Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection.

Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation ΔR2∗) to the resulting deconvolved events. Remarkably, we demonstrate that Mv-SPFM achieves comparable performance even when using a single-echo formulation. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Furthermore, we show that by employing stability selection, the performance of the algorithm depends less on the selection of temporal and spatial regularization parameters λ and ρ. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of ΔR2∗, for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.

An experimental approach to training mood for resilience.

According to influential theories about mood, exposure to environments characterized by specific patterns of punishments and rewards could shape mood response to future stimuli. This raises the intriguing possibility that mood could be trained by exposure to controlled environments. The aim of the present study is to investigate experimental settings that increase resilience of mood to negative stimuli. For this study, a new task was developed where participants register their mood when rewards are added or subtracted from their score. The study was conducted online, using Amazon MTurk, and a total of N = 1287 participants were recruited for all three sets of experiments. In an exploratory experiment, sixteen different experimental task environments which are characterized by different mood-reward relationships, were tested. We identified six task environments that produce the greatest improvements in mood resilience to negative stimuli, as measured by decreased sensitivity to loss. In a next step, we isolated the two most effective task environments, from the previous set of experiments, and we replicated our results and tested mood's resilience to negative stimuli over time, in a novel sample. We found that the effects of the task environments on mood are detectable and remain significant after multiple task rounds (approximately two minutes) for an environment where good mood yielded maximum reward. These findings are a first step in our effort to better understand the mechanisms behind mood training and its potential clinical utility.

A highly replicable decline in mood during rest and simple tasks.

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior.

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here, we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

Working memory and reward increase the accuracy of animal location encoding in the medial prefrontal cortex.

The ability to perceive spatial environments and locate oneself during navigation is crucial for the survival of animals. Mounting evidence suggests a role of the medial prefrontal cortex (mPFC) in spatially related behaviors. However, the properties of mPFC spatial encoding and how it is influenced by animal behavior are poorly defined. Here, we train the mice to perform 3 tasks differing in working memory and reward-seeking: a delayed non-match to place (DNMTP) task, a passive alternation (PA) task, and a free-running task. Single-unit recording in the mPFC shows that although individual mPFC neurons exhibit spatially selective firing, they do not reliably represent the animal location. The population activity of mPFC neurons predicts the animal location. Notably, the population coding of animal locations by the mPFC is modulated by animal behavior in that the coding accuracy is higher in tasks involved in working memory and reward-seeking. This study reveals an approach whereby the mPFC encodes spatial positions and the behavioral variables affecting it.

11C-Metomidate PET-CT versus adrenal vein sampling to subtype primary aldosteronism: a prospective clinical trial.

OBJECTIVE: Adrenal vein sampling (AVS) is recommended to subtype primary aldosteronism, but it is technically challenging. We compared 11C-Metomidate-PET-computed tomography (PET-CT) and AVS for subtyping of primary aldosteronism. METHODS: Patients with confirmed primary aldosteronism underwent both AVS and 11C-Metomidate PET-CT (post-dexamethasone). All results were reviewed at a multidisciplinary meeting to decide on final subtype diagnosis. Primary outcome was accuracy of PET versus AVS to diagnosis of unilateral primary aldosteronism based on post-surgical biochemical cure. Secondary outcome was accuracy of both tests to final subtype diagnosis. RESULTS: All 25 patients recruited underwent PET and successful AVS (100%). Final diagnosis was unilateral in 22 patients, bilateral in two and indeterminate in one due to discordant lateralization. Twenty patients with unilateral primary aldosteronism underwent surgery, with 100% complete biochemical success, and 75% complete/partial clinical success. For the primary outcome, sensitivity of PET was 80% [95% confidence interval (95% CI): 56.3-94.3] and AVS was 75% (95% CI: 50.9-91.3). For the secondary outcome, sensitivity and specificity of PET was 81.9% (95% CI: 59.7-94.8) and 100% (95% CI: 15.8-100), and AVS was 68.2% (95% CI: 45.1-86.1) and 100% (95% CI: 15.8-100), respectively. Twelve out of 20 (60%) patients had both PET and AVS lateralization, four (20%) PET-only, three (15%) AVS-only, while one patient did not lateralize on PET or AVS. Post-surgery outcomes did not differ between patients identified by either test. CONCLUSION: In our pilot study, 11C-Metomidate PET-CT performed comparably to AVS, and this should be validated in larger studies. PET identified patients with unilateral primary aldosteronism missed on AVS, and these tests could be used together to identify more patients with unilateral primary aldosteronism. VIDEO ABSTRACT: http://links.lww.com/HJH/B918.

Magnetoencephalographic correlates of mood and reward dynamics in human adolescents.

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25-40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.

Validation of "Care of the Dying Evaluation" in Emergency Medicine (CODE-EM): pilot phase of end-of-life management protocol offered within emergency room (EMPOWER) study.

BACKGROUND: An increasing number of patients who present to emergency departments are at their end-of-life phase and have significant palliative care needs such as in symptom control for pain and dyspnoea. Evaluating quality of care provided is imperative, yet there is no suitable tool validated in the emergency and Asian settings. We aim to examine the face and construct validity, and reliability of a newly developed questionnaire, Care of the Dying Evaluation - Emergency Medicine, for measuring the quality of end-of-life care in an Asian emergency context. METHODS: A mixed methods pilot study was conducted. Participants composed of the next-of-kin to thirty dying patients who presented to the emergency departments of three public hospitals in Singapore. Qualitative evaluation, using cognitive "think-aloud" interviews, and quantitative analysis were employed. Percentage agreement and κ statistic were measured to evaluate temporal stability of the questionnaire. Cronbach's α and item-total correlations were used to assess internal consistency within the constructs. Confirmatory factor analysis was performed for construct validity. RESULTS: All participants reported clear understanding of the questionnaire with no ambiguity; a minority felt the questions caused emotional distress (7/30, 23.3%). The questions showed moderate to good test-retest reliability. Internal consistencies within the constructs were good for "ENVIRONMENT" and "CARE", and moderate for "COMMUNICATION". Factor loadings range from 0.40 to 0.99. CONCLUSIONS: The Care of the Dying Evaluation - Emergency Medicine questionnaire may be valid and reliable for use in an Asian emergency setting. Our prospective multicentre study using this evaluation tool may provide more insight on the quality of care rendered to dying patients and identify areas for improvement. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03906747).

The temporal representation of experience in subjective mood.

Humans refer to their mood state regularly in day-to-day as well as clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here, we use a computational approach to quantitatively answer this question and show that early events exert a stronger influence on reported mood (a primacy weighting) compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations across random, consistent, or dynamic reward environments, different age groups, and in both healthy and depressed participants. Moreover, we find evidence for neural encoding of the Primacy, but not the Recency, model in frontal brain regions related to mood regulation. These findings hold implications for the timing of events in experimental or clinical settings and suggest new directions for individualized mood interventions.

Early Coagulopathy in Pediatric Traumatic Brain Injury: A Pediatric Acute and Critical Care Medicine Asian Network (PACCMAN) Retrospective Study.

BACKGROUND: Although early coagulopathy increases mortality in adults with traumatic brain injury (TBI), less is known about pediatric TBI. OBJECTIVE: To describe the prothrombin time (PT), activated partial thromboplastin time (APTT), and platelet levels of children with moderate to severe TBI to identify predictors of early coagulopathy and study the association with clinical outcomes. METHODS: Using the Pediatric Acute and Critical Care Medicine Asian Network (PACCMAN) TBI retrospective cohort, we identified patients <16 yr old with a Glasgow Coma Scale (GCS) ≤13. We compared PT, APTT, platelets, and outcomes between children with isolated TBI and multiple trauma with TBI. We performed logistic regressions to identify predictors of early coagulopathy and study the association with mortality and poor functional outcomes. RESULTS: Among 370 children analyzed, 53/370 (14.3%) died and 127/370 (34.3%) had poor functional outcomes. PT was commonly deranged in both isolated TBI (53/173, 30.6%) and multiple trauma (101/197, 51.3%). Predictors for early coagulopathy were young age (adjusted odds ratio [aOR] 0.94, 95% CI 0.88-0.99, P = .023), GCS < 8 (aOR 1.96, 95% CI 1.26-3.06, P = .003), and presence of multiple trauma (aOR 2.21, 95% confidence interval [CI] 1.37-3.60, P = .001). After adjusting for age, gender, GCS, multiple traumas, and presence of intracranial bleed, children with early coagulopathy were more likely to die (aOR 7.56, 95% CI 3.04-23.06, P < .001) and have poor functional outcomes (aOR 2.16, 95% CI 1.26-3.76, P = .006). CONCLUSION: Early coagulopathy is common and independently associated with death and poor functional outcomes among children with TBI.

Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression.

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

Traumatic Brain Injury Outcomes in 10 Asian Pediatric ICUs: A Pediatric Acute and Critical Care Medicine Asian Network Retrospective Study.

OBJECTIVES: Traumatic brain injury remains an important cause of death and disability. We aim to report the epidemiology and management of moderate to severe traumatic brain injury in Asian PICUs and identify risk factors for mortality and poor functional outcomes. DESIGN: A retrospective study of the Pediatric Acute and Critical Care Medicine Asian Network moderate to severe traumatic brain injury dataset collected between 2014 and 2017. SETTING: Patients were from the participating PICUs of Pediatric Acute and Critical Care Medicine Asian Network. PATIENTS: We included children less than 16 years old with a Glasgow Coma Scale less than or equal to 13. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We obtained data on patient demographics, injury circumstances, and PICU management. We performed a multivariate logistic regression predicting for mortality and poor functional outcomes. We analyzed 380 children with moderate to severe traumatic brain injury. Most injuries were a result of road traffic injuries (174 [45.8%]) and falls (160 [42.1%]). There were important differences in temperature control, use of antiepileptic drugs, and hyperosmolar agents between the sites. Fifty-six children died (14.7%), and 104 of 324 survivors (32.1%) had poor functional outcomes. Poor functional outcomes were associated with non-high-income sites (adjusted odds ratio, 1.90; 95% CI, 1.11-3.29), Glasgow Coma Scale less than 8 (adjusted odds ratio, 4.24; 95% CI, 2.44-7.63), involvement in a road traffic collision (adjusted odds ratio, 1.83; 95% CI, 1.04-3.26), and presence of child abuse (adjusted odds ratio, 2.75; 95% CI, 1.01-7.46). CONCLUSIONS: Poor functional outcomes are prevalent after pediatric traumatic brain injury in Asia. There is an urgent need for further research in these high-risk groups.

Revealing the multidimensional mental representations of natural objects underlying human similarity judgements.

Objects can be characterized according to a vast number of possible criteria (such as animacy, shape, colour and function), but some dimensions are more useful than others for making sense of the objects around us. To identify these core dimensions of object representations, we developed a data-driven computational model of similarity judgements for real-world images of 1,854 objects. The model captured most explainable variance in similarity judgements and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. These dimensions predicted external categorization behaviour and reflected typicality judgements of those categories. Furthermore, humans can accurately rate objects along these dimensions, highlighting their interpretability and opening up a way to generate similarity estimates from object dimensions alone. Collectively, these results demonstrate that human similarity judgements can be captured by a fairly low-dimensional, interpretable embedding that generalizes to external behaviour.

End-of-life management protocol offered within emergency room (EMPOWER): study protocol for a multicentre study.

BACKGROUND: Patients at their end-of-life (EOL) phase frequently visit the emergency department (ED) due to their symptoms, yet the environment and physicians in ED are not traditionally equipped or trained to provide palliative care. This multicentre study aims to measure the current quality of EOL care in ED to identify gaps, formulate improvements and implement the improved EOL care protocol. We shall also evaluate healthcare resource utilisation and its associated costs. METHODS AND ANALYSIS: This study employs a quasiexperimental interrupted time series design using both qualitative and quantitative methods, involving the EDs of three tertiary hospitals in Singapore, over a period of 3 years. There are five phases in this study: (1) retrospective chart reviews of patients who died within 5 days of ED attendance; (2) pilot phase to validate the CODE questionnaire in the local context; (3) preimplementation phase; (4) focus group discussions (FGDs); and (5) postimplementation phase. In the prospective cohort, patients who are actively dying or have high likelihood of mortality this admission, and whose goal of care is palliation, will be eligible for inclusion. At least 140 patients will be recruited for each preimplementation and postimplementation phase. There will be face-to-face interviews with patients' family members, review of medical records and self-administered staff survey to evaluate existing knowledge and confidence. The FGDs will involve hospital and community healthcare providers. Data obtained from the retrospective cohort, preimplementation phase and FGDs will be used to guide prospective improvement and protocol changes. Patient, family and staff relevant outcomes from these changes will be measured using time series regression. ETHICS AND DISSEMINATION: The study protocol has been reviewed and ethics approval obtained from the National Healthcare Group Domain Specific Review Board, Singapore. The results from this study will be actively disseminated through manuscript publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT03906747.

Salvage arthrodesis for failed first metatarsophalangeal joint arthroplasty: A network meta-analysis.

BACKGROUND: We aim to provide an evidence-based literature review of salvage arthrodesis for failed first metatarsophalangeal joint arthroplasty with a network meta-analysis. METHODS: A search of PubMed, Embase and Cochrane databases was conducted in December 2016 which identified 12 relevant articles out of 340 articles assessing the efficacy of salvage arthrodesis for failed joint arthroplasty of the first metatarsophalangeal joint. The 12 studies were assigned a level of evidence (I-V) and interventions were graded a level of recommendation (A-C, I) in support of or against the treatment modality. RESULTS: There is fair evidence (grade B) to support salvage arthrodesis with structural bone graft. There is poor evidence (grade C) for salvage arthrodesis without bone graft. There was no good evidence (grade A) to recommend either intervention. Meta-analysis showed that salvage arthrodesis resulted in improved functional outcome over time. CONCLUSIONS: Salvage arthrodesis showed good bone union rates and patient satisfaction. LEVEL OF CLINICAL EVIDENCE: III - Systematic Review of Level III studies.

Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks.

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.

Subtle predictive movements reveal actions regardless of social context.

Humans have a remarkable ability to predict the actions of others. To address what information enables this prediction and how the information is modulated by social context, we used videos collected during an interactive reaching game. Two participants (an "initiator" and a "responder") sat on either side of a plexiglass screen on which two targets were affixed. The initiator was directed to tap one of the two targets, and the responder had to either beat the initiator to the target (competition) or arrive at the same time (cooperation). In a psychophysics experiment, new observers predicted the direction of the initiators' reach from brief clips, which were clipped relative to when the initiator began reaching. A machine learning classifier performed the same task. Both humans and the classifier were able to determine the direction of movement before the finger lift-off in both social conditions. Further, using an information mapping technique, the relevant information was found to be distributed throughout the body of the initiator in both social conditions. Our results indicate that we reveal our intentions during cooperation, in which communicating the future course of actions is beneficial, and also during competition despite the social motivation to reveal less information.

Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information.

Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.

Distributed Weight Consolidation: A Brain Segmentation Case Study.

Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns. However, it may be the case that derivative datasets or predictive models developed within individual sites can be shared and combined with fewer restrictions. Training on distributed data and combining the resulting networks is often viewed as continual learning, but these methods require networks to be trained sequentially. In this paper, we introduce distributed weight consolidation (DWC), a continual learning method to consolidate the weights of separate neural networks, each trained on an independent dataset. We evaluated DWC with a brain segmentation case study, where we consolidated dilated convolutional neural networks trained on independent structural magnetic resonance imaging (sMRI) datasets from different sites. We found that DWC led to increased performance on test sets from the different sites, while maintaining generalization performance for a very large and completely independent multi-site dataset, compared to an ensemble baseline.

Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain.

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the white matter. Typically, this is done via basis pursuit, but estimation of the exact directions is limited due to discretization [1, 2]. The difficulties inherent in modeling DWI data are shared by many other problems involving fitting non-parametric mixture models. Ekanadaham et al. [3] proposed an approach, continuous basis pursuit, to overcome discretization error in the 1-dimensional case (e.g., spike-sorting). Here, we propose a more general algorithm that fits mixture models of any dimensionality without discretization. Our algorithm uses the principles of L2-boost [4], together with refitting of the weights and pruning of the parameters. The addition of these steps to L2-boost both accelerates the algorithm and assures its accuracy. We refer to the resulting algorithm as elastic basis pursuit, or EBP, since it expands and contracts the active set of kernels as needed. We show that in contrast to existing approaches to fitting mixtures, our boosting framework (1) enables the selection of the optimal bias-variance tradeoff along the solution path, and (2) scales with high-dimensional problems. In simulations of DWI, we find that EBP yields better parameter estimates than a non-negative least squares (NNLS) approach, or the standard model used in DWI, the tensor model, which serves as the basis for diffusion tensor imaging (DTI) [5]. We demonstrate the utility of the method in DWI data acquired in parts of the brain containing crossings of multiple fascicles of nerve fibers.