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The past, present, and future of the brain imaging data structure (BIDS)
Abstract The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
Obesity and the cerebral cortex: Underlying neurobiology in mice and humans.
Obesity is a major modifiable risk factor for Alzheimer's disease (AD), characterized by progressive atrophy of the cerebral cortex. The neurobiology of obesity contributions to AD is poorly understood. Here we show with in vivo MRI that diet-induced obesity decreases cortical volume in mice, and that higher body adiposity associates with lower cortical volume in humans. Single-nuclei transcriptomics of the mouse cortex reveals that dietary obesity promotes an array of neuron-adverse transcriptional dysregulations, which are mediated by an interplay of excitatory neurons and glial cells, and which involve microglial activation and lowered neuronal capacity for neuritogenesis and maintenance of membrane potential. The transcriptional dysregulations of microglia, more than of other cell types, are like those in AD, as assessed with single-nuclei cortical transcriptomics in a mouse model of AD and two sets of human donors with the disease. Serial two-photon tomography of microglia demonstrates microgliosis throughout the mouse cortex. The spatial pattern of adiposity-cortical volume associations in human cohorts interrogated together with in silico bulk and single-nucleus transcriptomic data from the human cortex implicated microglia (along with other glial cells and subtypes of excitatory neurons), and it correlated positively with the spatial profile of cortical atrophy in patients with mild cognitive impairment and AD. Thus, multi-cell neuron-adverse dysregulations likely contribute to the loss of cortical tissue in obesity. The dysregulations of microglia may be pivotal to the obesity-related risk of AD.
Exploring the incidence of inadequate response to antidepressants in the primary care of depression.
Data from the UK suggests 13-55 % of depression patients experience some level of treatment resistance. However, little is known about how physicians manage inadequate response to antidepressants in primary care. This study aimed to explore the incidence of inadequate response to antidepressants in UK primary care. One-hundred-eighty-four medication-free patients with low mood initiated antidepressant treatment and monitored severity of depression symptoms, using the QIDS-SR16, for 48 weeks. Medication changes, visits to healthcare providers, and health-related quality of life were also recorded. Patients were classified into one of four response types based on their QIDS scores at three study timepoints: persistent inadequate responders (<50 % reduction in baseline QIDS at all timepoints), successful responders (≥50 % reduction in baseline QIDS at all timepoints), slow responders (≥50 % reduction in QIDS at week 48, despite earlier inadequate responses), and relapse (initial ≥50 % reduction in baseline QIDS, but inadequate response by week 48). Forty-eight weeks after initiating treatment 47 % of patients continued to experience symptoms of depression (QIDS >5), and 20 % of patients had a persistent inadequate response. Regardless of treatment response, 96 % (n = 176) of patients did not visit their primary care physician over the 40-week follow-up period. These results suggest that despite receiving treatment, a considerable proportion of patients with low mood remain unwell and fail to recover. Monitoring depression symptoms remotely can enable physicians to identify inadequate responders, allowing patients to be reassessed or referred to secondary services, likely improving patients' quality of life and reducing the socioeconomic impacts of chronic mental illness.
Feasibility and usability of remote monitoring in Alzheimer's disease.
INTRODUCTION: Remote monitoring technologies (RMTs) can measure cognitive and functional decline objectively at-home, and offer opportunities to measure passively and continuously, possibly improving sensitivity and reducing participant burden in clinical trials. However, there is skepticism that age and cognitive or functional impairment may render participants unable or unwilling to comply with complex RMT protocols. We therefore assessed the feasibility and usability of a complex RMT protocol in all syndromic stages of Alzheimer's disease and in healthy control participants. METHODS: For 8 weeks, participants (N = 229) used two activity trackers, two interactive apps with either daily or weekly cognitive tasks, and optionally a wearable camera. A subset of participants participated in a 4-week sub-study (N = 45) using fixed at-home sensors, a wearable EEG sleep headband and a driving performance device. Feasibility was assessed by evaluating compliance and drop-out rates. Usability was assessed by problem rates (e.g., understanding instructions, discomfort, forgetting to use the RMT or technical problems) as discussed during bi-weekly semi-structured interviews. RESULTS: Most problems were found for the active apps and EEG sleep headband. Problem rates increased and compliance rates decreased with disease severity, but the study remained feasible. CONCLUSIONS: This study shows that a highly complex RMT protocol is feasible, even in a mild-to-moderate AD population, encouraging other researchers to use RMTs in their study designs. We recommend evaluating the design of individual devices carefully before finalizing study protocols, considering RMTs which allow for real-time compliance monitoring, and engaging the partners of study participants in the research.
Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism.
Deuterium metabolic imaging (DMI) is an emerging magnetic resonance technique, for non-invasive mapping of human brain glucose metabolism following oral or intravenous administration of deuterium-labeled glucose. Regional differences in glucose metabolism can be observed in various brain pathologies, such as Alzheimer's disease, cancer, epilepsy or schizophrenia, but the achievable spatial resolution of conventional phase-encoded DMI methods is limited due to prolonged acquisition times rendering submilliliter isotropic spatial resolution for dynamic whole brain DMI not feasible. The purpose of this study was to implement non-Cartesian spatial-spectral sampling schemes for whole-brain 2H FID-MR Spectroscopic Imaging to assess time-resolved metabolic maps with sufficient spatial resolution to reliably detect metabolic differences between healthy gray and white matter regions. Results were compared with lower-resolution DMI maps, conventionally acquired within the same session. Six healthy volunteers (4 m/2 f) were scanned for ~90 min after administration of 0.8 g/kg oral [6,6']-2H glucose. Time-resolved whole brain 2H FID-DMI maps of glucose (Glc) and glutamate + glutamine (Glx) were acquired with 0.75 and 2 mL isotropic spatial resolution using density-weighted concentric ring trajectory (CRT) and conventional phase encoding (PE) readout, respectively, at 7 T. To minimize the effect of decreased signal-to-noise ratios associated with smaller voxels, low-rank denoising of the spatiotemporal data was performed during reconstruction. Sixty-three minutes after oral tracer uptake three-dimensional (3D) CRT-DMI maps featured 19% higher (p = .006) deuterium-labeled Glc concentrations in GM (1.98 ± 0.43 mM) compared with WM (1.66 ± 0.36 mM) dominated regions, across all volunteers. Similarly, 48% higher (p = .01) 2H-Glx concentrations were observed in GM (2.21 ± 0.44 mM) compared with WM (1.49 ± 0.20 mM). Low-resolution PE-DMI maps acquired 70 min after tracer uptake featured smaller regional differences between GM- and WM-dominated areas for 2H-Glc concentrations with 2.00 ± 0.35 mM and 1.71 ± 0.31 mM, respectively (+16%; p = .045), while no regional differences were observed for 2H-Glx concentrations. In this study, we successfully implemented 3D FID-MRSI with fast CRT encoding for dynamic whole-brain DMI at 7 T with 2.5-fold increased spatial resolution compared with conventional whole-brain phase encoded (PE) DMI to visualize regional metabolic differences. The faster metabolic activity represented by 48% higher Glx concentrations was observed in GM- compared with WM-dominated regions, which could not be reproduced using whole-brain DMI with the low spatial resolution protocol. Improved assessment of regional pathologic alterations using a fully non-invasive imaging method is of high clinical relevance and could push DMI one step toward clinical applications.
Test-Retest Reproducibility of Reduced-Field-of-View Density-Weighted CRT MRSI at 3T.
Quantifying an imaging modality's ability to reproduce results is important for establishing its utility. In magnetic resonance spectroscopic imaging (MRSI), new acquisition protocols are regularly introduced which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-field-of-view density-weighted concentric ring trajectory (rFOV-DW-CRT) MRSI) by calculating the coefficient of variance of data acquired from a test-retest experiment. The posterior cingulate cortex (PCC) and the right superior corona radiata (SCR) were selected as the regions of interest (ROIs) for grey matter (GM) and white matter (WM), respectively. CVs for between-subject and within-subject were consistently around or below 15% for Glx, tCho, and Myo-Ins, and below 5% for tNAA and tCr.
Evaluating the efficacy and mechanisms of a ketogenic diet as adjunctive treatment for people with treatment-resistant depression: A protocol for a randomised controlled trial.
BACKGROUND: One-third of people with depression do not respond to antidepressants, and, after two adequate courses of antidepressants, are classified as having treatment-resistant depression (TRD). Some case reports suggest that ketogenic diets (KDs) may improve some mental illnesses, and preclinical data indicate that KDs can influence brain reward signalling, anhedonia, cortisol, and gut microbiome which are associated with depression. To date, no trials have examined the clinical effect of a KD on TRD. METHODS: This is a proof-of-concept randomised controlled trial to investigate the efficacy of a six-week programme of weekly dietitian counselling plus provision of KD meals, compared with an intervention involving similar dietetic contact time and promoting a healthy diet with increased vegetable consumption and reduction in saturated fat, plus food vouchers to purchase healthier items. At 12 weeks we will assess whether participants have continued to follow the assigned diet. The primary outcome is the difference between groups in the change in Patient Health Questionnaire-9 (PHQ-9) score from baseline to 6 weeks. PHQ-9 will be measured at weeks 2, 4, 6 and 12. The secondary outcomes are the differences between groups in the change in remission of depression, change in anxiety score, functioning ability, quality of life, cognitive performance, reward sensitivity, and anhedonia from baseline to 6 and 12 weeks. We will also assess whether changes in reward sensitivity, anhedonia, cortisol awakening response and gut microbiome may explain any changes in depression severity. DISCUSSION: This study will test whether a ketogenic diet is an effective intervention to reduce the severity of depression, anxiety and improve quality of life and functioning ability for people with treatment-resistant depression.
A Behavioral Association Between Prediction Errors and Risk-Seeking: Theory and Evidence
Reward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choices towards or away from uncertainty; they can be manipulated with drugs that target the dopaminergic system. The common neural substrate suggests that RPEs and risk preferences might be linked on the level of behavior as well, but this has never been tested. Here, we aim to close this gap. First, we apply a recent theory of learning in the basal ganglia to predict how exactly RPEs might influence risk preferences. We then test our behavioral predictions using a novel bandit task in which value and risk vary independently across options. Critically, conditions are included where options vary in risk but are matched for value. We find that subjects become more risk seeking if choices are preceded by positive RPEs, and more risk averse if choices are preceded by negative RPEs. These findings cannot be explained by other known effects, such as nonlinear utility curves or dynamic learning rates. Finally, we show that RPE-induced risk-seeking is indexed by pupil dilation: participants with stronger pupillary correlates of RPE also show more pronounced behavioral effects. Author’s summary Many of our decisions are based on expectations. Sometimes, however, surprises happen: outcomes are not as expected. Such discrepancies between expectations and actual outcomes are called prediction errors. Our brain recognises and uses such prediction errors to modify our expectations and make them more realistic--a process known as reinforcement learning. In particular, neurons that release the neurotransmitter dopamine show activity patterns that strongly resemble prediction errors. Interestingly, the same neurotransmitter is also known to regulate risk preferences: dopamine levels control our willingness to take risks. We theorised that, since learning signals cause dopamine release, they might change risk preferences as well. In this study, we test this hypothesis. We find that participants are more likely to make a risky choice just after they experienced an outcome that was better than expected, which is precisely what out theory predicts. This suggests that dopamine signalling can be ambiguous--a learning signal can be mistaken for an impulse to take a risk.
Goal commitment is supported by vmPFC through selective attention.
When striking a balance between commitment to a goal and flexibility in the face of better options, people often demonstrate strong goal perseveration. Here, using functional MRI (n = 30) and lesion patient (n = 26) studies, we argue that the ventromedial prefrontal cortex (vmPFC) drives goal commitment linked to changes in goal-directed selective attention. Participants performed an incremental goal pursuit task involving sequential decisions between persisting with a goal versus abandoning progress for better alternative options. Individuals with stronger goal perseveration showed higher goal-directed attention in an interleaved attention task. Increasing goal-directed attention also affected abandonment decisions: while pursuing a goal, people lost their sensitivity to valuable alternative goals while remaining more sensitive to changes in the current goal. In a healthy population, individual differences in both commitment biases and goal-oriented attention were predicted by baseline goal-related activity in the vmPFC. Among lesion patients, vmPFC damage reduced goal commitment, leading to a performance benefit.
Reward positivity affects temporal interval production in a continuous timing task.
The neural circuits of reward processing and interval timing (including the perception and production of temporal intervals) are functionally intertwined, suggesting that it might be possible for momentary reward processing to influence subsequent timing behavior. Previous animal and human studies have mainly focused on the effect of reward on interval perception, whereas its impact on interval production is less clear. In this study, we examined whether feedback, as an example of performance-contingent reward, biases interval production. We recorded EEG from 20 participants while they engaged in a continuous drumming task with different realistic tempos (1728 trials per participant). Participants received color-coded feedback after each beat about whether they were correct (on time) or incorrect (early or late). Regression-based EEG analysis was used to unmix the rapid occurrence of a feedback response called the reward positivity (RewP), which is traditionally observed in more slow-paced tasks. Using linear mixed modeling, we found that RewP amplitude predicted timing behavior for the upcoming beat. This performance-biasing effect of the RewP was interpreted as reflecting the impact of fluctuations in reward-related anterior cingulate cortex activity on timing, and the necessity of continuous paradigms to make such observations was highlighted.
DIMOND: DIffusion Model OptimizatioN with Deep Learning.
Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.
Relationship of plasma biomarkers to digital cognitive tests in Alzheimer's disease.
INTRODUCTION: A major limitation in Alzheimer's disease (AD) research is the lack of the ability to measure cognitive performance at scale-robustly, remotely, and frequently. Currently, there are no established online digital platforms validated against plasma biomarkers of AD. METHODS: We used a novel web-based platform that assessed different cognitive functions in AD patients (N = 46) and elderly controls (N = 53) who were also evaluated for plasma biomarkers (amyloid beta 42/40 ratio, phosphorylated tau ([p-tau]181, glial fibrillary acidic protein, neurofilament light chain). Their cognitive performance was compared to a second, larger group of elderly controls (N = 352). RESULTS: Patients with AD were significantly impaired across all digital cognitive tests, with performance correlating with plasma biomarker levels, particularly p-tau181. The combination of p-tau181 and the single best-performing digital test achieved high accuracy in group classification. DISCUSSION: These findings show how online testing can now be deployed in patients with AD to measure cognitive function effectively and related to blood biomarkers of the disease. HIGHLIGHTS: This is the first study comparing online digital testing to plasma biomarkers.Alzheimer's disease patients and two independent cohorts of elderly controls were assessed.Cognitive performance correlated with plasma biomarkers, particularly phosphorylated tau (p-tau)181.Glial fibrillary acidic protein and neurofilament light chain, and less so the amyloid beta 42/40 ratio, were also associated with performance.The best cognitive metric performed at par to p-tau181 in group classification.
Timing along the cardiac cycle modulates neural signals of reward-based learning.
Natural fluctuations in cardiac activity modulate brain activity associated with sensory stimuli, as well as perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the relationship between fluctuations in heart activity and other internal representations. Here we investigate whether the cardiac cycle relates to learning-related internal representations - absolute and signed prediction errors. We combined machine learning techniques with electroencephalography with both simple, direct indices of task performance and computational model-derived indices of learning. Our results demonstrate that just as people are more sensitive to low magnitude, near-threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. However, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger differences in their prediction error representations between cardiac cycles exhibited higher learning rates and greater task accuracy.
When a test is more than just a test: Findings from patient interviews and survey in the trial of a technology to measure antidepressant medication response (the PReDicT Trial).
BACKGROUND: A RCT of a novel intervention to detect antidepressant medication response (the PReDicT Test) took place in five European countries, accompanied by a nested study of its acceptability and implementation presented here. The RCT results indicated no effect of the intervention on depression at 8 weeks (primary outcome), although effects on anxiety at 8 weeks and functioning at 24 weeks were found. METHODS: The nested study used mixed methods. The aim was to explore patient experiences of the Test including acceptability and implementation, to inform its use within care. A bespoke survey was completed by trial participants in five countries (n = 778) at week 8. Semi-structured interviews were carried out in two countries soon after week 8 (UK n = 22, Germany n = 20). Quantitative data was analysed descriptively; for qualitative data, thematic analysis was carried out using a framework approach. Results of the two datasets were interrogated together. OUTCOMES: Survey results showed the intervention was well received, with a majority of participants indicating they would use it again, and it gave them helpful extra information; a small minority indicated the Test made them feel worse. Qualitative data showed the Test had unexpected properties, including: instigating a process of reflection, giving participants feedback on progress and new understanding about their illness, and making participants feel supported and more engaged in treatment. INTERPRETATION: The qualitative and quantitative results are generally consistent. The Test's unexpected properties may explain why the RCT showed little effect, as properties were experienced across both trial arms. Beyond the RCT, the qualitative data sheds light on measurement reactivity, i.e., how measurements of depression can impact patients.
Sedation Research in Critically Ill Pediatric Patients: Proposals for Future Study Design From the Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research IV Workshop.
OBJECTIVES: Sedation and analgesia for infants and children requiring mechanical ventilation in the PICU is uniquely challenging due to the wide spectrum of ages, developmental stages, and pathophysiological processes encountered. Studies evaluating the safety and efficacy of sedative and analgesic management in pediatric patients have used heterogeneous methodologies. The Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research (SCEPTER) IV hosted a series of multidisciplinary meetings to establish consensus statements for future clinical study design and implementation as a guide for investigators studying PICU sedation and analgesia. DESIGN: Twenty-five key elements framed as consensus statements were developed in five domains: study design, enrollment, protocol, outcomes and measurement instruments, and future directions. SETTING: A virtual meeting was held on March 2-3, 2022, followed by an in-person meeting in Washington, DC, on June 15-16, 2022. Subsequent iterative online meetings were held to achieve consensus. SUBJECTS: Fifty-one multidisciplinary, international participants from academia, industry, the U.S. Food and Drug Administration, and family members of PICU patients attended the virtual and in-person meetings. Participants were invited based on their background and experience. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Common themes throughout the SCEPTER IV consensus statements included using coordinated multidisciplinary and interprofessional teams to ensure culturally appropriate study design and diverse patient enrollment, obtaining input from PICU survivors and their families, engaging community members, and using developmentally appropriate and validated instruments for assessments of sedation, pain, iatrogenic withdrawal, and ICU delirium. CONCLUSIONS: These SCEPTER IV consensus statements are comprehensive and may assist investigators in the design, enrollment, implementation, and dissemination of studies involving sedation and analgesia of PICU patients requiring mechanical ventilation. Implementation may strengthen the rigor and reproducibility of research studies on PICU sedation and analgesia and facilitate the synthesis of evidence across studies to improve the safety and quality of care for PICU patients.