Network Of Brain Regions Research Articles

Efficient Distributed Transfer Learning for Large‐Scale Gaussian Graphic Models

ABSTRACTA transfer learning for the large‐scale Gaussian graphical models (GGMs) is considered in a distributed architecture for fitting these target and auxiliary studies scattered across multiple sites. A distributed transfer learning algorithm, DisPower‐Trans‐CLIME, is proposed to learn the target GGMs by incorporating the data from similar and related auxiliary studies. The algorithm is communication efficient via the distributed power method for matrix decomposition. We show that DisPower‐Trans‐CLIME has a fast convergence rate comparable to the centred Trans‐CLIME algorithm. A debiased DisPower‐Trans‐CLIME is constructed and is proved to be element‐wise asymptotically normal for statistical inference. Thus, a multiple testing procedure is developed to detect edge of GGMs with false discovery rate (FDR) control. Extensive simulation experiments have been conducted to demonstrate superior numerical performance of our proposed learning algorithm on estimation and edge detection. It is also applied to infer the regional connection networks in brain regions of interest (ROIs) based on a target hospital site by leveraging the graph from multiple other hospital sites for autism spectrum disorder. We observe that the degrees of regional connectivity in the right brain are balanced, while the ones of the left brain region are extremely uneven because of the presence of multiple strong connections. However, the specific association, between the degrees of connectivity of these regions and ASD disease, is unknown.

Speech and neuroimaging effects following HiCommunication: a randomized controlled group intervention trial in Parkinson's disease.

Speech, voice and communication changes are common in Parkinson's disease. HiCommunication is a novel group intervention for speech and communication in Parkinson's disease based on principles driving neuroplasticity. In a randomized controlled trial, 95 participants with Parkinson's disease were allocated to HiCommunication or an active control intervention. Acoustic analysis was performed pre-, post- and six months after intervention. Intention-to-treat analyses with missing values imputed in linear multilevel models and complimentary per-protocol analyses were performed. The proportion of participants with a clinically relevant increase in the primary outcome measure of voice sound level was calculated. Resting-state functional MRI was performed pre- and post-intervention. Spectral dynamic causal modelling and the parametric empirical Bayes methods were applied to resting-state functional MRI data to describe effective connectivity changes in a speech-motor-related network of brain regions. From pre- to post-intervention, there were significant group-by-time interaction effects for the measures voice sound level in text reading (unstandardized b = 2.3, P = 0.003), voice sound level in monologue (unstandardized b = 2.1, P = 0.009), Acoustic Voice Quality Index (unstandardized b = -0.5, P = 0.016) and Harmonics-to-Noise Ratio (unstandardized b = 1.3, P = 0.014) post-intervention. For 59% of the participants, the increase in voice sound level after HiCommunication was clinically relevant. There were no sustained effects at the six-month follow-up. In the effective connectivity analysis, there was a significant decrease in inhibitory self-connectivity in the left supplementary motor area and increased connectivity from the right supplementary motor area to the left paracentral gyrus after HiCommunication compared to after the active control intervention. In conclusion, the HiCommunication intervention showed promising effects on voice sound level and voice quality in people with Parkinson's disease, motivating investigations of barriers and facilitators for implementation of the intervention in healthcare settings. Resting-state brain effective connectivity was altered following the intervention in areas implicated, possibly due to reorganization in brain networks.

Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected

HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to HIV uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.

Differential effects of bilateral hippocampal CA3 damage on the implicit learning and recognition of complex event sequences

ABSTRACT Learning regularities in the environment is a fundament of human cognition, which is supported by a network of brain regions that include the hippocampus. In two experiments, we assessed the effects of selective bilateral damage to human hippocampal subregion CA3, which was associated with autobiographical episodic amnesia extending ~50 years prior to the damage, on the ability to recognize complex, deterministic event sequences presented either in a spatial or a non-spatial configuration. In contrast to findings from related paradigms, modalities, and homologue species, hippocampal damage did not preclude recognition memory for an event sequence studied and tested at four spatial locations, whereas recognition memory for an event sequence presented at a single location was at chance. In two additional experiments, recognition memory for novel single-items was intact, whereas the ability to recognize novel single-items in a different location from that presented at study was at chance. The results are at variance with a general role of the hippocampus in the learning and recognition of complex event sequences based on non-adjacent spatial and temporal dependencies. We discuss the impact of the results on established theoretical accounts of the hippocampal contributions to implicit sequence learning and episodic memory.

Grid-like entorhinal representation of an abstract value space during prospective decision making

How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.

Osl-dynamics, a toolbox for modeling fast dynamic brain activity.

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.

Emotional Enhancement of Memory in Alzheimer’s Disease: A Systematic Review

AbstractBackgroundProgressive decline in episodic memory is a hallmark of Alzheimer’s disease (AD). Emotional arousal is known to enhance memory. The amygdala, locus coeruleus, hippocampus, and other regions, are key brain structures involved in the facilitation of memory through emotions. Amygdala‐mediated enhanced encoding of emotionally arousing stimuli or events may improve memory in AD patients, however whether this effect is present in patients with AD remains unclear.MethodA systematic search of the literature was conducted in accordance with PRISMA guidelines to identify relevant literature on emotional enhancement of memory (EEM) in AD. We utilized MEDLINE and PsycInfo. Search terms related to emotional memory and Alzheimer’s were searched as keywords and mapped to medical subject headings.Result1545 studies were screened; 40 were included. Participant age ranged from 64 to 84 years. Sample sizes ranged from 20 to 126. Only 11 of the 40 (27.5%) included brain MRI and evaluation of amygdala volume. 34 studies examined long‐term memory. Results showed that 12 out of 34 studies (35.3%) showed clear EEM for long‐term memory among participant with AD. A further 10 studies (29.4%) found that EEM was influenced by stimuli type, valence of emotion, level of encoding, or intensity of emotion. Yet 12 studies (35.3%) found no effect of EEM in AD. Of the 6 studies that specifically explored flashbulb memories in AD, 3 found no EEM and 3 observed that the effect was content‐dependent, such that personal details were remembered more often than factual details of events.ConclusionStudies which included brain imaging showed that amygdala volume is the strongest predictor of EEM in AD, regardless of level of cognitive impairment. The conflicting literature on EEM in AD reveals the importance of evaluating volumes or networks of brain regions known to be involved in emotional memory processing. AD patients included in these studies likely had varying degrees of atrophy in the amygdala, locus coeruleus, hippocampus and frontal regions involved in modulating emotional memories. However, without confirmation from brain imaging, the integrity of these brain structures cannot be established. Future studies are needed to confirm whether atrophy of important brain regions diminishes EEM in AD.

Aberrant degree centrality profiles during rumination in major depressive disorder.

Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.

Developmental alterations in the neural oscillatory dynamics underlying attentional reorienting

The neural and cognitive processes underlying the flexible allocation of attention undergo a protracted developmental course with changes occurring throughout adolescence. Despite documented age-related improvements in attentional reorienting throughout childhood and adolescence, the neural correlates underlying such changes in reorienting remain unclear. Herein, we used magnetoencephalography (MEG) to examine neural dynamics during a Posner attention-reorienting task in 80 healthy youth (6–14 years old). The MEG data were examined in the time-frequency domain and significant oscillatory responses were imaged in anatomical space. During the reorienting of attention, youth recruited a distributed network of regions in the fronto-parietal network, along with higher-order visual regions within the theta (3–7 Hz) and alpha-beta (10–24 Hz) spectral windows. Beyond the expected developmental improvements in behavioral performance, we found stronger theta oscillatory activity as a function of age across a network of prefrontal brain regions irrespective of condition, as well as more limited age- and validity-related effects for alpha-beta responses. Distinct brain-behavior associations between theta oscillations and attention-related symptomology were also uncovered across a network of brain regions. Taken together, these data are the first to demonstrate developmental effects in the spectrally-specific neural oscillations serving the flexible allocation of attention.

Mapping the evolution of regional brain network efficiency and its association with cognitive abilities during the first twenty-eight months of life

Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regional graph-theory features may provide unique insights into the development of cognitive abilities. Utilizing a large and longitudinal rsfMRI dataset from the UNC/UMN Baby Connectome Project, we investigated the developmental trajectories of regional efficiency and evaluated the relationships between these changes and cognitive abilities using Mullen Scales of Early Learning during the first twenty-eight months of life. Our results revealed a complex and spatiotemporally heterogeneous development pattern of regional global and local efficiency during this age period. Furthermore, we found that the trajectories of the regional global efficiency at the left temporal occipital fusiform and bilateral occipital fusiform gyri were positively associated with cognitive abilities, including visual reception, expressive language, receptive language, and early learning composite scores (P < 0.05, FDR corrected). However, these associations were weakened with age. These findings offered new insights into the regional developmental features of brain topologies and their associations with cognition and provided evidence of ongoing optimization of brain networks at both whole-brain and regional levels.

Extra-hippocampal contributions to pattern separation.

Pattern separation, or the process by which highly similar stimuli or experiences in memory are represented by non-overlapping neural ensembles, has typically been ascribed to processes supported by the hippocampus. Converging evidence from a wide range of studies, however, suggests that pattern separation is a multistage process supported by a network of brain regions. Based on this evidence, considered together with related findings from the interference resolution literature, we propose the 'cortico-hippocampal pattern separation' (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation. Particularly, these regions may contribute to pattern separation by (1) resolving interference in sensory regions that project to the hippocampus, thus regulating its cortical input, or (2) directly modulating hippocampal processes in accordance with task demands. Considering recent interest in how hippocampal operations are modulated by goal states likely represented and regulated by extra-hippocampal regions, we argue that pattern separation is similarly supported by neocortical-hippocampal interactions.

Modulation of Spectral Representation and Connectivity Patterns in Response to Visual Narrative in the Human Brain.

The regional brain networks and the underlying neurophysiological mechanisms subserving the cognition of visual narrative in humans have largely been studied with non-invasive brain recording. In this study, we specifically investigated how regional and cross-regional cortical activities support visual narrative interpretation using intracranial stereotactic electroencephalograms recordings from thirteen human subjects (6 females, and 7 males). Widely distributed recording sites across the brain were sampled while subjects were explicitly instructed to observe images from fables presented in “sequential” order, and a set of images drawn from multiple fables presented in “scrambled” order. Broadband activity mainly within the frontal and temporal lobes were found to encode if a presented image is part of a visual narrative (sequential) or random image set (scrambled). Moreover, the temporal lobe exhibits strong activation in response to visual narratives while the frontal lobe is more engaged when contextually novel stimuli are presented. We also investigated the dynamics of interregional interactions between visual narratives and contextually novel series of images. Interestingly, the interregional connectivity is also altered between sequential and scrambled sequences. Together, these results suggest that both changes in regional neuronal activity and cross-regional interactions subserve visual narrative and contextual novelty processing.

Structural brain network correlated with the resilience to traumatic events in the healthy participants: An MRI study on healthy people in a stricken area of the Great East Japan Earthquake.

Mental health care is an important issue in areas stricken by a natural disaster. Some people develop stress-related mental disorders while others are resilient to the traumatic events. In this study, we evaluated the relationships between resilience and structural neural networks derived from the gray matter MRI scan of the brain by using a novel similarity-based approach. Participants were 99 healthy subjects who underwent a 1.5-tesla MRI scan and the Connor-Davidson Resilience Scale (CD-RISC) test approximately 1 year on average after the Great East Japan Earthquake. We computed network metrics such as small world properties, the geometric characters of the whole-brain network and degree, betweenness centrality, and clustering coefficient, which were the regional geometric characters and mapped onto the image. Regarding small world properties, there were no significant correlations between the brain network indices and the CD-RISC total score. However, there was a significant negative correlation between the CD-RISC total score and the betweenness centrality in the right anterior cingulate cortex. No significant relationship was found for the CD-RISC total score with regional degree, clustering coefficient, or gray matter volumes. Our data suggest that the regional brain network at the right anterior cingulate cortex is associated with resilience in people who are exposed to a great natural disaster. The brain connectome may provide adjunct biological information to understand trauma- and stressor-related disorders. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response.

IntroductionTrauma-focused psychotherapy for post-traumatic stress disorder (PTSD) is effective in about half of all patients. Investigating biological systems related to prospective treatment response is important to gain insight in mechanisms predisposing patients for successful intervention. We studied if spontaneous brain activity, brain network characteristics and head motion during the resting state are associated with future treatment success.MethodsFunctional magnetic resonance imaging scans were acquired from 46 veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy (tf-CBT), eye movement desensitization and reprocessing (EMDR), or a combination thereof. After intervention, 24 patients were classified as treatment responders and 22 as treatment resistant. Differences between groups in spontaneous brain activity were evaluated using amplitude of low-frequency fluctuations (ALFF), while global and regional brain network characteristics were assessed using a minimum spanning tree (MST) approach. In addition, in-scanner head motion was assessed.ResultsNo differences in spontaneous brain activity and global network characteristics were observed between the responder and non-responder group. The right inferior parietal lobule, right putamen and left superior parietal lobule had a more central position in the network in the responder group compared to the non-responder group, while the right dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus and left inferior temporal gyrus had a less central position. In addition, responders showed less head motion.DiscussionThese results show that areas involved in executive functioning, attentional and action processes, learning, and visual-object processing, are related to prospective PTSD treatment response in veterans. In addition, these findings suggest that involuntary micromovements may be related to future treatment success.

Effects of Acupuncture at Neiguan in Neural Activity of Related Brain Regions: A Resting-State fMRI Study in Anxiety.

BackgroundAcupuncture of PC6 points has the effects of calming, tranquilizing, regulating qi, and relieving pain and has been clinically found to alleviate anxiety disorders. To explore the mechanism of improvement at the Neiguan point acupuncture in anxiety patients, we used fMRI to observe the changes in brain function in patients with immediate anxiety before and after acupuncture at the Neiguan point.Subjects and MethodsThe experiment followed the principle of randomized, single-blind design. Twenty-four anxiety volunteers (14 males and 10 females, 20–35 years old) were divided randomly into two groups: a group of acupuncture at Neiguan and a group of acupuncture at non-acupoint. Functional magnetic resonance imaging (fMRI) was applied to measure brain activity pre- and post-acupuncture. The amplitude of low-frequency fluctuations (ALFF) and seed-based functional connectivity (FC) was used to analyze the activity and network of brain regions. Statistical analysis was done using SPSS 21.0 and REST 1.8 software.ResultsALFF results revealed that post-acupuncture at Neiguan increased the activity of the left parahippocampal gyrus, fusiform gyrus, and right superior temporal gyrus and decreased the activity of the right middle frontal gyrus, right precuneus, and cuneus. Post-acupuncture at non-acupoint led to a significant ALFF increase in the thalamus and middle frontal gyrus. The ALFF in the left middle frontal gyrus was decreased. Functional connectivity in several anterior default mode network (DMN) regions and vermis cerebelli at left parahippocampal/fusiform gyri was increased, and connectivity in bilateral superior temporal gyri was decreased. FC with posterior DMN regions decreased at the right middle frontal gyrus, right precuneus, and cuneus.ConclusionOur study elucidates that acupuncture at Neiguan modulates anxiety by activating or deactivating these brain anxiety-related regions and provides potential explanations for the application of PC6 acupuncture in mental diseases.

Target deconvolution studies of (2R,6R)-hydroxynorketamine: an elusive search.

The off-label use of racemic ketamine and the FDA approval of (S)-ketamine are promising developments for the treatment of depression. Nevertheless, racemic ketamine and (S)-ketamine are controlled substances with known abuse potential and their use is associated with undesirable side effects. For these reasons, research efforts have focused on identifying alternatives. One candidate is (2R,6R)-hydroxynorketamine ((2R,6R)-HNK), a ketamine metabolite that in preclinical models lacks the dissociative and abuse properties of ketamine while retaining its antidepressant-like behavioral efficacy. (2R,6R)-HNK's mechanism of action however is unclear. The main goals of this study were to perform an in-depth pharmacological characterization of (2R,6R)-HNK at known ketamine targets, to use target deconvolution approaches to discover novel proteins that bind to (2R,6R)-HNK, and to characterize the biodistribution and behavioral effects of (2R,6R)-HNK across several procedures related to substance use disorder liability. We found that unlike (S)- or (R)-ketamine, (2R,6R)-HNK did not directly bind to any known or proposed ketamine targets. Extensive screening and target deconvolution experiments at thousands of human proteins did not identify any other direct (2R,6R)-HNK-protein interactions. Biodistribution studies using radiolabeled (2R,6R)-HNK revealed non-selective brain regional enrichment, and no specific binding in any organ other than the liver. (2R,6R)-HNK was inactive in conditioned place preference, open-field locomotor activity, and intravenous self-administration procedures. Despite these negative findings, (2R,6R)-HNK produced a reduction in immobility time in the forced swim test and a small but significant increase in metabolic activity across a network of brain regions, and this metabolic signature differed from the brain metabolic profile induced by ketamine enantiomers. In sum, our results indicate that (2R,6R)-HNK does not share pharmacological or behavioral profile similarities with ketamine or its enantiomers. However, it could still be possible that both ketamine and (2R,6R)-HNK exert antidepressant-like efficacy through a common and previously unidentified mechanism. Given its pharmacological profile, we predict that (2R,6R)-HNK will exhibit a favorable safety profile in clinical trials, and we must wait for clinical studies to determine its antidepressant efficacy.

How Does the Brain Help us Understand Others?

What do you think your friends are thinking when they get a compliment? How do they feel when they get a good grade at school? Thinking about other people and what they know, believe, or want is called social cognition. Certain parts of the brain are important for social cognition, and those parts work together in a network to allow us to think about others. How do we develop these social skills, starting as babies? In this article, we will introduce the parts of the brain that are important for social cognition, and we will explain how the network of brain regions that perform social cognition develops over the years, from a new-born baby to an adult.

Atomoxetine and citalopram alter brain network organisation in Parkinson’s disease

Recent evidence has shown potential cognitive benefits in Parkinson’s disease from restoring neuro- transmitter deficits including noradrenergic and serotonergic transmission. Here, we study global and regional brain network organization using task-free imaging, which minimizes performance confounds and the bias towards predetermined networks. Thirty-three patients with idiopathic Parkinson’s disease were studied three times in a double-blinded, placebo-controlled crossover design, following placebo, 40mg oral atomoxetine or 30mg oral citalopram. Seventy-six controls were scanned without medication to provide normative data. Relative to controls, patients on placebo had executive impairments, which was reflected in dysfunctional network dynamics in terms of reduced clustering coefficient, hub degree and hub centrality. In patients, atomoxetine improved fluency in proportion to plasma concentration (P=0.006, r2=0.24), and improved response inhibition in proportion to increased hub Eigen centrality (P=0.044, r2=0.14). Citalopram did not improve fluency or inhibitory control, but its influence on network integration and efficiency depended on disease severity: clustering (P=0.01, r2=0.22), modularity (P=0.043, r2=0.14) and path length (P=0.006, r2=0.25) increased in patients with milder forms of Parkinson’s disease, but decreased in patients with more advanced disease (UPDRSIII&gt;30). This study supports the use of task-free imaging of brain networks in translational pharmacology of neurodegenerative disorders. We propose that hub connectivity contributes to cognitive performance in Parkinson’s disease, and that noradrenergic treatment strategies can partially restore the neural systems supporting executive function.robinborchert@gmail.com

Modular Electrode Array for Multi-site Extracellular Recordings from Brains of Freely Moving Rodents.

Multi-site extracellular recordings from awake, freely moving rodents are an insightful technique that allows deduction of the dynamics of neural activity within a network of brain regions. Multiple advances in the design and materials of recording setups are available in the literature. However, most of these designs require several skill sets to assemble the electrodes and are expensive. Here, we explain in detail a custom design to build a multi-site (16 sites) electrode array (EA) and record extracellular electrical signals (local field potential and multi-unit spiking activity) at variable depths in freely behaving rodents. This EA weighs ∼3.0 g and costs less than $30. It provides mesoscopic neural activity maps (at millimeter scale) at low spatial resolution, thus enabling the experimenting group to further target specific regions with more expensive high-density probes at the resolution of an individual neuron. The article outlines the processes of building and implanting the array and recording neural activity during a behavior task. We also highlight the limitations of our design and the necessary steps to troubleshoot common issues faced during the initial implementation of the protocols. Finally, we explain the specific data one would obtain while using the probes during social interactions between rodents. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Preparation of the electrode array Basic Protocol 2: Surgical implantation of the electrode array Basic Protocol 3: Recording of neural activity from the electrode array while a mouse performs social investigation of a novel conspecific Basic Protocol 4: Histology and electrode registration.

A meta-analysis of the relation between hippocampal volume and memory ability in typically developing children and adolescents.

Memory is supported by a network of brain regions, with the hippocampus serving a critical role in this cognitive process. Previous meta‐analyses on the association between hippocampal structure and memory have largely focused on adults. Multiple studies have since suggested that hippocampal volume is related to memory performance in children and adolescents; however, the strength and direction of this relation varies across reports, and thus, remains unclear. To further understand this brain–behavior relation, we conducted a meta‐analysis to investigate the association between hippocampal volume (assessed as total volume) and memory during typical development. Across 25 studies and 61 memory outcomes with 1357 participants, results showed a small, but significant, positive association between total hippocampal volume and memory performance. Estimates of the variability across studies in the relation between total volume and memory were not explained by differences in memory task type (delayed vs. immediate; relational vs. nonrelational), participant age range, or the method of normalization of hippocampal volumes. Overall, findings suggest that larger total hippocampal volume relates to better memory performance in children and adolescents and that this relation is similar across the memory types and age ranges assessed. To facilitate enhanced generalization across studies in the future, we discuss considerations for the field moving forward.