Conjunctive spatial and self-motion codes are topographically organized in the GABAergic cells of the lateral septum

The hippocampal spatial code’s relevance for downstream neuronal populations—particularly its major subcortical output the lateral septum (LS)—is still poorly understood. Here, using calcium imaging combined with unbiased analytical methods, we functionally characterized and compared the spatial tuning of LS GABAergic cells to those of dorsal CA3 and CA1 cells. We identified a significant number of LS cells that are modulated by place, speed, acceleration, and direction, as well as conjunctions of these properties, directly comparable to hippocampal CA1 and CA3 spatially modulated cells. Interestingly, Bayesian decoding of position based on LS spatial cells reflected the animal’s location as accurately as decoding using the activity of hippocampal pyramidal cells. A portion of LS cells showed stable spatial codes over the course of multiple days, potentially reflecting long-term episodic memory. The distributions of cells exhibiting these properties formed gradients along the anterior–posterior and dorsal–ventral axes of the LS, directly reflecting the topographical organization of hippocampal inputs to the LS. Finally, we show using transsynaptic tracing that LS neurons receiving CA3 and CA1 excitatory input send projections to the hypothalamus and medial septum, regions that are not targeted directly by principal cells of the dorsal hippocampus. Together, our findings demonstrate that the LS accurately and robustly represents spatial, directional as well as self-motion information and is uniquely positioned to relay this information from the hippocampus to its downstream regions, thus occupying a key position within a distributed spatial memory network.

Calcium imaging of neurons in freely behaving mice reveals how the lateral septum, the main output of the hippocampal place cells, effectively represents information about not only location, but also head direction and self-movement, and may be pivotal in sending this information to downstream brain regions.     

From spreading depression to spatial cognition

The Laboratory of Neurophysiology of Memory started its existence in 1954 by systematic research into spreading depression of EEG activity of laboratory rodents and by the use of this remarkable phenomenon as a functional ablation method in behavioral research. Its main contributions were in the study of memory formation and consolidation, interhemispheric transfer, motor learning, conditioned taste aversion and spatial orientation and navigation. In the last five years it concentrated on navigation of rats in multiple reference frames, on electrophysiological evidence for the role of hippocampal place cells support of behavior in such dissociated frames, on the analysis of idiothetic and allothetic forms of navigation and on the mathematical methods allowing assessment of the contribution of goal directed locomotion to place cell activity. The methods used in spatial memory research in rats were used for examination of human subjects in a laboratory equipped with a tracking system for humans in the hospital Homolka. Animal models of Alzheimer disease were studied in transgenic mice with the human gene for the beta amyloid precursor protein.     

Bias-free estimation of information content in temporally sparse neuronal activity

Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic overestimation (upward bias), which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca²⁺ imaging because of the temporal sparsity of elevated Ca²⁺ signals. Here, we introduce methods to correct for the bias in the naïve estimation of information content from limited sample sizes and temporally sparse neuronal activity. We demonstrate the higher accuracy of our methods over previous ones, when applied to Ca²⁺ imaging data recorded from the mouse hippocampus and primary visual cortex, as well as to simulated data with matching tuning properties and firing statistics. Our bias-correction methods allowed an accurate estimation of the information place cells carry about the animal’s position (spatial information) and uncovered the spatial resolution of hippocampal coding. Furthermore, using our methods, we found that cells with higher peak firing rates carry higher spatial information per spike and exposed differences between distinct hippocampal subfields in the long-term evolution of the spatial code. These results could be masked by the bias when applying the commonly used naïve calculation of information content. Thus, a bias-free estimation of information content can uncover otherwise overlooked properties of the neural code.     

Online analysis of microendoscopic 1-photon calcium imaging data streams

In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop.     

Independence of landmark and self-motion-guided navigation: a different role for grid cells

Recent interest in the neural bases of spatial navigation stems from the discovery of neuronal populations with strong, specific spatial signals. The regular firing field arrays of medial entorhinal grid cells suggest that they may provide place cells with distance information extracted from the animal''s self-motion, a notion we critically review by citing new contrary evidence. Next, we question the idea that grid cells provide a rigid distance metric. We also discuss evidence that normal navigation is possible using only landmarks, without self-motion signals. We then propose a model that supposes that information flow in the navigational system changes between light and dark conditions. We assume that the true map-like representation is hippocampal and argue that grid cells have a crucial navigational role only in the dark. In this view, their activity in the light is predominantly shaped by landmarks rather than self-motion information, and so follows place cell activity; in the dark, their activity is determined by self-motion cues and controls place cell activity. A corollary is that place cell activity in the light depends on non-grid cells in ventral medial entorhinal cortex. We conclude that analysing navigational system changes between landmark and no-landmark conditions will reveal key functional properties.     

Representing episodes in the mammalian brain

Memory lets the past inform the present so that we can attain future goals. In many species, these abilities require the hippocampus. Recent experiments, in which memory demand was varied while overt behavior and the environment were kept constant, have revealed firing patterns of hippocampal neurons that corresponded with memory demands and predicted performance. Although the active population appeared to be 'place cells' that signalled location, it actually included cells the activity patterns of which distinguished the recent or pending history of behavior during identical actions that occurred in the same place. Different populations of hippocampal cells fired as a rat walked along the same spatial path on the way to different goals, and coded past, present and pending events. Other experiments provide converging data that neuronal activity is modulated by goal-directed behavioral episodes. Together, these firing patterns suggest a testable mechanism of episodic memory coding: that hippocampal dynamics encode a temporally extended, hierarchically organized representation of goal-directed behavior.     

Fully-automated image processing software to analyze calcium traces in populations of single cells

Advances in fluorescence live cell imaging over the last decade have revolutionized cell biology by providing access to single-cell information in space and time. One current limitation of live-cell imaging is the lack of automated procedures to analyze single-cell data in large cell populations. Most commercially available image processing softwares do not have built-in image segmentation tools that can automatically and accurately extract single-cell data in a time series. Consequently, individual cells are usually identified manually, a process which is time consuming and inherently low-throughput. We have developed a MATLAB-based image segmentation algorithm that reliably detects individual cells in dense populations and measures their fluorescence intensity over time. To demonstrate the value of this algorithm, we measured store-operated calcium entry (SOCE) in hundreds of individual cells. Rapid access to single-cell calcium signals in large populations allowed us to precisely determine the relationship between SOCE activity and STIM1 levels, a key component of SOCE. Our image processing tool can in principle be applied to a wide range of live-cell imaging modalities and cell-based drug screening platforms.     

Mouse Delta Opioid Receptors are Located on Presynaptic Afferents to Hippocampal Pyramidal Cells

Delta opioid receptors participate in the control of chronic pain and emotional responses. Recent data have also identified their implication in drug-context associations pointing to a modulatory role on hippocampal activity. We used fluorescent knock-in mice that express a functional delta opioid receptor fused at its carboxy terminus with the green fluorescent protein in place of the native receptor to investigate the receptor neuroanatomical distribution in this structure. Fine mapping of the pyramidal layer was performed in hippocampal acute brain slices and organotypic cultures using fluorescence confocal imaging, co-localization with pre- and postsynaptic markers and correlative light-electron microscopy. The different approaches concurred to identify delta opioid receptors on presynaptic afferents to glutamatergic principal cells. In the latter, only scarce receptors were detected that were confined within the Golgi or vesicular intracellular compartments with no receptor present at the cell surface. In the mouse hippocampus, expression of functional delta opioid receptors is therefore mostly associated with interneurons emphasizing a presynaptic modulatory effect on the pyramidal cell firing rate.     

Increased attention to spatial context increases both place field stability and spatial memory

The hippocampal formation is critical for the acquisition and consolidation of memories. When recorded in freely moving animals, hippocampal pyramidal neurons fire in a location-specific manner: they are "place" cells, comprising a hippocampal representation of the animal's environment. To explore the relationship between place cells and spatial memory, we recorded from mice in several behavioral contexts. We found that long-term stability of place cell firing fields correlates with the degree of attentional demands and that successful spatial task performance was associated with stable place fields. Furthermore, conditions that maximize place field stability greatly increase orientation to novel cues. This suggests that storage and retrieval of place cells is modulated by a top-down cognitive process resembling attention and that place cells are neural correlates of spatial memory. We propose a model whereby attention provides the requisite neuromodulatation to switch short-term homosynaptic plasticity to long-term heterosynaptic plasticity, and we implicate dopamine in this process.     

Identification of Bicluster Regions in a Binary Matrix and Its Applications

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of “coherence” patterns. These methods perform unsatisfactorily if the purpose is to identify biclusters of a constant level. This paper presents a two-step biclustering method to identify constant level biclusters for binary or quantitative data. This algorithm identifies the maximal dimensional submatrix such that the proportion of non-signals is less than a pre-specified tolerance δ. The proposed method has much higher sensitivity and slightly lower specificity than several prominent biclustering methods from the analysis of two synthetic datasets. It was further compared with the Bimax method for two real datasets. The proposed method was shown to perform the most robust in terms of sensitivity, number of biclusters and number of serotype-specific biclusters identified. However, dichotomization using different signal level thresholds usually leads to different sets of biclusters; this also occurs in the present analysis.     

A simple end-point assay for calcium channel activity

Cellular calcium signaling events are transient. Hence they are observed in real time using fluorescence imaging or electrophysiological methods that require sophisticated instrumentation and specialized skills. For high throughput assays simple and inexpensive techniques are desirable. Many calcium channels that serve as drug targets have subtypes arising from diverse subunit combinations. These need to be targeted selectively for achieving efficacy and for avoiding side effects in therapies. This in turn increases the number of calcium channels that act as drug targets. We report a novel method for intracellular calcium sensing that utilizes the calcium dependent stable interaction between CaM kinase II (CaMKII) and its ligands such as the NMDA receptor subunit GluN2B. The CaMKII-GluN2B complex formed persists as a memory of the transient increase in calcium. In a cell-based assay system GFP-α-CaMKII expressed in the cytosol responds to calcium by translocating towards GluN2B sequence motif exogenously expressed on mitochondria or endoplasmic reticulum. The resulting punctate fluorescence pattern serves as the signal for intracellular calcium release. The pattern is stable, unaffected by sample processing and is observable without real time imaging. The activities of calcium channel proteins heterologously expressed in HEK-293 cells were detected with specificity using this technique. A calcium sensor vector and a calcium sensor cell line were developed as tools to perform this technique. This technique being simple and less expensive could significantly facilitate high throughput screening in calcium channel drug discovery.     

Rhythmic modulation of theta oscillations supports encoding of spatial and behavioral information in the rat hippocampus

Oscillatory patterns of activity in various frequency ranges are ubiquitously expressed in cortical circuits. While recent studies in humans emphasized rhythmic modulations of neuronal oscillations ("second-order" rhythms), their potential involvement in information coding remains an open question. Here, we show that a rhythmic (～0.7?Hz) modulation of hippocampal theta power, unraveled by second-order spectral analysis, supports encoding of spatial and behavioral information. The phase preference of neuronal discharge within this slow rhythm significantly increases the amount of information carried by action potentials in various motor/cognitive behaviors by (1) distinguishing between the spikes fired within versus outside the place field of hippocampal place cells, (2) disambiguating place firing of neurons having multiple place fields, and (3) predicting between alternative future spatial trajectories. This finding demonstrates the relevance of second-order spectral components of brain rhythms for decoding neuronal information.     

α-Calcium calmodulin kinase II modulates the temporal structure of hippocampal bursting patterns

The alpha calcium calmodulin kinase II (α-CaMKII) is known to play a key role in CA1/CA3 synaptic plasticity, hippocampal place cell stability and spatial learning. Additionally, there is evidence from hippocampal electrophysiological slice studies that this kinase has a role in regulating ion channels that control neuronal excitability. Here, we report in vivo single unit studies, with α-CaMKII mutant mice, in which threonine 305 was replaced with an aspartate (α-CaMKII(T305D) mutants), that indicate that this kinase modulates spike patterns in hippocampal pyramidal neurons. Previous studies showed that α-CaMKII(T305D) mutants have abnormalities in both hippocampal LTP and hippocampal-dependent learning. We found that besides decreased place cell stability, which could be caused by their LTP impairments, the hippocampal CA1 spike patterns of α-CaMKII(T305D) mutants were profoundly abnormal. Although overall firing rate, and overall burst frequency were not significantly altered in these mutants, inter-burst intervals, mean number of intra-burst spikes, ratio of intra-burst spikes to total spikes, and mean intra-burst intervals were significantly altered. In particular, the intra burst intervals of place cells in α-CaMKII(T305D) mutants showed higher variability than controls. These results provide in vivo evidence that besides its well-known function in synaptic plasticity, α-CaMKII, and in particular its inhibitory phosphorylation at threonine 305, also have a role in shaping the temporal structure of hippocampal burst patterns. These results suggest that some of the molecular processes involved in acquiring information may also shape the patterns used to encode this information.     

Prefrontal cortical activity predicts the occurrence of nonlocal hippocampal representations during spatial navigation

The receptive field of a neuron describes the regions of a stimulus space where the neuron is consistently active. Sparse spiking outside of the receptive field is often considered to be noise, rather than a reflection of information processing. Whether this characterization is accurate remains unclear. We therefore contrasted the sparse, temporally isolated spiking of hippocampal CA1 place cells to the consistent, temporally adjacent spiking seen within their spatial receptive fields (“place fields”). We found that isolated spikes, which occur during locomotion, are strongly phase coupled to hippocampal theta oscillations and transiently express coherent nonlocal spatial representations. Further, prefrontal cortical activity is coordinated with and can predict the occurrence of future isolated spiking events. Rather than local noise within the hippocampus, sparse, isolated place cell spiking reflects a coordinated cortical–hippocampal process consistent with the generation of nonlocal scenario representations during active navigation.

This study of active navigation shows that, rather than being local noise within the hippocampus, sparse, isolated place cell spiking reflects a coordinated cortical-hippocampal process consistent with the generation of non-local scenario representations.     

Novel candidate genes associated with hippocampal oscillations

The hippocampus is critical for a wide range of emotional and cognitive behaviors. Here, we performed the first genome-wide search for genes influencing hippocampal oscillations. We measured local field potentials (LFPs) using 64-channel multi-electrode arrays in acute hippocampal slices of 29 BXD recombinant inbred mouse strains. Spontaneous activity and carbachol-induced fast network oscillations were analyzed with spectral and cross-correlation methods and the resulting traits were used for mapping quantitative trait loci (QTLs), i.e., regions on the genome that may influence hippocampal function. Using genome-wide hippocampal gene expression data, we narrowed the QTLs to eight candidate genes, including Plcb1, a phospholipase that is known to influence hippocampal oscillations. We also identified two genes coding for calcium channels, Cacna1b and Cacna1e, which mediate presynaptic transmitter release and have not been shown to regulate hippocampal network activity previously. Furthermore, we showed that the amplitude of the hippocampal oscillations is genetically correlated with hippocampal volume and several measures of novel environment exploration.     

Calcium imaging of epileptiform events with single-cell resolution

Epileptic discharges propagate through apparently normal circuits, although it is still unclear how this recruitment takes place. To understand the role of different classes of neurons in neocortical epilepsy, we have developed a novel imaging assay that detects which neurons participate in epileptiform discharges. Using calcium imaging of neuronal populations during bicuculline-induced spontaneous epileptiform events in slices from juvenile mouse somatosensory cortex, we find that fast calcium transients correlate with epileptiform field potentials and intracellular depolarizing shifts and can be used as an optical signature that a given neuron has participated in an epileptiform event. Our results demonstrate a novel method to characterize epileptiform events with single-cell resolution. In addition, our data are consistent with an important role for layer 5 in generating neocortical seizures and indicate that subgroups of neurons are particularly prone to epileptiform recruitment.     

Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data

The amount of metagenomic data is growing rapidly while the computational methods for metagenome analysis are still in their infancy. It is important to develop novel statistical learning tools for the prediction of associations between bacterial communities and disease phenotypes and for the detection of differentially abundant features. In this study, we presented a novel statistical learning method for simultaneous association prediction and feature selection with metagenomic samples from two or multiple treatment populations on the basis of count data. We developed a linear programming based support vector machine with and joint penalties for binary and multiclass classifications with metagenomic count data (metalinprog). We evaluated the performance of our method on several real and simulation datasets. The proposed method can simultaneously identify features and predict classes with the metagenomic count data.     

Imaging mass spectrometry statistical analysis

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research.     

Activity in a Premotor Cortical Nucleus of Zebra Finches Is Locally Organized and Exhibits Auditory Selectivity in Neurons but Not in Glia

Motor functions are often guided by sensory experience, most convincingly illustrated by complex learned behaviors. Key to sensory guidance in motor areas may be the structural and functional organization of sensory inputs and their evoked responses. We study sensory responses in large populations of neurons and neuron-assistive cells in the songbird motor area HVC, an auditory-vocal brain area involved in sensory learning and in adult song production. HVC spike responses to auditory stimulation display remarkable preference for the bird''s own song (BOS) compared to other stimuli. Using two-photon calcium imaging in anesthetized zebra finches we measure the spatio-temporal structure of baseline activity and of auditory evoked responses in identified populations of HVC cells. We find strong correlations between calcium signal fluctuations in nearby cells of a given type, both in identified neurons and in astroglia. In identified HVC neurons only, auditory stimulation decorrelates ongoing calcium signals, less for BOS than for other sound stimuli. Overall, calcium transients show strong preference for BOS in identified HVC neurons but not in astroglia, showing diversity in local functional organization among identified neuron and astroglia populations.     

Three-Dimensional Distribution of Sensory Stimulation-Evoked Neuronal Activity of Spinal Dorsal Horn Neurons Analyzed by In Vivo Calcium Imaging

The spinal dorsal horn comprises heterogeneous populations of interneurons and projection neurons, which form neuronal circuits crucial for processing of primary sensory information. Although electrophysiological analyses have uncovered sensory stimulation-evoked neuronal activity of various spinal dorsal horn neurons, monitoring these activities from large ensembles of neurons is needed to obtain a comprehensive view of the spinal dorsal horn circuitry. In the present study, we established in vivo calcium imaging of multiple spinal dorsal horn neurons by using a two-photon microscope and extracted three-dimensional neuronal activity maps of these neurons in response to cutaneous sensory stimulation. For calcium imaging, a fluorescence resonance energy transfer (FRET)-based calcium indicator protein, Yellow Cameleon, which is insensitive to motion artifacts of living animals was introduced into spinal dorsal horn neurons by in utero electroporation. In vivo calcium imaging following pinch, brush, and heat stimulation suggests that laminar distribution of sensory stimulation-evoked neuronal activity in the spinal dorsal horn largely corresponds to that of primary afferent inputs. In addition, cutaneous pinch stimulation elicited activities of neurons in the spinal cord at least until 2 spinal segments away from the central projection field of primary sensory neurons responsible for the stimulated skin point. These results provide a clue to understand neuronal processing of sensory information in the spinal dorsal horn.