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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.
Concurrent spinal and brain imaging with optically pumped magnetometers
Background: The spinal cord and its interactions with the brain are fundamental for movement control and somatosensation. However, brain and spinal electrophysiology in humans have largely been treated as distinct enterprises, in part due to the relative inaccessibility of the spinal cord. Consequently, there is a dearth of knowledge on human spinal electrophysiology, including the multiple pathologies that affect the spinal cord as well as the brain. New method: Here we exploit recent advances in the development of wearable optically pumped magnetometers (OPMs) which can be flexibly arranged to provide coverage of both the spinal cord and the brain in relatively unconstrained environments. This system for magnetospinoencephalography (MSEG) measures both spinal and cortical signals simultaneously by employing custom-made scanning casts. Results: We evidence the utility of such a system by recording spinal and cortical evoked responses to median nerve stimulation at the wrist. MSEG revealed early (10 – 15 ms) and late (>20 ms) responses at the spinal cord, in addition to typical cortical evoked responses (i.e., N20). Comparison with existing methods: Early spinal evoked responses detected were in line with conventional somatosensory evoked potential recordings. Conclusion: This MSEG system demonstrates the novel ability for concurrent non-invasive millisecond imaging of brain and spinal cord.
brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
Evaluating functional brain organization in individuals and identifying contributions to network overlap
Abstract Individual differences in the spatial organization of resting-state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting-state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting-state networks can be derived using high-quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that overlap between 2-network pairs is indicative of coupling. These results suggest that regions of network overlap concurrently process information from both contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.
A cognitive map for value-guided choice in ventromedial prefrontal cortex.
The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.
PET-measured human dopamine synthesis capacity and receptor availability predict trading rewards and time-costs during foraging.
Foraging behavior requires weighing costs of time to decide when to leave one reward patch to search for another. Computational and animal studies suggest that striatal dopamine is key to this process; however, the specific role of dopamine in foraging behavior in humans is not well characterized. We use positron emission tomography (PET) imaging to directly measure dopamine synthesis capacity and D1 and D2/3 receptor availability in 57 healthy adults who complete a computerized foraging task. Using voxelwise data and principal component analysis to identify patterns of variation across PET measures, we show that striatal D1 and D2/3 receptor availability and a pattern of mesolimbic and anterior cingulate cortex dopamine function are important for adjusting the threshold for leaving a patch to explore, with specific sensitivity to changes in travel time. These findings suggest a key role for dopamine in trading reward benefits against temporal costs to modulate behavioral adaptions to changes in the reward environment critical for foraging.
The first year of a new era.
What happened when eLife decided to eliminate accept/reject decisions after peer review?
Generative replay underlies compositional inference in the hippocampal-prefrontal circuit.
Human reasoning depends on reusing pieces of information by putting them together in new ways. However, very little is known about how compositional computation is implemented in the brain. Here, we ask participants to solve a series of problems that each require constructing a whole from a set of elements. With fMRI, we find that representations of novel constructed objects in the frontal cortex and hippocampus are relational and compositional. With MEG, we find that replay assembles elements into compounds, with each replay sequence constituting a hypothesis about a possible configuration of elements. The content of sequences evolves as participants solve each puzzle, progressing from predictable to uncertain elements and gradually converging on the correct configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry and a role for generative replay in compositional inference and hypothesis testing.
Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity.
Motor recovery is still limited for people with stroke especially those with greater functional impairments. In order to improve outcome, we need to understand more about the mechanisms underpinning recovery. Task-unbiased, blood flow-independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings and offers substantial promise to investigate physiological mechanisms, but behaviourally relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven people with subcortical ischaemic stroke and unilateral hand paresis of any degree were longitudinally evaluated at 3 weeks (early subacute) and 12 weeks (late subacute) after stroke. Resting-state magnetoencephalography and clinical scores of motor function were recorded and compared with matched controls. Magnetoencephalography data were decomposed using a data-driven hidden Markov model into 10 time-varying resting-state networks. People with stroke showed statistically significantly improved Action Research Arm Test and Fugl-Meyer upper extremity scores between 3 weeks and 12 weeks after stroke (both P < 0.001). Hidden Markov model analysis revealed a primarily alpha-band ipsilesional resting-state sensorimotor network which had a significantly increased life-time (the average time elapsed between entering and exiting the network) and fractional occupancy (the occupied percentage among all networks) at 3 weeks after stroke when compared with controls. The life-time of the ipsilesional resting-state sensorimotor network positively correlated with concurrent motor scores in people with stroke who had not fully recovered. Specifically, this relationship was observed only in ipsilesional rather in contralesional sensorimotor network, default mode network or visual network. The ipsilesional sensorimotor network metrics were not significantly different from controls at 12 weeks after stroke. The increased recruitment of alpha-band ipsilesional resting-state sensorimotor network at subacute stroke served as functionally correlated biomarkers exclusively in people with stroke with not fully recovered hand paresis, plausibly reflecting functional motor recovery processes.