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Variation in an animal's behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent circuit dynamics may reflect neuromodulatory influences such as that of acetylcholine (ACh). Although early literature suggested that ACh exerts broad, homogeneous control over cortical function, recent evidence indicates potential anatomical and functional segregation of cholinergic signaling. In addition, it is unclear whether states as defined by different behavioral markers reflect heterogeneous cholinergic and cortical network activity. Here, we perform simultaneous, dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that higher arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic modulation and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.

Clinical and theoretical considerations presume that patients with different personality disorder (PD) clusters will be associated with distinct alliance rupture profiles; however, there is scarce empirical literature examining this. The present study adopted a systematic framework for investigating profiles of alliance ruptures for individuals belonging to each of the three PD clusters.The sample consisted of 94 patients from a randomized controlled trial for treatment of depression. PD cluster features were assessed at intake and ruptures were assessed across treatment. Three sets of multilevel analyses were conducted to test differences between the PD clusters in the general tendency to show a rupture profile, rupture development throughout the treatment, and timing of predicting ruptures by PD within sessions.The three clusters were associated with distinct profiles of alliance ruptures. Clusters A and B were characterized by a general tendency to show more withdrawal and confrontation ruptures. Cluster A had a greater decrease in confrontation ruptures over the course of treatment, while cluster B had a greater decrease in withdrawal ruptures. Cluster C was characterized by a general tendency to show fewer withdrawal and confrontation ruptures, with a greater increase in both ruptures over the course of treatment. For withdrawal ruptures, the differences between clusters were evident from the beginning of sessions, whereas for confrontation ruptures, there was less of a difference between beginning and end of sessions.The distinct profiles of alliance ruptures for each PD cluster may contribute to progress towards tailoring treatment to individuals with PDs.

Two-photon microscopy can resolve fluorescence dynamics deep in scattering tissue, but applying this techniquein vivois limited by short working distance water-immersion objectives. Here we present an ultra long working distance (20 mm) air objective called the Cousa objective. It is optimized for performance across multiphoton imaging wavelengths, offers a >4 mm2FOV with submicron lateral resolution, and is compatible with commonly used multiphoton imaging systems. We share the full optical prescription, along with data on real world performance includingin vivocalcium imaging in a range of species and approaches.

Tuft dendrites of layer 5 pyramidal neurons form specialized compartments important for motor learning and performance, yet their computational capabilities remain unclear. Structural-functional mapping of the tuft tree from the motor cortex during motor tasks revealed two morphologically distinct populations of layer 5 pyramidal tract neurons (PTNs) that exhibit specific tuft computational properties. Early bifurcating and large nexus PTNs showed marked tuft functional compartmentalization, representing different motor variable combinations within and between their two tuft hemi-trees. By contrast, late bifurcating and smaller nexus PTNs showed synchronous tuft activation. Dendritic structure and dynamic recruitment of the N -methyl- d -aspartate (NMDA)–spiking mechanism explained the differential compartmentalization patterns. Our findings support a morphologically dependent framework for motor computations, in which independent amplification units can be combinatorically recruited to represent different motor sequences within the same tree.

Emotion regulation habits have long been implicated in risk for depression. However, research in this area traditionally adopts an approach that ignores the multifaceted nature of emotion regulation strategies, the clinical heterogeneity of depression, and potential differential relations between emotion regulation features and individual symptoms. To address limitations associated with the dominant aggregate-level approach, this study aimed to identify which features of key emotion regulation strategies are most predictive and when those features are most predictive of individual symptoms of depression across different time lags. Leveraging novel developments in the field of machine learning, artificial neural network models with feature selection were estimated using data from 460 participants who participated in a 20-wave longitudinal study with weekly assessments. At each wave, participants completed measures of repetitive negative thinking, positive reappraisal, perceived stress, and depression symptoms. Results revealed that specific features of repetitive negative thinking (wondering "why cannot I get going?" and having thoughts or images about feelings of loneliness) and positive reappraisal (looking for positive sides) were important indicators for detecting various depressive symptoms, above and beyond perceived stress. These features had overlapping and unique predictive relations with individual cognitive, affective, and somatic symptoms. Examining temporal fluctuations in the predictive utility, results showed that the utility of these emotion regulation features was stable over time. These findings illuminate potential pathways through which emotion regulation features may confer risk for depression and help to identify actionable targets for its prevention and treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Magnetic resonance imaging (MRI) and Ecological Momentary Assessment (EMA) are two powerful methods to study psychological processes that have become increasingly popular in the last two decades. Though each method is valuable in its own right, their complementary strengths have the potential to further advance our understanding of typical and atypical human behavior. Despite the increase in the number of studies that combine these methods over the last decade, there are no existing recommendations for best practices or norms for conducting and reporting the findings from these types of studies. To address the absence of such standards in the field, we conducted a systematic review of papers that combine these methods. A systematic search of peer-reviewed papers using Google Scholar resulted in a pool of 11,558 articles. Inclusion criteria were 1) data-based study in which 2) participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., population age and type, type of MRI scans and EMA assessments, sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research guidelines. A special focus is given to the ways in which the two methods are combined on an analytical level and suggestions for novel computational methods that could further advance the field.

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly coupled to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed in awake animals. Here, we took advantage of recent developments in wide-field, mesoscopic calcium imaging to monitor neural activity across the neocortex of awake mice. We develop a novel “graph of graphs” approach to quantify rapidly time-varying functional connectivity and show that spontaneous behaviors are represented by fast changes in both the activity and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular resolution 2-photon microscopy also demonstrated that the correlations among neighboring neurons and between local and large-scale networks also encodes behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks that are not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insight into how behavioral information is represented across the mammalian neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.

Variation in an animal’s behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent control of circuit dynamics may reflect neuromodulatory influences including acetylcholine (ACh). While early literature suggested ACh exerts broad, homogeneous control over cortical function, recent evidence indicates potential anatomical and functional segregation of cholinergic signaling. Additionally, it is unclear whether states as defined by different behavioral markers reflect heterogeneous cholinergic and cortical network activity. We performed simultaneous, dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that increasing arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic release and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.

Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, “success”- and “failure” related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial’s outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

Mobile devices are widely used around the world, frequently by people speaking local languages or dialects that are not well documented. For these languages, it might not be beneficial for commercial companies to develop Automatic Speech Recognition (ASR) systems, so users of these languages cannot utilize voice activation features (often using Keyword Spotting, KWS) of their devices. Standard KWS methods aim to statistically model the generation process of the speech signal, requiring hours of recorded and transcribed speech for training, and therefore are not adequate for limited-data scenarios. In this paper we propose a new KWS method, suitable for limited-data scenarios, which can be easily applied by developers. The proposed method uses a new histogram representation for words, obtained with respect to a pre-trained Gaussian Mixture Model (GMM). Sentences are represented by fixed-length global feature vectors, extracted from the response curves obtained by a word classifier. Word and sentence classifiers are trained using a discriminative approach, which is typically robust to training-set size. The dataset for training the GMM is easy to obtain, since no annotation is required. We compared the proposed system to a Hidden Markov Model (HMM) based system, trained using the same low data-resources conditions as ours, and to a state-of-the-art ASR system, trained using either the limited data scenario, or using many hours of recorded speech. In the limited data situation, our system performs better then both benchmarks in all experiments except for clean speech of children (CSLU dataset), where it performs as good as the HMM. Since the ASR benchmark performs poorly without enough training data, we also trained it without limiting the available data. In this case the ASR benchmark performs better when tested on speech of adults (TED-LIUM dataset of TED lectures) for all noise conditions, and our system performs better when tested on speech of children with low to moderate SNR values. The results demonstrate the advantages of the proposed system, and the conditions under which it performs better.

Neocortical sensory areas are generally thought to faithfully represent external stimuli. However, a growing body of evidence suggests that cortical networks exhibit considerable functional plasticity over multiple temporal scales, allowing them to modify their output to reflect ongoing behavioral demands. Nevertheless, the dynamics of sensory and non-sensory representations during acquisition of stimulus-guided behavior are not well understood. We performed longitudinal 2-photon imaging of activity in primary visual cortex (V1) of mice learning a conditioned eyeblink task. We found that both excitatory and inhibitory neurons robustly encode the visual stimulus throughout training despite a significant experience-dependent reduction in response magnitude. In contrast, both pyramidal neurons and parvalbumin-expressing interneurons exhibit emergence of behavioral representation during learning. The plasticity of visual response magnitude and behavioral representation is abolished following loss of NMDA-type glutamate receptors. Overall, our findings demonstrate that V1 networks can dynamically multiplex distinct behaviorally relevant representations over the course of learning.

Adaptive movements are critical for animal survival. To guide future actions, the brain monitors various outcomes, including achievement of movement and appetitive goals. The nature of these outcome signals and their neuronal and network realization in the motor cortex (M1), which directs skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in the murine forelimb M1. We found two populations of layer 2-3 neurons, termed success- and failure-related neurons, that develop with training, and report end results of trials. In these neurons, prolonged responses were recorded after success or failure trials independent of reward and kinematics. In addition, the initial state of layer 5 pyramidal tract neurons contained a memory trace of the previous trial's outcome. Intertrial cortical activity was needed to learn new task requirements. These M1 layer-specific performance outcome signals may support reinforcement motor learning of skilled behavior.

Existing sound retrieval systems are mostly based on a textual query. Using text to describe a sound signal is not intuitive and is often inaccurate due to subjective impression of the user; different people may use different words to describe the same sound which makes theses system complex to design and unintuitive to use. Vocal imitation, however, is the most natural human way to describe a sound. In this paper we consider a newly rising approach for sound retrieval based on vocal imitations, where the user records himself imitating the desired sound, and the system retrieves a ranked list of the most similar sounds in the dataset. In this work we represent sound signals using histograms, obtained with respect to a Gaussian Mixture Model (GMM), representing the spectral domain. This recently proposed approach was successfully applied for word representation in a keyword spotting task. Having a fixed length representation for vocal imitation signals allows us to train a robust classifier using support vector machine (SVM). Given a test imitation signal, we apply the classifier and use the output score to rank the retrieved signals, based on a majority vote. Our simulation results show that the proposed system yields a more accurate ranking compared with other existing solutions.

There has been tremendous interest in piezoelectricity at the nanoscale, for example in nanowires and nanofibers where piezoelectric properties may be enhanced or controllably tuned, thus necessitating robust characterization techniques of piezoelectric response in nanomaterials. Piezo-response force microscopy (PFM) is a well-established scanning probe technique routinely used to image piezoelectric/ferroelectric domains in thin films, however, its applicability to nanoscale objects is limited due to the requirement for physical contact with an atomic force microscope (AFM) tip that may cause dislocation or damage, particularly to soft materials, during scanning. Here we report a non-destructive PFM (ND-PFM) technique wherein the tip is oscillated into “discontinuous” contact during scanning, while applying an AC bias between tip and sample and extracting the piezoelectric response for each contact point by monitoring the resulting localized deformation at the AC frequency. ND-PFM is successfully applied to soft polymeric (poly-L-lactic acid) nanowires, as well as hard ceramic (barium zirconate titanate–barium calcium titanate) nanowires, both previously inaccessible by conventional PFM. Our ND-PFM technique is versatile and compatible with commercial AFMs, and can be used to correlate piezoelectric properties of nanomaterials with their microstructural features thus overcoming key characterisation challenges in the field.

Clinical Brain-Computer Interface (BCI) systems seek to enable paralyzed individuals to operate devices with their brain activity. Non-invasive systems based on electroen-cephalographic (EEG) signals are popular since they avoid risks associated with invasive procedures, but unfortunately EEG signals are inherently noisy, making effective classifiers challenging to develop. Commonly, new classifiers are benchmarked on signals from healthy subjects executing physical movements, under the assumption that the performance will transfer to clinical cases where only imagined movements are possible. Here, we show in contrast that classifiers trained on signals associated with actual movements perform erratically when applied to signals associated with imagined movements. We suggest that this is because the signals lay in different domains. Then, to exploit the different statistical distributions, we apply a domain adaptation technique, Frustratingly Easy Domain Adaptation (FEDA), improving classifier performance accuracy by a third on a discrimination task that simulates the clinical condition.

Common voice conversion methods are based on Gaussian Mixture Modeling (GMM), which requires exhaustive training (typically lasting hours), often leading to ill-conditioning if the dataset used is too small. We propose a new conversion method that is trained in seconds, using either small or large scale datasets. The proposed Grid-Based (GB) method is based on sequential Bayesian tracking, by which the conversion process is expressed as a sequential estimation problem of tracking the target spectrum based on the observed source spectrum. The converted MFCC vectors are sequentially evaluated using a weighted sum of the target training set used as grid-points. To improve the perceived quality of the synthesized signals, we use a post-processing block for enhancing the global variance. Objective and subjective evaluations show that the enhanced-GB method is comparable to classic GMM-based methods, in terms of quality, and comparable to their enhanced versions, in terms of individuality.

Metric learning includes a wide range of algorithms aiming to improve the classification accuracy by capturing the spatial structure of the training set. The performance of those (supervised) methods greatly depends on the amount of labeled data available for training. In practice, however, it is usually not easy to obtain a large-scale labeled set, as opposed to an unlabeled one. In this paper we propose a new method for metric learning using a small-scale labeled set and a large-scale unlabeled set. This method can be applied in two setups — a Semi-Supervised (SS) classification setup and a Domain Adaptation (DA) setup. We used two sources of hand-written digits images to demonstrate the performance of our proposed method. We show that in both SS and DA setups, the proposed method leads to fewer classification errors compared to Euclidean distance and to Large Margin Nearest Neighbor (LMNN).

Many voice conversion systems require parallel training sets of the source and target speakers. Non-parallel training is more complicated as it involves evaluation of source-target correspondence along with the conversion function itself. INCA is a recently proposed method for non-parallel training, based on iterative estimation of alignmentand conversion function. The alignment is evaluated using a simple nearestneighbor search, which often leads to phonetic miss-matched source-target pairs. We propose here a generalized approach, denoted as Temporal-Context INCA (TC-INCA), based on matching temporal context vectors. We formulate the training stage as a minimization problem of a joint cost, considering both context-based alignment and conversion function. We show that TC-INCA reduces the joint cost and prove its convergence. Experimental results indicate that TC-INCA significantly improves the alignment accuracy, compared to INCA. Moreover, subjective evaluations show that TC-INCA leads to improved quality of the synthesized output signals, when small training sets are used.

In this paper, we introduce a time-varying shorttime Fourier transform (TV-STFT) for representing discrete signals. We derive an explicit condition for perfect reconstruction using time-varying analysis and synthesis windows. Based on the derived representation, we propose an adaptive algorithm that controls the length of the analysis window to achieve a lower mean-square error (MSE) at each iteration. When compared to the conventional multiplicative transfer function approach with a fixed length analysis window, the resulting algorithm achieves faster convergence without compromising for higher steady state MSE. Experimental results demonstrate the effectiveness of the proposed approach.

The authors report the observation of a cavity mode, at λ = 0.94 µm, which is tightly confined between two slabs of two-dimensional photonic crystals deeply etched into a laser-like heterostructure waveguide. Guided photoluminescence of this heterostructure is used as an internal point source to probe the horizontal cavity.

The goal of voice conversion is to modify a source speaker's speech to sound as if spoken by a target speaker. Common conversion methods are based on Gaussian mixture modeling (GMM). They aim to statistically model the spectral structure of the source and target signals and require relatively large training sets (typically dozens of sentences) to avoid over-fitting. Moreover, they often lead to muffled synthesized output signals, due to excessive smoothing of the spectral envelopes. Mobile applications are characterized with low resources in terms of training data, memory footprint, and computational complexity. As technology advances, computational and memory requirements become less limiting; however, the amount of available training data still presents a great challenge, as a typical mobile user is willing to record himself saying just few sentences. In this paper, we propose the grid-based (GB) conversion method for such low resource environments, which is successfully trained using very few sentences (5---10). The GB approach is based on sequential Bayesian tracking, by which the conversion process is expressed as a sequential estimation problem of tracking the target spectrum based on the observed source spectrum. The converted Mel frequency cepstrum coefficient (MFCC) vectors are sequentially evaluated using a weighted sum of the target training vectors used as grid points. The training process includes simple computations of Euclidian distances between the training vectors and is easily performed even in cases of very small training sets. We use global variance (GV) enhancement to improve the perceived quality of the synthesized signals obtained by the proposed and the GMM-based methods. Using just 10 training sentences, our enhanced GB method leads to converted sentences having closer GV values to those of the target and to lower spectral distances at the same time, compared to enhanced version of the GMM-based conversion method. Furthermore, subjective evaluations show that signals produced by the enhanced GB method are perceived as more similar to the target speaker than the enhanced GMM signals, at the expense of a small degradation in the perceived quality.