Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components.

The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.

Quality over quantity: powering neuroimaging samples in psychiatry.

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank.

OBJECTIVES: Despite rising rates of multimorbidity, existing risk assessment tools are mostly limited to a single outcome of interest. This study tests the feasibility of producing multiple disease risk estimates with at least 70% discrimination (area under the receiver operating curve, AUROC) within the time and information constraints of the existing primary care health check framework. DESIGN: Observational prospective cohort study SETTING: UK Biobank. PARTICIPANTS: 228 240 adults from the UK population. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Myocardial infarction, atrial fibrillation, heart failure, stroke, all-cause dementia, chronic kidney disease, fatty liver disease, alcoholic liver disease, liver cirrhosis and liver failure. RESULTS: Using a set of predictors easily gathered at the standard primary care health check (such as the National Health Service Health Check), we demonstrate that it is feasible to simultaneously produce risk estimates for multiple disease outcomes with AUROC of 70% or greater. These predictors can be entered once into a single form and produce risk scores for stroke (AUROC 0.727, 95% CI 0.713 to 0.740), all-cause dementia (0.823, 95% CI 0.810 to 0.836), myocardial infarction (0.785, 95% CI 0.775 to 0.795), atrial fibrillation (0.777, 95% CI 0.768 to 0.785), heart failure (0.828, 95% CI 0.818 to 0.838), chronic kidney disease (0.774, 95% CI 0.765 to 0.783), fatty liver disease (0.766, 95% CI 0.753 to 0.779), alcoholic liver disease (0.864, 95% CI 0.835 to 0.894), liver cirrhosis (0.763, 95% CI 0.734 to 0.793) and liver failure (0.746, 95% CI 0.695 to 0.796). CONCLUSIONS: Easily collected diagnostics can be used to assess 10-year risk across multiple disease outcomes, without the need for specialist computing or invasive biomarkers. Such an approach could increase the utility of existing data and place multiorgan risk information at the fingertips of primary care providers, thus creating opportunities for longer-term multimorbidity prevention. Additional work is needed to validate whether these findings would hold in a larger, more representative cohort outside the UK Biobank.

A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis.

OBJECTIVE: Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials. METHODS: Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way. RESULTS: We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting. CONCLUSION: Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.

Two common and distinct forms of variation in human functional brain networks.

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.

Spatial confidence regions for combinations of excursion sets in image analysis.

The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide confidence statements about the intersection, or union, of the excursion sets across all fields. Such spatial regions are of natural interest as they directly correspond to the questions 'Where do all random fields exceed a predetermined threshold?', or 'Where does at least one random field exceed a predetermined threshold?'. To assess the degree of spatial variability present, our method provides, with a desired confidence, subsets and supersets of spatial regions defined by logical conjunctions (i.e. set intersections) or disjunctions (i.e. set unions), without any assumption on the dependence between the different fields. The method is verified by extensive simulations and demonstrated using task-fMRI data to identify brain regions with activation common to four variants of a working memory task.

MRI economics: Balancing sample size and scan duration in brain wide association studies.

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Decentralized Mixed Effects Modeling in COINSTAC.

Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.

NHS Health Check attendance is associated with reduced multiorgan disease risk: a matched cohort study in the UK Biobank.

BACKGROUND: The NHS Health Check is a preventive programme in the UK designed to screen for cardiovascular risk and to aid in primary disease prevention. Despite its widespread implementation, the effectiveness of the NHS Health Check for longer-term disease prevention is unclear. In this study, we measured the rate of new diagnoses in UK Biobank participants who underwent the NHS Health Check compared with those who did not. METHODS: Within the UK Biobank prospective study, 48,602 NHS Health Check recipients were identified from linked primary care records. These participants were then covariate-matched on an extensive range of socio-demographic, lifestyle, and medical factors with 48,602 participants without record of the check. Follow-up diagnoses were ascertained from health records over an average of 9 years (SD 2 years) including hypertension, diabetes, hypercholesterolaemia, stroke, dementia, myocardial infarction, atrial fibrillation, heart failure, fatty liver disease, alcoholic liver disease, liver cirrhosis, liver failure, acute kidney injury, chronic kidney disease (stage 3 +), cardiovascular mortality, and all-cause mortality. Time-varying survival modelling was used to compare adjusted outcome rates between the groups. RESULTS: In the immediate 2 years after the NHS Health Check, higher diagnosis rates were observed for hypertension, high cholesterol, and chronic kidney disease among health check recipients compared to their matched counterparts. However, in the longer term, NHS Health Check recipients had significantly lower risk across all multiorgan disease outcomes and reduced rates of cardiovascular and all-cause mortality. CONCLUSIONS: The NHS Health Check is linked to reduced incidence of disease across multiple organ systems, which may be attributed to risk modification through earlier detection and treatment of key risk factors such as hypertension and high cholesterol. This work adds important evidence to the growing body of research supporting the effectiveness of preventative interventions in reducing longer-term multimorbidity.

Reciprocal relationships between stress and depressive symptoms: the essential role of the nucleus accumbens.

BACKGROUND: Stress and depression have a reciprocal relationship, but the neural underpinnings of this reciprocity are unclear. We investigated neuroimaging phenotypes that facilitate the reciprocity between stress and depressive symptoms. METHODS: In total, 22 195 participants (52.0% females) from the population-based UK Biobank study completed two visits (initial visit: 2006-2010, age = 55.0 ± 7.5 [40-70] years; second visit: 2014-2019; age = 62.7 ± 7.5 [44-80] years). Structural equation modeling was used to examine the longitudinal relationship between self-report stressful life events (SLEs) and depressive symptoms. Cross-sectional data were used to examine the overlap between neuroimaging correlates of SLEs and depressive symptoms on the second visit among 138 multimodal imaging phenotypes. RESULTS: Longitudinal data were consistent with significant bidirectional causal relationship between SLEs and depressive symptoms. In cross-sectional analyses, SLEs were significantly associated with lower bilateral nucleus accumbal volume and lower fractional anisotropy of the forceps major. Depressive symptoms were significantly associated with extensive white matter hyperintensities, thinner cortex, lower subcortical volume, and white matter microstructural deficits, mainly in corticostriatal-limbic structures. Lower bilateral nucleus accumbal volume were the only imaging phenotypes with overlapping effects of depressive symptoms and SLEs (B = -0.032 to -0.023, p = 0.006-0.034). Depressive symptoms and SLEs significantly partially mediated the effects of each other on left and right nucleus accumbens volume (proportion of effects mediated = 12.7-14.3%, p < 0.001-p = 0.008). For the left nucleus accumbens, post-hoc seed-based analysis showed lower resting-state functional connectivity with the left orbitofrontal cortex (cluster size = 83 voxels, p = 5.4 × 10-5) in participants with high v. no SLEs. CONCLUSIONS: The nucleus accumbens may play a key role in the reciprocity between stress and depressive symptoms.

Associations between cortisol stress responses and limbic volume and thickness in young adults: An exploratory study.

The investigation of the relationship between neural measures of limbic structures and hypothalamic pituitary adrenal axis responses to acute stress exposure in healthy young adults has so far focused in particular on task-based and resting state functional connectivity studies. Thus, the present study examined the association between limbic volume and thickness measures and acute cortisol responses to the psychosocial stress paradigm ScanSTRESS. Using Permutation Analysis of Linear Models controlling for sex, age and total brain volume, the associations between (sex-specific) cortisol increases and human connectome project style anatomical variables of limbic structures (i.e. volume and thickness) were investigated in 66 healthy and young (18-33 years) subjects (35 men, 31 women taking oral contraceptives). In addition, exploratory (sex-specific) bivariate correlations between cortisol increases and structural measures were conducted. The present data provide interesting new insights into the involvement of striato-limbic structures in psychosocial stress processing, suggesting that acute cortisol stress responses are also associated with mere structural measures of the human brain. Thus, our preliminary findings suggest that not only situation- and context-dependent reactions of the limbic system (i.e. blood oxygenation level-dependent reactions) are related to acute (sex-specific) cortisol stress responses but also basal and somewhat more constant structural measures. Our study hereby paves the way for further analyses in this context and highlights the relevance of the topic.

Neuroscience Needs Network Science.

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Leveraging the Adolescent Brain Cognitive Development Study to improve behavioral prediction from neuroimaging in smaller replication samples.

Magnetic resonance imaging (MRI) is a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~100 subjects for structural MRI. Even with 100 random re-samplings of 50 subjects in the discovery sample, prediction can be adequately powered with 98 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators' research programs and grants.

A scalable approach for continuous time Markov models with covariates.

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set.

Liver disease is a significant risk factor for cardiovascular outcomes - A UK Biobank study.

BACKGROUND & AIMS: Chronic liver disease (CLD) is associated with increased cardiovascular disease (CVD) risk. We investigated whether early signs of liver disease (measured by iron-corrected T1-mapping [cT1]) were associated with an increased risk of major CVD events. METHODS: Liver disease activity (cT1) and fat (proton density fat fraction [PDFF]) were measured using LiverMultiScan® between January 2016 and February 2020 in the UK Biobank imaging sub-study. Using multivariable Cox regression, we explored associations between liver cT1 (MRI) and primary CVD (coronary artery disease, atrial fibrillation [AF], embolism/vascular events, heart failure [HF] and stroke), and CVD hospitalisation and all-cause mortality. Liver blood biomarkers, general metabolism biomarkers, and demographics were also included. Subgroup analysis was conducted in those without metabolic syndrome (defined as at least three of: a large waist, high triglycerides, low high-density lipoprotein cholesterol, increased systolic blood pressure, or elevated haemoglobin A1c). RESULTS: A total of 33,616 participants (mean age 65 years, mean BMI 26 kg/m2, mean haemoglobin A1c 35 mmol/mol) had complete MRI liver data with linked clinical outcomes (median time to major CVD event onset: 1.4 years [range: 0.002-5.1]; follow-up: 2.5 years [range: 1.1-5.2]). Liver disease activity (cT1), but not liver fat (PDFF), was associated with higher risk of any major CVD event (hazard ratio 1.14; 95% CI 1.03-1.26; p = 0.008), AF (1.30; 1.12-1.51; p <0.001); HF (1.30; 1.09-1.56; p= 0.004); CVD hospitalisation (1.27; 1.18-1.37; p <0.001) and all-cause mortality (1.19; 1.02-1.38; p = 0.026). FIB-4 index was associated with HF (1.06; 1.01-1.10; p = 0.007). Risk of CVD hospitalisation was independently associated with cT1 in individuals without metabolic syndrome (1.26; 1.13-1.4; p <0.001). CONCLUSION: Liver disease activity, by cT1, was independently associated with a higher risk of incident CVD and all-cause mortality, independent of pre-existing metabolic syndrome, liver fibrosis or fat. IMPACT AND IMPLICATIONS: Chronic liver disease (CLD) is associated with a twofold greater incidence of cardiovascular disease. Our work shows that early liver disease on iron-corrected T1 mapping was associated with a higher risk of major cardiovascular disease (14%), cardiovascular disease hospitalisation (27%) and all-cause mortality (19%). These findings highlight the prognostic relevance of a comprehensive evaluation of liver health in populations at risk of CVD and/or CLD, even in the absence of clinical manifestations or metabolic syndrome, when there is an opportunity to modify/address risk factors and prevent disease progression. As such, they are relevant to patients, carers, clinicians, and policymakers.

Association of gout with brain reserve and vulnerability to neurodegenerative disease.

Studies of neurodegenerative disease risk in gout are contradictory. Relationships with neuroimaging markers of brain structure, which may offer insights, are uncertain. Here we investigated associations between gout, brain structure, and neurodegenerative disease incidence. Gout patients had smaller global and regional brain volumes and markers of higher brain iron, using both observational and genetic approaches. Participants with gout also had higher incidence of all-cause dementia, Parkinson's disease, and probable essential tremor. Risks were strongly time dependent, whereby associations with incident dementia were highest in the first 3 years after gout diagnosis. These findings suggest gout is causally related to several measures of brain structure. Lower brain reserve amongst gout patients may explain their higher vulnerability to multiple neurodegenerative diseases. Motor and cognitive impairments may affect gout patients, particularly in early years after diagnosis.

Neuroscience needs Network Science.

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

The effect of d-cycloserine on brain processing of breathlessness over pulmonary rehabilitation: an experimental medicine study.

Research question: Pulmonary rehabilitation is the best treatment for chronic breathlessness in COPD but there remains an unmet need to improve efficacy. Pulmonary rehabilitation has strong parallels with exposure-based cognitive behavioural therapies (CBT), both clinically and in terms of brain activity patterns. The partial N-methyl-d-aspartate (NMDA)-receptor agonist d-cycloserine has shown promising results in enhancing efficacy of CBT, thus we hypothesised that it would similarly augment the effects of pulmonary rehabilitation in the brain. Positive findings would support further development in phase 3 clinical trials. Methods: 72 participants with mild-to-moderate COPD were recruited to a double-blind pre-registered (ClinicalTrials.gov identifier: NCT01985750) experimental medicine study running parallel to a pulmonary rehabilitation course. Participants were randomised to 250 mg d-cycloserine or placebo, administered immediately prior to the first four sessions of pulmonary rehabilitation. Primary outcome measures were differences between d-cycloserine and placebo in brain activity in the anterior insula, posterior insula, anterior cingulate cortices, amygdala and hippocampus following completion of pulmonary rehabilitation. Secondary outcomes included the same measures at an intermediate time point and voxel-wise difference across wider brain regions. An exploratory analysis determined the interaction with breathlessness anxiety. Results: No difference between d-cycloserine and placebo groups was observed across the primary or secondary outcome measures. d-cycloserine was shown instead to interact with changes in breathlessness anxiety to dampen reactivity to breathlessness cues. Questionnaire and measures of respiratory function showed no group difference. This is the first study testing brain-active drugs in pulmonary rehabilitation. Rigorous trial methodology and validated surrogate end-points maximised statistical power. Conclusion: Although increasing evidence supports therapeutic modulation of NMDA pathways to treat symptoms, we conclude that a phase 3 clinical trial of d-cycloserine would not be worthwhile.