Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Xiaoqing Lian, Pengsen Ma, Tengfeng Ma et al.

Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biological context, whereas high-content phenotypic profiling provides deep biological insights but is resource-intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure-based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement-invariant biological fingerprint from chemical structures and explicit filtering of experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero-shot settings with over 20% relative improvement over chemical baselines in mechanism-of-action (MOA) prediction. Furthermore, the framework achieves a 6-fold increase in hit rates for novel anti-cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian-xiao/DECODE.

Vitaly Vanchurin

We present a unified geometric framework for modeling learning dynamics in physical, biological, and machine learning systems. The theory reveals three fundamental regimes, each emerging from the power-law relationship $g \propto κ^α$ between the metric tensor $g$ in the space of trainable variables and the noise covariance matrix $κ$. The quantum regime corresponds to $α= 1$ and describes Schrödinger-like dynamics that emerges from a discrete shift symmetry. The efficient learning regime corresponds to $α= \tfrac{1}{2}$ and describes very fast machine learning algorithms. The equilibration regime corresponds to $α= 0$ and describes classical models of biological evolution. We argue that the emergence of the intermediate regime $α= \tfrac{1}{2}$ is a key mechanism underlying the emergence of biological complexity.

Devendra Patel, Aaditya Jain, Jayant Verma et al.

We present NDAI-NeuroMAP, the first neuroscience-domain-specific dense vector embedding model engineered for high-precision information retrieval tasks. Our methodology encompasses the curation of an extensive domain-specific training corpus comprising 500,000 carefully constructed triplets (query-positive-negative configurations), augmented with 250,000 neuroscience-specific definitional entries and 250,000 structured knowledge-graph triplets derived from authoritative neurological ontologies. We employ a sophisticated fine-tuning approach utilizing the FremyCompany/BioLORD-2023 foundation model, implementing a multi-objective optimization framework combining contrastive learning with triplet-based metric learning paradigms. Comprehensive evaluation on a held-out test dataset comprising approximately 24,000 neuroscience-specific queries demonstrates substantial performance improvements over state-of-the-art general-purpose and biomedical embedding models. These empirical findings underscore the critical importance of domain-specific embedding architectures for neuroscience-oriented RAG systems and related clinical natural language processing applications.

Takuma Sumi, Georgi S. Medvedev

Graph Signal Processing (GSP) extends classical signal processing to signals defined on graphs, enabling filtering, spectral analysis, and sampling of data generated by networks of various kinds. Graphon Signal Processing (GnSP) develops this framework further by employing the theory of graphons. Graphons are measurable functions on the unit square that represent graphs and limits of convergent graph sequences. The use of graphons provides stability of GSP methods to stochastic variability in network data and improves computational efficiency for very large networks. We use GnSP to address the stimulus identification problem (SIP) in computational and biological neural networks. The SIP is an inverse problem that aims to infer the unknown stimulus s from the observed network output f. We first validate the approach in spiking neural network simulations and then analyze calcium imaging recordings. Graphon-based spectral projections yield trial-invariant, lowdimensional embeddings that improve stimulus classification over Principal Component Analysis and discrete GSP baselines. The embeddings remain stable under variations in network stochasticity, providing robustness to different network sizes and noise levels. To the best of our knowledge, this is the first application of GnSP to biological neural networks, opening new avenues for graphon-based analysis in neuroscience.

Vivien W. Y. Li, Yuliang Wang, Yuliang Wang et al.

BackgroundThe purpose of this study is to determine the prevalence, risk factors, and characteristics of seizures and epilepsy in children with acquired brain injury (ABI), and compare their outcomes with children with ABI but no seizures.MethodBasic demographic data, clinical features, brain injury severity, seizure and epilepsy characteristics, and functional and neurodevelopmental outcomes of children with ABI with follow-up of at least 2 years were reviewed. Logistic regression was performed to determine the risk factors for seizures.ResultsThe study included 82 children with ABI due to tumors, trauma, hypoxia, stroke, infection, and neuro-inflammatory disorders. There were 43 (52%) boys. The median age at diagnosis was 2.9 years and median follow-up interval was 5 years. A total of 27 (33%) children experienced seizures and 20 (24%) were diagnosed as having epilepsy. Risk factors for seizures included cortical brain injury (p = 0.013) and central nervous system (CNS) infection (p = 0.001). Among those with seizures, seven had acute seizures within 7 days of ABI. The median time of onset of epilepsy after ABI was 2 years, and five children had refractory epilepsy (RE) needing more than two anti-epileptics. The hazard ratio (HR) for epilepsy in those with cortical brain injuries and CNS infections were 4.582 (95% CI [1.83, 11.49], p = 0.001) and 4.796 (95% CI [1.568, 14.67], p = 0.006), respectively. HR for epilepsy onset in those who had post-stroke seizures were 4.467, 95% CI [1.575, 12.67], p =0.005). Most subjects demonstrated significant improvements in Karnofsky Performance Scale (KPS) scores following rehabilitation (p < 0.0001); however, a greater proportion of children with post-ABI seizures required special educational services (p = 0.025).ConclusionCortical brain injuries, CNS infection and post-stroke seizures significantly increase the risk of epilepsy in children with ABI. While functional improvements were observed after rehabilitation, children with post-ABI seizures more often required special educational support. The identification of risk factors for seizures, time to epilepsy onset, and the functional outcomes can guide subsequent management and counseling.

Semen Goreninskii, Ulyana Chernova, Elisaveta Prosetskaya et al.

Fabrication of tissue engineering scaffolds with tailored physicochemical and biological characteristics is a relevant task in biomedical engineering. The present work was focused at the evaluation of the effect of fabrication approach (single-channel or multi-channel electrospinning) on the properties of the fabricated poly(lactic acid)(PLA)/poly(epsilon-caprolactone)(PCL) scaffolds with various polymer mass ratios (1/0, 2/1, 1/1, 1/2, and 0/1). The scaffolds with same morphology (regardless of electrospinning variant) were fabricated and characterized using SEM, water contact angle measurement, FTIR, XRD, tensile testing and in vitro experiment with multipotent mesenchymal stem cells. It was demonstrated, that multi-channel electrospinning prevents intermolecular interactions between the polymer components of the scaffold, preserving their crystal structure, what affects the mechanical characteristics of the scaffold (particularly, leads to 2-fold difference in elongation). Better adhesion of multipotent mesenchymal stem cells on the surface of the scaffolds fabricated using multichannel electrospinning was demonstrated.

Daniel Cortés-Denia, Manuel Pulido-Martos, Janine Bosak et al.

Background Several studies have examined the impact of leadership on employee well-being and health. However, this research has focused on a variable-centred approach. By contrast, the present study adopts a person-centred approach. Aims To (a) identify latent ‘resources’ profiles among two samples combining vigour at work, work engagement and physical activity levels; (b) examine the link between the identified profiles and indicators of psychological/physical health; and (c) test whether different levels of transformational leadership determine the probability of belonging to a particular profile. Method Two samples of workers, S1 and S2 (NS1 = 354; NS2 = 158), completed a cross-sectional survey before their annual medical examination. Results For S1, the results of latent profile analysis yielded three profiles: spiritless, spirited and high-spirited. Both high-spirited and spirited profiles showed a positive relationship with mental health, whereas spiritless showed a negative relationship. For S2, two profiles (spirited and spiritless) were replicated, with similar effects on mental health, but none of them was related to total cholesterol. In both samples, transformational leadership determined the probability of belonging to a particular profile. Conclusions Transformational leadership increased the probability of belonging to a more positive profile and, therefore, to better workers’ health.

Jordan C. Rozum, Colin Campbell, Eli Newby et al.

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kinetic parameters for biochemical reactions). Boolean networks have emerged as a powerful tool for modeling these systems. We provide a methodological overview of Boolean network models of biological systems. After a brief introduction, we describe the process of building, analyzing, and validating a Boolean model. We then present the use of the model to make predictions about the system's response to perturbations and about how to control (or at least influence) its behavior. We emphasize the interplay between structural and dynamical properties of Boolean networks and illustrate them in three case studies from disparate levels of biological organization.

Mayalen Etcheverry, Michael Levin, Clément Moulin-Frier et al.

Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes. Biological networks like gene regulatory networks and protein pathways are key drivers of embryogenesis and physiological processes. Comprehending their diverse behaviors is essential for tackling diseases, including cancer, as well as for engineering novel biological constructs. Despite the availability of extensive mathematical models represented in Systems Biology Markup Language (SBML), researchers face significant challenges in exploring the full spectrum of behaviors and optimizing interventions to efficiently shape those behaviors. Existing tools designed for simulation of biological network models are not tailored to facilitate interventions on network dynamics nor to facilitate automated discovery. Leveraging recent developments in machine learning (ML), this paper introduces SBMLtoODEjax, a lightweight library designed to seamlessly integrate SBML models with ML-supported pipelines, powered by JAX. SBMLtoODEjax facilitates the reuse and customization of SBML-based models, harnessing JAX's capabilities for efficient parallel simulations and optimization, with the aim to accelerate research in biological network analysis.

Horacio G. Rotstein, Fidel Santamaria

Neuroscience is undergoing dramatic progress because of the vast data streams derived from the new technologies product of the BRAIN initiative and other enterprises. As any other scientific field, neuroscience benefits from having clear definitions of its theoretical components and their interactions. This allows generating theories that integrate knowledge, provide mechanistic insights, and predict results under new experimental conditions. However, theoretical neuroscience is a heterogeneous field that has not yet agreed on how to build theories or whether it is desirable to have an overarching theory or whether theories are simply tools to understand the brain. Here we advocate for the need of developing theoretical frameworks as a basis of generating common theoretical structures. We enumerate the elements of theoretical frameworks we deem necessary for any theory in neuroscience. In particular, we address the notions of paradigms, models, and scales of organizations. We then identify areas with pressing needs to develop brain theories: integration of statistical and dynamic approaches; multi-scale integration; coding; and interpretability in the context of Artificial Intelligence. We also point out that future theoretical frameworks would benefit from the incorporation of the principles of Evolution as a fundamental structure rather than purely mathematical or engineering principles. Rather than providing definite answers, the objective of this paper is to serve as an initial and succinct presentation of these topics to encourage discussion and further in depth development of each topic.

Shiyuan Li

Spike-timing dependent plasticity in biological neural networks has been proven to be important during biological learning process. On the other hand, artificial neural networks use a different way to learn, such as Back-Propagation or Contrastive Hebbian Learning. In this work we introduce approximate STDP, a new neural networks learning framework more similar to the biological learning process. It uses only STDP rules for supervised and unsupervised learning, every neuron distributed learn patterns and don' t need a global loss or other supervised information. We also use a numerical way to approximate the derivatives of each neuron in order to better use SDTP learning and use the derivatives to set a target for neurons to accelerate training and testing process. The framework can make predictions or generate patterns in one model without additional configuration. Finally, we verified our framework on MNIST dataset for classification and generation tasks.

Rony Cleper, Nimrod Hertz-Palmor, Nimrod Hertz-Palmor et al.

ObjectiveTo identify COVID-19 work-related stressors and experiences associated with sleep difficulties in HCW, and to assess the role of depression and traumatic stress in this association.MethodsA cross-sectional study of HCW using self-report questionnaires, during the first peak of the pandemic in Israel (April 2020), conducted in a large tertiary medical center in Israel. Study population included 189 physicians and nurses working in designated COVID-19 wards and a comparison group of 643 HCW. Mean age of the total sample was 41.7 ± 11.1, 67% were female, 42.1% physicians, with overall mean number of years of professional experience 14.2 ± 20. The exposure was working in COVID-19 wards and related specific stressors and negative experiences. Primary outcome measurement was the Insomnia Severity Index (ISI). Secondary outcomes included the Primary Care-Post Traumatic Stress Disorder Screen (PC-PTSD-5); the Patient Health Questionnaire-9 (PHQ-9) for depression; the anxiety module of the Patient-Reported Outcomes Measurement Information System (PROMIS); Pandemic-Related Stress Factors (PRSF) and witnessing patient suffering and death.ResultsCompared with non-COVID-19 HCW, COVID-19 HCW were more likely to be male (41.3% vs. 30.7%) and younger (36.91 ± 8.81 vs. 43.14 ± 11.35 years). COVID-19 HCW reported higher prevalence of sleep difficulties: 63% vs. 50.7% in the non-COVID group (OR 1.62, 95% CI 1.15–2.29, p = 0.006), mostly difficulty maintaining sleep: 26.5% vs. 18.5% (OR 1.65, 95% CI 1.11–2.44, p = 0.012). Negative COVID-19 work-related experiences, specifically witnessing patient physical suffering and death, partially explained the association. Although past psychological problems and current depression and PTSD were associated with difficulty maintaining sleep, the main association remained robust also after controlling for those conditions in the full model.Conclusion and RelevanceCOVID-19 frontline HCW were more likely to report sleep difficulties, mainly difficulty maintaining sleep, as compared with non-COVID-19 HCW working at the same hospital. Negative patient-care related experiences likely mediated the increased probability for those difficulties. Future research is needed to elucidate the long-term trajectories of sleep difficulties among HCW during large scale outbreaks, and to identify risk factors for their persistence.

Ramkripa Raghavan, Neha S. Anand, Guoying Wang et al.

Abstract Alterations in tryptophan and serotonin have been implicated in various mental disorders; but studies are limited on child neurodevelopmental disabilities such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). This prospective cohort study examined the associations between levels of tryptophan and select metabolites (5-methoxytryptophol (5-MTX), 5-hydroxytryptophan (5-HTP), serotonin, N-acetyltrytophan) in cord plasma (collected at birth) and physician-diagnosed ASD, ADHD and other developmental disabilities (DD) in childhood. The study sample (n = 996) derived from the Boston Birth Cohort, which included 326 neurotypical children, 87 ASD, 269 ADHD, and 314 other DD children (mutually exclusive). These participants were enrolled at birth and followed-up prospectively (from October 1, 1998 to June 30, 2018) at the Boston Medical Center. Higher levels of cord 5-MTX was associated with a lower risk of ASD (aOR: 0.56, 95% CI: 0.41, 0.77) and ADHD (aOR: 0.79, 95% CI: 0.65, 0.96) per Z-score increase, after adjusting for potential confounders. Similarly, children with cord 5-MTX ≥ 25th percentile (vs. <25th percentile) had a reduction in ASD (aOR: 0.27, 95% CI: 0.14, 0.49) and ADHD risks (aOR: 0.45, 95% CI: 0.29, 0.70). In contrast, higher levels of cord tryptophan, 5-HTP and N-acetyltryptophan were associated with higher risk of ADHD, with aOR: 1.25, 95% CI: 1.03, 1.51; aOR: 1.32, 95% CI: 1.08, 1.61; and aOR: 1.27, 95% CI: 1.05, 1.53, respectively, but not with ASD and other DD. Cord serotonin was not associated with ASD, ADHD, and other DD. Most findings remained statistically significant in the sensitivity and subgroup analyses.

Lingyan Yu, Rebecca Kazinka, Danielle Pratt et al.

Persecutory ideations are self-referential delusions of being the target of malevolence despite a lack of evidence. Wisner et al. (2021) found that reduced connectivity between the left frontoparietal (lFP) network and parts of the orbitofrontal cortex (OFC) correlated with increased persecutory behaviors among psychotic patients performing in an economic social decision-making task that can measure the anticipation of a partner’s spiteful behavior. If this pattern could be observed in the resting state, it would suggest a functional-structural prior predisposing individuals to persecutory ideation. Forty-four patients in the early course of a psychotic disorder provided data for resting-state functional connectivity magnetic resonance imaging across nine brain networks that included the FP network and a similar OFC region. As predicted, we found a significant and negative correlation between the lFP–OFC at rest and the level of suspicious mistrust on the decision-making task using a within-group correlational design. Additionally, self-reported persecutory ideation correlated significantly with the connectivity between the right frontoparietal (rFP) network and the OFC. We extended the previous finding of reduced connectivity between the lFP network and the OFC in psychosis patients to the resting state, and observed a possible hemispheric difference, such that greater rFP–OFC connectivity predicted elevated self-reported persecutory ideation, suggesting potential differences between the lFP and rFP roles in persecutory social interactions.

Minakshi Nimesh Parikh, Kartik Srinivasa Valipay, Mehul Brahmbhatt et al.

Introduction: The global scale of COVID-19 has been enormous, with the disease affecting 20 million people worldwide and resulting in 751,154 deaths by August 14, 2020. An increase in mental health problems is expected with an event of such scale, given past experience with epidemics such as severe acute respiratory syndrome and Ebola, among various vulnerable populations. One such population may be the family members of patients affected with COVID-19. Methods: This was a cross-sectional study. Five hundred and forty-one relatives of patients admitted in the COVID-19 wing of a tertiary care hospital were studied. Sociodemographic details were recorded and a Gujarati version of General Health Questionnaire-28 (GHQ-28) was applied. A total score of ≥4 on GHQ-28 was considered indicative of “caseness” or psychiatric morbidity and the population was divided into two groups based on whether the score was <4 or ≥4. The groups were analyzed for any differences with respect to variables like age, gender. Conclusion: 5.17% of the study population had a GHQ-28 total score of ≥4 indicative of “psychiatric morbidity.” The most common symptoms were fatigue, stress, sleep disturbance, and anxiety. Male gender and advanced age were statistically significantly more likely to have a GHQ-28 total score ≥4.

Jae‐Han Bae, Jeong‐Min Kim, Kwang‐Yeol Park et al.

Abstract Objective We investigated the effect of arterial stiffness on the severity of enlarged perivascular spaces (EPVSs) and cerebral microbleeds (CMBs) at different brain locations. Methods A total of 854 stroke patients underwent both brachial‐ankle pulse wave velocity (baPWV) measurement and brain MRI. The extent of EPVS was separately rated at the levels of the basal ganglia (BG) and centrum semiovale (CS). The CMBs were categorized as strictly lobar CMB and deep CMB. The patients were categorized according to baPWV quartiles, and multivariable logistic regressions were performed to evaluate whether the baPWV increment was independently associated with each cerebral SVD marker at different locations. The odds ratio (OR) with 95% confidence interval (CI) was derived on the reference of the first quartile. Results Severe EPVSs at BG and CS were detected in 243 (28.5%) and 353 patients (41.3%), respectively. The increment of baPWV quartiles was associated with both severe BG EPVS burden (Q4: OR = 2.58, CI = 1.45–4.60) and severe CS EPVS burden (Q4: OR = 2.06, CI = 1.24–3.42). Deep CMBs were found in 259 patients (30.3%), and strictly lobar CMBs were found in 170 patients (19.9%). Multivariable logistic regression model revealed deep CMB was independently associated with the baPWV increment (Q4: OR = 2.52, CI = 1.62–3.94). However, strictly lobar CMB had a neutral relationship with baPWV. Conclusion Increased arterial stiffness is consistently associated with the presence of deep CMB and severe EPVS burden at the BG and CS, suggesting a common pathophysiologic mechanism.

Patrick Krauss, Achim Schilling

In order to gain a mechanistic understanding of how tinnitus emerges in the brain, we must build biologically plausible computational models that mimic both tinnitus development and perception, and test the tentative models with brain and behavioral experiments. With a special focus on tinnitus research, we review recent work at the intersection of artificial intelligence, psychology and neuroscience, indicating a new research agenda that follows the idea that experiments will yield theoretical insight only when employed to test brain-computational models. This view challenges the popular belief, that tinnitus research is primarily data limited, and that producing large, multi-modal, and complex datasets, analyzed with advanced data analysis algorithms, will finally lead to fundamental insights into how tinnitus emerges. However, there is converging evidence that although modern technologies allow assessing neural activity in unprecedentedly rich ways in both, animals and humans, empirical testing one verbally defined hypothesis about tinnitus after another, will never lead to a mechanistic understanding. Instead, hypothesis testing needs to be complemented with the construction of computational models that generate verifiable predictions. We argue, that even though, contemporary artificial intelligence and machine learning approaches largely lack biological plausibility, the models to be constructed will have to draw on concepts from these fields, since they have already proven to do well in modeling brain function. Nevertheless, biological fidelity will have to be increased successively, leading to ever better and fine-grained models, allowing at the end for even testing possible treatment strategies in silico, before application in animal or patient studies.

Xiao-Mei Li, Zhan-Ming Shi, Hua Hu et al.

Background The use of ketamine in electroconvulsive therapy (ECT) has been examined in the treatment of major depressive disorder (MDD); however, there has been no systematic review and meta-analysis of related randomised controlled trials (RCTs).Aim To examine the efficacy and safety of ketamine augmentation of ECT in MDD treatment.Methods Two reviewers searched Chinese (China National Knowledge Infrastructure and Wanfang) and English (PubMed, PsycINFO, Embase and Cochrane Library) databases from their inception to 23 July 2019. The included studies' bias risk was evaluated using the Cochrane risk of bias assessment tool. The primary outcome of this meta-analysis was improved depressive symptoms at day 1 after a single ECT treatment session. Data were pooled to calculate the standardised mean difference and risk ratio with their 95% CIs using RevMan V.5.3. We used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach to assess the whole quality of evidence.Results Four RCTs (n = 239) compared ketamine alone or ketamine plus propofol (n = 149) versus propofol alone (n = 90) in patients with MDD who underwent a single ECT session. Three RCTs were considered as unclear risk with respect to random sequence generation using the Cochrane risk of bias. Compared with propofol alone, ketamine alone and the combination of ketamine and propofol had greater efficacy in the treatment of depressive symptoms at days 1, 3 and 7 after a single ECT session. Moreover, compared with propofol alone, ketamine alone and the combination of ketamine and propofol were significantly associated with increased seizure duration and seizure energy index. Compared with propofol, ketamine alone was significantly associated with increased opening-eye time. Based on the GRADE approach, the evidence level of primary and secondary outcomes ranged from very low (26.7%, 4/15) to ‘low’ (73.3%, 11/15).Conclusion Compared with propofol, there were very low or low evidence levels showing that ketamine alone and the combination of ketamine and propofol appeared to rapidly improve depressive symptoms of patients with MDD undergoing a single ECT session. There is a need for high-quality RCTs.

George Cevora

Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful. In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI.

J. Jarcho

Early childhood adversity linked to parenting is associated with an increased lifetime risk for numerous mental health disorders (1,2). Over the last decade, seminal longitudinal research has demonstrated that lasting effects of these adverse childhood experiences on brain function can diminish or exacerbate this risk (3–5). In this issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Callaghan et al. (6) begin to answer a critical question: how do early caregiving experiences “get under the skin” to exert long-term effects on the relation between brain function and symptoms of psychopathology? In this case, the authors focus on anxiety. To do so, they build on established rodent literature demonstrating that when exposed to stress in the presence of a parental cue, young pups that have experienced high-quality parenting relative to low-quality parenting exhibit diminished behavioral signs of distress, decreased stress hormone release, and blunted amygdala response (7). Critically, this parental “buffering” effect on amygdala function is absent in older pups (8). Thus, childhood may be a sensitive period during which the effects of highquality parenting in infancy shape lasting neurobiological responses to stressors, and whereby the absence of such shaping caused by low-quality parenting may contribute to heightened amygdala reactivity often associated with anxietylike behavior. In children, Callaghan et al. (6) describe these relations as a neuroenvironmental loop and hypothesize that early parental care, brain development, and behavior interact to scaffold the maturation of emotion regulation circuitry. Callaghan et al. (6) made important inroads in testing this hypothesis by pairing neuroimaging with the longitudinal assessment of symptoms of anxiety across 3 years in a unique sample of youths (n = 102) who experienced early childhood adversity linked to caregiving (adoption after previous institutionalization [PI]) and those who had been reared from birth by their biological parents. During either childhood or adolescence, youths underwent functional magnetic resonance imaging while viewing photographs of their own parent or a stranger. Buffering was defined as a decrease in right amygdala response to viewing one’s own parent relative to a stranger. Overall, children without early childhood adversity exhibited diminished amygdala responses, whereas PI children and adolescents from both rearing groups did not. However, a substantial number of youths in both rearing groups exhibited decreased amygdala reactivity. In a test of their neuroenvironmental loop hypothesis, Callaghan et al. (6) then demonstrated that early childhood adversity interacted with brain function to influence the expression of anxiety symptoms