, but impaired fear learning in older adult mice. Lastly, young cKO mice also exhibited enhanced motor learning. Our results suggest that a shift in the window of synaptic plasticity and an age-dependent early cognitive decline may be novel circuit pathophysiology for a well-established autism genetic risk factor.

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memories composed of varying numbers of associations and compared the resulting network properties with those observed experimentally. We show that, when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overexpression of specific 2- and 3-neuron motifs, distributions of connection numbers in clusters of 3-8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. In addition, memories loaded into the network can be retrieved, even in the presence of noise that is comparable with the baseline variations in the postsynaptic potential. The confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning. We predict that overexpression of excitatory-excitatory bidirectional connections observed in many cortical systems must be accompanied with underexpression of bidirectionally connected inhibitory-excitatory neuron pairs.SIGNIFICANCE STATEMENT Many structural and dynamical properties of local cortical networks are ubiquitously present across areas and species. Because synaptic connectivity is shaped by experience, we wondered whether continual learning, rather than genetic control, is responsible for producing such features. To answer this question, we developed a biologically constrained recurrent network of excitatory and inhibitory neurons capable of learning predefined sequences of network states. Embedding such associative memories into the network revealed that, when individual neurons are robustly loaded with a near-maximum amount of memories they can support, the network develops many properties that are consistent with experimental observations. Our findings suggest that basic structural and dynamical properties of local networks in the brain are simply a byproduct of learning and memory storage.

Diversity and Connectivity of Layer 5 Somatostatin-Expressing Interneurons in the Mouse Barrel Cortex.

Inhibitory interneurons represent 10-15% of the neurons in the somatosensory cortex, and their activity powerfully shapes sensory processing. Three major groups of GABAergic interneurons have been defined according to developmental, molecular, morphological, electrophysiological, and synaptic features. Dendritic-targeting somatostatin-expressing interneurons (SST-INs) have been shown to display diverse morphological, electrophysiological, and molecular properties and activity patterns in vivo However, the correlation between these properties and SST-IN subtype is unclear. In this study, we aimed to correlate the morphological diversity of layer 5 (L5) SST-INs with their electrophysiological and molecular diversity in mice of either sex. Our morphological analysis demonstrated the existence of three subtypes of L5 SST-INs with distinct electrophysiological properties: T-shaped Martinotti cells innervate L1, and are low-threshold spiking; fanning-out Martinotti cells innervate L2/3 and the lower half of L1, and show adapting firing patterns; non-Martinotti cells innervate L4, and show a quasi-fast spiking firing pattern. We estimated the proportion of each subtype in L5 and found that T-shaped Martinotti, fanning-out Martinotti, and Non-Martinotti cells represent ∼10, ∼50, and ∼40% of L5 SST-INs, respectively. Last, we examined the connectivity between the three SST-IN subtypes and L5 pyramidal cells (PCs). We found that L5 T-shaped Martinotti cells inhibit the L1 apical tuft of nearby PCs; L5 fanning-out Martinotti cells also inhibit nearby PCs but they target the dendrite mainly in L2/3. On the other hand, non-Martinotti cells inhibit the dendrites of L4 neurons while avoiding L5 PCs. Our data suggest that morphologically distinct SST-INs gate different excitatory inputs in the barrel cortex.SIGNIFICANCE STATEMENT Morphologically diverse layer 5 SST-INs show different patterns of activity in behaving animals. However, little is known about the abundance and connectivity of each morphological type and the correlation between morphological subtype and spiking properties. We demonstrate a correlation between the morphological and electrophysiological diversity of layer 5 SST-INs. Based on these findings we built a classifier to infer the abundance of each morphological subtype. Last, using paired recordings combined with morphological analysis, we investigated the connectivity of each morphological subtype. Our data suggest that, by targeting different cell types and cellular compartments, morphologically diverse SST-INs might gate different excitatory inputs in the mouse barrel cortex.

Layer-specific excitation/inhibition balances during neuronal synchronization in the visual cortex.

KEY POINTS: Understanding the balance between synaptic excitation and inhibition in cortical circuits in the brain, and how this contributes to cortical rhythms, is fundamental to explaining information processing in the cortex. This study used cortical layer-specific optogenetic activation in mouse cortex to show that excitatory neurons in any cortical layer can drive powerful gamma rhythms, while inhibition balances excitation. The net impact of this is to keep activity within each layer in check, but simultaneously to promote the propagation of activity to downstream layers. The data show that rhythm-generating circuits exist in all principle layers of the cortex, and provide layer-specific balances of excitation and inhibition that affect the flow of information across the layers. ABSTRACT: Rhythmic activity can synchronize neural ensembles within and across cortical layers. While gamma band rhythmicity has been observed in all layers, the laminar sources and functional impacts of neuronal synchronization in the cortex remain incompletely understood. Here, layer-specific optogenetic stimulation demonstrates that populations of excitatory neurons in any cortical layer of the mouse's primary visual cortex are sufficient to powerfully entrain neuronal oscillations in the gamma band. Within each layer, inhibition balances excitation and keeps activity in check. Across layers, translaminar output overcomes inhibition and drives downstream firing. These data establish that rhythm-generating circuits exist in all principle layers of the cortex, but provide layer-specific balances of excitation and inhibition that may dynamically shape the flow of information through cortical circuits. These data might help explain how excitation/inhibition (E/I) balances across cortical layers shape information processing, and shed light on the diverse nature and functional impacts of cortical gamma rhythms.

Invariant and heritable local cortical organization as revealed by fMRI.

Neural interactions in local cortical networks critically depend on the distance between interacting elements: the shorter the distance, the stronger the interactions. Here we quantified these interactions in six cortical areas of 854 individuals, including monozygotic and dizygotic twins, nontwin siblings, and nonrelated individuals. We found that the strength of zero-lag correlation between prewhitened, resting-state, blood level oxygenation-dependent functional magnetic resonance imaging time series decreased with distance as a power law. The rate of decrease, b, varied among individuals by ~1.9×, was highly correlated between hemispheres, but differed among areas (by ~1.2×) in a systematic fashion, becoming progressively less steep from frontal to occipital areas. With respect to twin status, b was significantly correlated between monozygotic twins, less so between dizygotic twins or nontwin siblings, and not at all in nonrelated individuals. These results quantify the lawful, distance-related cortical interactions and demonstrate, for the first time, the heritability of their power law. NEW & NOTEWORTHY Local cortical circuitry involves orderly neuronal interactions. A key feature of these interactions is that they are stronger the closer the interacting neurons. Here we quantified this crucial dependence of neural interactions on distance with functional magnetic resonance imaging and found that the strength of interactions decreases with distance as a power law that is very similar in all cortical lobes and heritable. These findings identify an invariant and heritable property of local cortical organization.

Region- and Layer-Specific Activation of the Higher Order Auditory Cortex Te2 after Remote Retrieval of Fear or Appetitive Memories.

The auditory cortex is involved in encoding sounds which have acquired an emotional-motivational charge. However, the neural circuitry engaged by emotional memory processes in the auditory cortex is poorly understood. In this study, we investigated the layers and regions that are recruited in the higher order auditory cortex Te2 by a tone previously paired to either fear or appetitive stimuli in rats. By tracking the protein coded by the immediate early gene zif268, we found that fear memory retrieval engages layers II-III in most regions of Te2. These results were neither due to an enhanced fear state nor to fear-evoked motor responses, as they were absent in animals retrieving an olfactory fear memory. These layers were also activated by appetitive auditory memory retrieval. Strikingly, layer IV was recruited by fear, but not appetitive memories, whereas layer V activity was related to the behavioral responses displayed to the CS. In addition to revealing the layers and regions that are recruited in the Te2 by either fear or appetitive remote memories, our study also shows that the neural circuitry within the Te2 that processes and stores emotional memories varies on the basis of the affective motivational charge of tones.

Deep two-photon brain imaging with a red-shifted fluorometric Ca2+ indicator.

In vivo Ca2+ imaging of neuronal populations in deep cortical layers has remained a major challenge, as the recording depth of two-photon microscopy is limited because of the scattering and absorption of photons in brain tissue. A possible strategy to increase the imaging depth is the use of red-shifted fluorescent dyes, as scattering of photons is reduced at long wavelengths. Here, we tested the red-shifted fluorescent Ca2+ indicator Cal-590 for deep tissue experiments in the mouse cortex in vivo. In experiments involving bulk loading of neurons with the acetoxymethyl (AM) ester version of Cal-590, combined two-photon imaging and cell-attached recordings revealed that, despite the relatively low affinity of Cal-590 for Ca2+ (Kd=561 nM), single-action potential-evoked Ca2+ transients were discernable in most neurons with a good signal-to-noise ratio. Action potential-dependent Ca2+ transients were recorded in neurons of all six layers of the cortex at depths of up to -900 µm below the pial surface. We demonstrate that Cal-590 is also suited for multicolor functional imaging experiments in combination with other Ca2+ indicators. Ca2+ transients in the dendrites of an individual Oregon green 1,2-bis(o-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid-1 (OGB-1)-labeled neuron and the surrounding population of Cal-590-labeled cells were recorded simultaneously on two spectrally separated detection channels. We conclude that the red-shifted Ca2+ indicator Cal-590 is well suited for in vivo two-photon Ca2+ imaging experiments in all layers of mouse cortex. In combination with spectrally different Ca2+ indicators, such as OGB-1, Cal-590 can be readily used for simultaneous multicolor functional imaging experiments.

Inhibitory interneurons in Alzheimer's disease.

Alzheimer's disease is currently the most common neurodegenerative disorder, characterized by distinct cognitive and sensory deficits. The underlying pathogenetic mechanisms, however, still remain elusive. How the molecular and morphological changes associated with Alzheimer's disease affect information processing in neuronal circuits and translate into cognitive dysfunction is unclear. Inhibitory interneurons have recently emerged as one of the earliest and important culprits in mediating dysfunction of neuronal circuits in neurodegeneration. Amyloid-beta and tau protein have been both linked to interneuron dysfunction, and likely play an important, albeit unknown, role in mediating changes in the overall activity of neuronal circuits. Resolving the role of inhibitory interneurons in neurodegeneration-specific changes in neuronal activity is crucial for understanding the impact of Alzheimer's disease on brain function and even for possible identification of effective treatments and diagnostic techniques (Ref. 63).