Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment.

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which 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., 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. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

Back to Normal? Harnessing Long Short-term Memory Network to Examine the Associations Between Adolescent Social Interactions and Depressive Symptoms During Different Stages of COVID-19.

Adolescence is a developmental period in which social interactions are critical for mental health. While the onset of COVID-19 significantly disrupted adolescents' social environments and mental health, it remains unclear how adolescents have adapted to later stages of the pandemic. We harnessed a machine learning architecture of Long Short-Term Memory recurrent networks (LSTM) with gradient-based feature importance, to model the association among daily social interactions and depressive symptoms during three stages of the pandemic. A year before COVID-19, 148 adolescents reported social interactions and depressive symptoms, every day for 21 days. One hundred sixteen of these youths completed a 28-day diary after schools closed due to COVID-19. Seventy-nine of these youths and additional 116 new participants completed a 28-day diary approximately a year into the pandemic. Our results show that LSTM successfully predicted depressive symptoms from at least a week of social interactions for all three waves (r2 > .70). Our study shows the utility of using an analytic approach that can identify temporal and nonlinear pathways through which social interactions may confer risk for depression. Our unique analysis of the importance of input features enabled us to interpret the association between social interactions and depressive symptoms. Collectively, we observed a return to pre-pandemic patterns a year into the pandemic, with reduced gender and age differences during the pandemic closures. This pattern suggests that the system of social influences in adolescence was affected by COVID-19, and that this effect was attenuated in more chronic stages of the pandemic.

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior.

Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.

The Cousa objective: a long-working distance air objective for multiphoton imaging in vivo.

Multiphoton microscopy can resolve fluorescent structures and dynamics deep in scattering tissue and has transformed neural imaging, but applying this technique in vivo can be limited by the mechanical and optical constraints of conventional objectives. Short working distance objectives can collide with compact surgical windows or other instrumentation and preclude imaging. 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 more than 4 mm2 field of view with submicrometer lateral resolution and is compatible with commonly used multiphoton imaging systems. A novel mechanical design, wider than typical microscope objectives, enabled this combination of specifications. We share the full optical prescription, and report performance including in vivo two-photon and three-photon imaging in an array of species and preparations, including nonhuman primates. The Cousa objective can enable a range of experiments in neuroscience and beyond.

Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity.

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.

Review of data processing of functional optical microscopy for neuroscience.

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

Which features of repetitive negative thinking and positive reappraisal predict depression? An in-depth investigation using artificial neural networks with feature selection.

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).

Dynamic compartmental computations in tuft dendrites of layer 5 neurons during motor behavior.

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.

Alliance rupture profiles by personality disorder pathology in psychotherapy for depression: Tendencies, development, and timing.

OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSION: The distinct profiles of alliance ruptures for each PD cluster may contribute to progress towards tailoring treatment to individuals with PDs.

NMDAR-Dependent Emergence of Behavioral Representation in Primary Visual Cortex.

Although neocortical sensory areas are generally thought to faithfully represent external stimuli, cortical networks exhibit considerable functional plasticity, allowing them to modify their output to reflect ongoing behavioral demands. We apply longitudinal 2-photon imaging of activity in the primary visual cortex (V1) of mice learning a conditioned eyeblink task to investigate the dynamic representations of task-relevant information. We find that, although all V1 neurons robustly and stably encode visual input, pyramidal cells and parvalbumin-expressing interneurons exhibit experience-dependent emergence of accurate behavioral representations during learning. The functional plasticity driving performance-predictive activity requires cell-autonomous expression of NMDA-type glutamate receptors. Our findings demonstrate that accurate encoding of behavioral output is not inherent to V1 but develops during learning to support visual task performance.

Cell-Type-Specific Outcome Representation in the Primary Motor Cortex.

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.

Mapping piezoelectric response in nanomaterials using a dedicated non-destructive scanning probe technique.

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.