Diagnostic performance of central vein sign versus oligoclonal bands for multiple sclerosis.

BACKGROUND: Cerebrospinal fluid (CSF) oligoclonal bands (OCB) are a diagnostic biomarker in multiple sclerosis (MS). The central vein sign (CVS) is an imaging biomarker for MS that may improve diagnostic accuracy. OBJECTIVES: The objective of the study is to examine the diagnostic performance of simplified CVS methods in comparison to OCB in participants with clinical or radiological suspicion for MS. METHODS: Participants from the CentrAl Vein Sign in MS (CAVS-MS) pilot study with CSF testing were included. Select-3 and Select-6 (counting up to three or six CVS+ lesions per scan) were rated on post-gadolinium FLAIR* images. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value for Select-3, Select-6, OCB, and combinations thereof were calculated for MS diagnosis at baseline and at 12 months. RESULTS: Of 53 participants, 25 were OCB+. At baseline, sensitivity for MS diagnosis was 0.75 for OCB, 0.83 for Select-3, and 0.71 for Select-6. Specificity for MS diagnosis was 0.76 for OCB, 0.48 for Select-3, and 0.86 for Select-6. At 12 months, PPV for MS diagnosis was 0.95 for Select-6 and 1.00 for Select-6 with OCB+ status. DISCUSSION: Results suggest similar diagnostic performance of simplified CVS methods and OCB. Ongoing studies will refine whether CVS could be used in replacement or in conjunction with OCB.

Psychometric Analysis of the Hip Disability and Osteoarthritis Outcome Score (HOOS).

Hip Disability and Osteoarthritis Outcome Survey (HOOS) was developed as a region- and disease-specific outcome to assess hip disability. Despite the use of the HOOS in clinical practice and research, psychometric analyses of the scale in a large dataset of patients have not been performed. As such, the purposes of this study were to assess the structural validity of the HOOS in patients who underwent a total hip arthroplasty. Data were obtained from the Surgical Outcome System (SOS) global registry. Confirmatory factor analysis (CFA) was conducted to assess the scale structure of the 40-item HOOS and exploratory factor analysis (EFA) was conducted to identify a parsimonious scale structure. The parsimonious model identified was subjected to multi-group and longitudinal invariance testing and LGC modeling. The original five-factor, 40-item HOOS did not meet recommended model fit indices values (CFI = 0.822, TLI = 0.809, IFI = 0.822, RMSEA = 0.085). Alternate model generation identified an alternative model (i.e., HOOS-9). Sound model fit was identified for the HOOS-9 (CFI = 0.974, TLI = 0.961, RMSEA = 0.046). Invariance testing criteria were also met between groups (i.e., age and sex) and across time. Lastly, a nonlinear growth trajectory was identified in responses pertaining to hip disability. The original scale structure of the 40-item HOOS was not supported. The HOOS-9 met contemporary model fit recommendations, along with multi-group and longitudinal invariance testing. Our findings support the preliminary use of the HOOS-9 to assess hip function and disability in research and clinical practice.

Structure-function coupling in macroscale human brain networks.

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

Exploring the Relationship between Self-Compassion and Psychological Pain: A Canonical Correlation Analysis.

This study investigated the association between self-compassion and psychological pain across various demographic variables. Using canonical correlation analysis, we observed an inverse relationship between the combined factors of the Self-Compassion Scale (SCS) and the Orbach and Mikulincer Mental Pain Scale (OMMP-8). Subgroup analyses revealed differences in SCS subscales among demographic groups with females, individuals with mental health diagnoses, and non-athletes displaying higher scores on negative SCS subscales and PsyPn. Injury status did not significantly affect self-compassion levels, although injured individuals scored higher on the irreversibility subscale of PsyPn. Negative SCS factors exhibited larger group differences and stronger correlations with PsyPn, indicating the potency of negative thinking in influencing psychological pain. These findings underscore the importance of self-compassion in mental health and suggest potential implications for intervention strategies.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

ComBatLS: A location- and scale-preserving method for multi-site image harmonization.

Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Instrument-Assisted Soft Tissue Mobilization 2-Handed Grip Force Production Consistency During Simulated Treatment: A Technical Report.

CONTEXT: Instrument-assisted soft tissue mobilization (IASTM) continues to increase in popularity and utilization among manual therapists. Despite its popularity, little is known about the consistency in peak or average forces that clinicians apply when performing IASTM treatments with a 2-handed grip. The purpose of this study was to examine intraclinician consistency in peak and average forces when applying a 2-handed IASTM grip. DESIGN: Randomized crossover study conducted in a university biomechanics laboratory. METHODS: Five (5) licensed athletic trainers with prior IASTM training used 5 different IASTM instruments to apply simulated treatment. Average peak forces (Fpeak) and average mean forces (Fmean) were collected via force plate for all 5 IASTM instruments with a skin simulant attached. Descriptive statistics, coefficients of variation (CVs), box and density plots, and Bland-Altman plots were assessed. RESULTS: The clinicians' average Fpeak ranged from 3.0 N to 11.6 N and average Fmean from 1.9 N to 8.1 N. Fpeak CVs for all instruments ranged from 14% to 31%, and Fmean CVs ranged from 15% to 35%. Bland-Altman plots indicated that for both Fpeak and Fmean, 97% of the data points fell within the limits of agreement across instruments and clinicians. Mean differences across instruments ranged from 0.9 N (91.8 g) to 4.1 N (418.1 g) for Fpeak and from 1.0 N (102.0 g) to 2.8 N (285.5 g) for Fmean. Thus, CVs, box and density plots, and Bland-Altman plots supported general force application consistency. CONCLUSION: Trained IASTM clinicians produced consistent treatment application forces (ie, Fpeak and Fmean) within treatment sessions during 2-handed simulated application.

Psilocybin desynchronizes the human brain.

A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

Protocol for a meta-analysis of stereotype threat in African Americans.

Stereotype threat is a well-known construct in psychology wherein individuals who belong to a negatively stereotyped demographic group underperform on cognitive or academic tasks due to the detrimental effects of a stereotype. Many psychologists have suggested that stereotype threat may be one of the reasons that some demographic groups are underrepresented in advanced academic programs and STEM fields. However, others have raised concerns about the quality of the stereotype threat research, suggesting that its apparent effects are inflated and that the phenomenon may be an illusion of questionable research practices and publication bias. The purpose of this proposed meta-analysis is to evaluate the existence of stereotype threat by (1) identifying the average effect size of stereotype threat studies in different types of studies, (2), investigating whether publication bias and p-hacking are present in the empirical research on stereotype threat, (3) testing for the influence of theoretical and methodological moderators, (4) assessing the overall quality of the research on stereotype threat, (5) and identifying the average effect in the methodologically strongest studies. This meta-analysis will be limited to studies that report data from African Americans because this population is a theoretically important group in stereotype threat research, and the size of score gaps between the African American and non-stereotyped populations in the United States should make the stereotype threat effect easiest to detect.

Neural Responses to Auditory Food Stimuli Following Cognitive Behavioral Therapy for Binge-Eating Disorder.

OBJECTIVE: Adults with binge-eating disorder (BED), compared with those without BED, demonstrate higher blood-oxygen-level-dependent (BOLD) response to food cues in reward-related regions of the brain. It is not known whether cognitive behavioral therapy (CBT) can reverse this reward system hyperactivation. This randomized controlled trial (RCT) assessed changes in BOLD response to binge-eating cues following CBT versus wait-list control (WLC). METHOD: Females with BED (N = 40) were randomized to CBT or WLC. Participants completed assessments at baseline and 16 weeks including measures of eating and appetite and functional magnetic resonance imaging (fMRI) to measure BOLD response while listening to personalized scripts of binge-eating and neutral-relaxing cues. Data were analyzed using general linear models with mixed effects. RESULTS: Overall retention rate was 87.5%. CBT achieved significantly greater reductions in binge-eating episodes than WLC (mean ± standard error decline of 14.6 ± 2.7 vs. 5.7 ± 2.8 episodes in the past 28 days, respectively; p = 0.03). CBT and WLC did not differ significantly in changes in neural responses to binge-eating stimuli during the fMRI sessions. Compared with WLC, CBT had significantly greater improvements in reward-based eating drive, disinhibition, and hunger as assessed by questionnaires (ps < 0.05). DISCUSSION: CBT was effective in reducing binge eating, but, contrary to our hypothesis, CBT did not improve BOLD response to auditory binge-eating stimuli in reward regions of the brain. Further studies are needed to assess mechanisms underlying improvements with CBT for BED. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03604172.

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

Presumed Monozygotic Twin Kidney Transplantation with a Thin Basement Membrane Nephropathy Donor: A Case Report.

Identical or Monozygotic twin kidney transplant usually possess an excellent immunological match and provide the opportunity to minimize or even avoid immunosuppression toxicity. However, there are concerns regarding disease recurrence among end stage kidney disease (ESKD) patients with an unknown etiology. Together with the risk of inherent, familial disease affecting donors and recipients alike, more invasive tests such as a pretransplant biopsy are being considered to ascertain renal prognosis. A 30-year-old female, known case of CKD Stage 5D from an unknown etiology, with secondary hyperparathyroidism and heart failure, presented at our OPD for kidney transplantation. Her donor is her identical twin who is asymptomatic and denies comorbidities. The recipient discloses a previous history of blood transfusion. Immunological workup revealed the following: matched blood type, zero HLA mismatch, negative T-cell tissue crossmatch but with a positive Class I HLA antigen screen. Antibody specificity revealed the presence of donor specific antibodies (DSA). After workup completion, the patient underwent a right kidney transplant with a preimplantation wedge biopsy on the donor kidney. Immediate graft function was noted post operatively. The wedge biopsy revealed a thinned glomerular basement membrane, consistent with Thin Basement Membrane Nephropathy (TBMN). The patient was started on immunosuppression and prophylaxis during the duration of the post operative period without any complications. Five months post-transplant, both the recipient and donor maintain an adequate renal function without any signs of allograft rejection. In this case report, we have demonstrated that TBMN may serve as a viable donor for a presumed monozygous twin kidney transplantation. When a live donor with TBMN is being considered, a thorough work-up and identification of high-risk features are essential to exclude other progressive renal diseases during the pretransplant evaluation.

Minimal Change Disease Associated with SARS-CoV-2 (COVID-19) Infection among Adult Filipinos: A Report of Two Cases and Review of Related Literature.

A 40-year-old Filipino female with a history of right total mastectomy for a low-grade phyllodes tumor was admitted due to stillbirth. Her laboratory results revealed an incidental finding of a positive COVID-19 RT-PCR swab, serum creatinine 1.04 mg/dL, urine RBC 1/HPF, and a 24-hour urine protein of 9.22 grams with hypoalbuminemia and dyslipidemia. Serologic workup was noted to be negative. A kidney biopsy was performed which demonstrated unremarkable light microscopy (LM) and immunofluorescence (IF) with widespread podocyte-foot process effacement, consistent with minimal change disease. She was started on prednisone (1 mg/kg/day) and achieved complete remission after six weeks. A 61-year-old Filipino male with a history of Type 2 Diabetes Mellitus, Hypertension, Dyslipidemia, and mild COVID-19 infection four months prior, now presented with diarrhea. On admission, his COVID-19 RT-PCR swab revealed a reinfection. Workup demonstrated a serum creatinine 3.39 mg/dL, urine RBC 2/HPF, and urine ACR 2.6 g/g. Serologic tests were negative. He was diagnosed with Nephrotic Syndrome and underwent kidney biopsy. Findings showed an unremarkable LM and IF with widespread podocyte-foot process effacement, consistent with minimal change disease. He was started on prednisone (1 mg/kg/day) and achieved complete remission after eight weeks. SARS-CoV-2 (COVID-19) may present with a variety of kidney involvement which includes glomerulopathies such as MCD. An accurate diagnosis using the patient's clinical presentation, renal histopathology, and adjunct laboratory examinations, is essential to direct effective management and good outcomes.

The potential of virtual fencing technology to facilitate sustainable livestock grazing management.

Virtual fencing (VF) technology is gaining interest due to its potential to facilitate sustainable grazing management. It allows farmers to contain grazing livestock without physical fences, thereby reducing the time and labour associated with the implementation of conventional fences. From a conservation perspective, some sensitive areas within uplands should not be grazed during certain periods of the year, and VF provides an invisible and moveable fence line that can exclude livestock from these areas. However, there are also concerns associated with its use, including animal welfare impacts, cost-effectiveness, and public perception. The extent to which VF can contribute to make livestock systems more sustainable remains to be investigated. To address this gap, this study investigates the potential of VF to promote sustainable grazing management using the Efficiency, Substitution, and Redesign framework, which has been used for the first time in this context. The framework is particularly relevant in taking an active and normative approach to identify key aspects to focus on to help achieve sustainability. We consulted stakeholders including farmers, wildlife inspectors, veterinarians, policy officers, researchers, NGOs, farm advisors or certification managers, through focus groups (N = 4) and in-depth, semi-structured interviews (N = 5). Stakeholders have highlighted the potential of VF to provide new opportunities to increase the efficiency and sustainability of livestock grazing systems, enabling their redesign, and contributing to improved environmental and animal welfare outcomes, as well as higher financial and social performance. However, there are important aspects that remain to be addressed to achieve such redesign, including issues of reliability due to poor network signal, animals' ability to learn, biosecurity and safety issues related to the absence of physical fences, farm suitability and farmers' ability to use the systems effectively. This study highlights the need to ensure that the development and uptake of VF are mutually beneficial to farmers, animals, and the wider farming industry. This includes a highlight on the importance of participative approaches to involve key stakeholders to address concerns and maximise the potential of the technology.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

Harmonization of Longitudinal Diffusion Tensor Imaging Data of the Pediatric Cervical and Thoracic Spinal Cord at 3T Using Longitudinal ComBat.

Diffusion tensor imaging (DTI) of the spinal cord has been extensively used to identify biomarkers for spinal cord pathology. Previously, the longitudinal ComBat (longComBat) technique was examined to reduce scanner effects in multi-site, multi-scanner spinal cord DTI data. This study aimed to assess its effectiveness on longitudinal scans using a single-scanner pediatric dataset, including healthy and spinal cord injury (SCI) subjects. Two identical datasets were collected from 42 healthy and 27 SCI subjects with a 2-hour interval between scans on a 3T Siemens MRI scanner. Axial DTI images of the entire cervical and thoracic spinal cord were obtained, and various average diffusion tensor metrics (FA, MD, RD, & AD) were measured at each vertebral level. Pearson correlation and intraclass correlation coefficients were used to evaluate inter- and intra-subject agreement pre- and post-harmonization. Minimal improvement in agreement was observed with the mean square residual (MSR) model, while the restricted maximum likelihood estimator (REML) model showed reduced intra-subject agreement in all the tensor metrics. The significant variability between longitudinal DTI scans within a single scanner was likely due to physiological motion rather than scanner effects. Post-harmonization using the longComBat MSR model showed limited improvement in agreement.

A network control theory pipeline for studying the dynamics of the structural connectome.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Ethanol consumption in non-human primates alters plasma markers of bone turnover but not tibia architecture.

Ethanol consumption is associated with positive, negative, and neutral effects on the skeletal system. Our previous work using a nonhuman primate model of voluntary ethanol consumption showed that chronic ethanol use has an impact on skeletal attributes, most notably on biochemical markers of bone turnover. However, these studies were limited by small sample sizes and resulting lack of statistical power. Here, we applied a machine learning framework to integrate data from 155 monkeys (100 ethanol and 55 controls) to identify the bone features associated with chronic ethanol use. Specifically, we analyzed the influence of ethanol consumption on biomarkers of bone turnover and cancellous and cortical bone architecture in tibia. We hypothesized that chronic ethanol use for 6 months to 2.5 years would result in measurable changes to cancellous features and the biochemical markers compared to control animals. We observed a decrease in bone turnover in monkeys exposed to ethanol; however, we did not find that ethanol consumption resulted in measurable changes in bone architecture.

A sensorimotor-association axis of thalamocortical connection development.

Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brain's capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity7-12, here we evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth. We first introduce, cortically annotate, and anatomically validate a new atlas of human thalamocortical connections using diffusion tractography. By applying this atlas to three independent youth datasets (ages 8-23 years; total N = 2,676), we reproducibly demonstrate that thalamocortical connections develop along a maturational gradient that aligns with the cortex's sensorimotor-association axis. Associative cortical regions with thalamic connections that take longest to mature exhibit protracted expression of neurochemical, structural, and functional markers indicative of higher circuit plasticity as well as heightened environmental sensitivity. This work highlights a central role for the thalamus in the orchestration of hierarchically organized and environmentally sensitive windows of cortical developmental malleability.