Applications of fMRI in translational medicine and clinical practice

Test-retest reliability of amygdala response to emotional faces

In the current study, we evaluated the test-retest reliability of amygdala response using an emotional face-matching task that has been widely used to examine pathophysiology and treatment mechanisms in psychiatric populations. Activation within the fusiform face area (FFA) was also examined. Twenty-seven healthy volunteers completed a variation of the face-matching paradigm developed by Hariri et al. (2000) at two time points approximately 90 days apart. Estimates of test-retest reliability of amygdala response to fearful faces were moderate, whereas angry and happy faces showed poor reliability. Test-retest reliability of the FFA was moderate to strong, regardless of facial affect. Collectively, these findings indicate that the reliability of the BOLD MR signal in the amygdala varies substantially by facial affect. Efforts to improve measurement precision, enlarge sample sizes, or increase the number of assessment occasions seem warranted.

Clinical applications of the functional connectome

Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.

Technologies: preclinical imaging for drug development

Preclinical imaging with magnetic resonance imaging (MRI), computerised tomography (CT), ultrasound (US), positron emission tomography (PET) or single-photon emission computed tomography (SPECT) enable non-invasive measures of tissue structure, function or metabolism in vivo. The technologies can add value to preclinical studies by enabling dynamic pharmacological observations on the same animal and because of possibilities for relatively direct clinical translation. Potential benefits from the application of preclinical imaging should be considered routinely in drug development.

Rapid quantitative pharmacodynamic imaging by a novel method: theory, simulation testing and proof of principle

Pharmacological challenge imaging has mapped, but rarely quantified, the sensitivity of a biological system to a given drug. We describe a novel method called rapid quantitative pharmacodynamic imaging. This method combines pharmacokinetic-pharmacodynamic modeling, repeated small doses of a challenge drug over a short time scale, and functional imaging to rapidly provide quantitative estimates of drug sensitivity including EC 50 (the concentration of drug that produces half the maximum possible effect). We first test the method with simulated data, assuming a typical sigmoidal dose-response curve and assuming imperfect imaging that includes artifactual baseline signal drift and random error. With these few assumptions, rapid quantitative pharmacodynamic imaging reliably estimates EC 50 from the simulated data, except when noise overwhelms the drug effect or when the effect occurs only at high doses. In preliminary fMRI studies of primate brain using a dopamine agonist, the observed noise level is modest compared with observed drug effects, and a quantitative EC 50 can be obtained from some regional time-signal curves. Taken together, these results suggest that research and clinical applications for rapid quantitative pharmacodynamic imaging are realistic.

Pharmacological imaging as a tool to visualise dopaminergic neurotoxicity

Dopamine abnormalities underlie a wide variety of psychopathologies, including ADHD and schizophrenia. A new imaging technique, pharmacological magnetic resonance imaging (phMRI), is a promising non-invasive technique to visualize the dopaminergic system in the brain. In this review we explore the clinical potential of phMRI in detecting dopamine dysfunction or neurotoxicity, assess its strengths and weaknesses and identify directions for future research. Preclinically, phMRI is able to detect severe dopaminergic abnormalities quite similar to conventional techniques such as PET and SPECT. phMRI benefits from its high spatial resolution and the possibility to visualize both local and downstream effects of dopaminergic neurotransmission. In addition, it allows for repeated measurements and assessments in vulnerable populations. The major challenge is the complex interpretation of phMRI results. Future studies in patients with dopaminergic abnormalities need to confirm the currently reviewed preclinical findings to validate the technique in a clinical setting. Eventually, based on the current review we expect that phMRI can be of use in a clinical setting involving vulnerable populations (such as children and adolescents) for diagnosis and monitoring treatment efficacy. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'.

Neural and behavioral effects of a novel mu opioid receptor antagonist in binge-eating obese people

BACKGROUND

Binge eating is associated with obesity and has been conceptualized as "food addiction." However, this view has received only inconsistent support in humans, and limited evidence relates key neurocircuitry to the disorder. Moreover, relatively few studies have used pharmacologic functional magnetic resonance imaging to probe the underlying basis of altered eating behaviors.

METHODS

In a double-blind, placebo-controlled, parallel group study, we explored the effects of a potent mu-opioid receptor antagonist, GSK1521498, in obese individuals with moderate binge eating. Subjects were tested during a baseline placebo run-in period and retested after 28-days of drug (n = 21) or placebo (n = 21) treatment. Using functional magnetic resonance imaging and behavioral measures, we determined the drug's effects on brain responses to food images and, separately, on motivation to expend energy to view comparable images.

RESULTS

Compared with placebo, GSK1521498 was associated with a significant reduction in pallidum/putamen responses to pictures of high-calorie food and a reduction in motivation to view images of high-calorie food. Intriguingly, although motivational responding was reduced, subjective liking for the same images actually increased following drug treatment.

CONCLUSIONS

Stimulus-specific putamen/pallidal responses in obese people with binge eating are sensitive to altered mu-opioid function. This neuromodulation was accompanied by reductions in motivational responding, as measured by grip force, although subjective liking responses to the same stimuli actually increased. As well as providing evidence for a link between the opioid system and food-related behavior in binge-eating obese individuals, these results support a dissociation across measures of motivation and liking associated with food-related stimuli in these individuals.

Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI

Diffuse optical imaging (DOI) is increasingly becoming a valuable neuroimaging tool when fMRI is precluded. Recent developments in high-density diffuse optical tomography (HD-DOT) overcome previous limitations of sparse DOI systems, providing improved image quality and brain specificity. These improvements in instrumentation prompt the need for advancements in both i) realistic forward light modeling for accurate HD-DOT image reconstruction, and ii) spatial normalization for voxel-wise comparisons across subjects. Individualized forward light models derived from subject-specific anatomical images provide the optimal inverse solutions, but such modeling may not be feasible in all situations. In the absence of subject-specific anatomical images, atlas-based head models registered to the subject's head using cranial fiducials provide an alternative solution. In addition, a standard atlas is attractive because it defines a common coordinate space in which to compare results across subjects. The question therefore arises as to whether atlas-based forward light modeling ensures adequate HD-DOT image quality at the individual and group level. Herein, we demonstrate the feasibility of using atlas-based forward light modeling and spatial normalization methods. Both techniques are validated using subject-matched HD-DOT and fMRI data sets for visual evoked responses measured in five healthy adult subjects. HD-DOT reconstructions obtained with the registered atlas anatomy (i.e. atlas DOT) had an average localization error of 2.7mm relative to reconstructions obtained with the subject-specific anatomical images (i.e. subject-MRI DOT), and 6.6mm relative to fMRI data. At the group level, the localization error of atlas DOT reconstruction was 4.2mm relative to subject-MRI DOT reconstruction, and 6.1mm relative to fMRI. These results show that atlas-based image reconstruction provides a viable approach to individual head modeling for HD-DOT when anatomical imaging is not available.

Association of functional magnetic resonance imaging indices with postoperative language outcomes in patients with primary brain tumors

OBJECT

Functional MRI (fMRI) has the potential to be a useful presurgical planning tool to treat patients with primary brain tumor. In this study the authors retrospectively explored relationships between language-related postoperative outcomes in such patients and multiple factors, including measures estimated from task fMRI maps (proximity of lesion to functional activation area, or lesion-to-activation distance [LAD], and activation-based language lateralization, or lateralization index [LI]) used in the clinical setting for presurgical planning, as well as other factors such as patient age, patient sex, tumor grade, and tumor volume.

METHODS

Patient information was drawn from a database of patients with brain tumors who had undergone preoperative fMRI-based language mapping of the Broca and Wernicke areas. Patients had performed a battery of tasks, including word-generation tasks and a text-versus-symbols reading task, as part of a clinical fMRI protocol. Individually thresholded task fMRI activation maps had been provided for use in the clinical setting. These clinical imaging maps were used to retrospectively estimate LAD and LI for the Broca and Wernicke areas.

RESULTS

There was a relationship between postoperative language deficits and the proximity between tumor and Broca area activation (the LAD estimate), where shorter LADs were related to the presence of postoperative aphasia. Stratification by tumor location further showed that for posterior tumors within the temporal and parietal lobes, more bilaterally oriented Broca area activation (LI estimate close to 0) and a shorter Wernicke area LAD were associated with increased postoperative aphasia. Furthermore, decreasing LAD was related to decreasing LI for both Broca and Wernicke areas. Preoperative deficits were related to increasing patient age and a shorter Wernicke area LAD.

CONCLUSIONS

Overall, LAD and LI, as determined using fMRI in the context of these paradigms, may be useful indicators of postsurgical outcomes. Whereas tumor location may influence postoperative deficits, the results indicated that tumor proximity to an activation area might also interact with how the language network is affected as a whole by the lesion. Although the derivation of LI must be further validated in individual patients by using spatially specific statistical methods, the current results indicated that fMRI is a useful tool for predicting postoperative outcomes in patients with a single brain tumor.

Variability of clinical functional MR imaging results: a multicenter study

PURPOSE

To investigate intersite variability of clinical functional magnetic resonance (MR) imaging, including influence of task standardization on variability and use of various parameters to inform the clinician whether the reliability of a given functional localization is high or low.

MATERIALS AND METHODS

Local ethics committees approved the study; all participants gave written informed consent. Eight women and seven men (mean age, 40 years) were prospectively investigated at three experienced functional MR sites with 1.5- (two sites) or 3-T (one site) MR. Nonstandardized motor and highly standardized somatosensory versions of a frequently requested clinical task (localization of the primary sensorimotor cortex) were used. Perirolandic functional MR variability was assessed (peak activation variability, center of mass [COM] variability, intraclass correlation values, overlap ratio [OR], activation size ratio). Data quality measures for functional MR images included percentage signal change (PSC), contrast-to-noise ratio (CNR), and head motion parameters. Data were analyzed with analysis of variance and a correlation analysis.

RESULTS

Localization of perirolandic functional MR activity differed by 8 mm (peak activity) and 6 mm (COM activity) among sites. Peak activation varied up to 16.5 mm (COM range, 0.4-16.5 mm) and 45.5 mm (peak activity range, 1.8-45.5 mm). Signal strength (PSC, CNR) was significantly lower for the somatosensory task (mean PSC, 1.0% ± 0.5 [standard deviation]; mean CNR, 1.2 ± 0.4) than for the motor task (mean PSC, 2.4% ± 0.8; mean CNR, 2.9 ± 0.9) (P < .001, both). Intersite variability was larger with low signal strength (negative correlations between signal strength and peak activation variability) even if the task was highly standardized (mean OR, 22.0% ± 18.9 [somatosensory task] and 50.1% ± 18.8 [motor task]).

CONCLUSION

Clinical practice and clinical functional MR biomarker studies should consider that the center of task-specific brain activation may vary up to 16.5 mm, with the investigating site, and should maximize functional MR signal strength and evaluate reliability of local results with PSC and CNR.

Hierarchical random cellular neural networks for system-level brain-like signal processing

Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain's dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms.

Clinical applications of functional MR imaging

Functional magnetic resonance (fMR) imaging for neurosurgical planning has become the standard of care in centers where it is available. Although paradigms to measure eloquent cortices are not yet standardized, simple tasks elicit reliable maps for planning neurosurgical procedures. A patient-specific paradigm design will refine the usability of fMR imaging for prognostication and recovery of function. Certain pathologic conditions and technical issues limit the interpretation of fMR imaging maps in clinical use and should be considered carefully. However, fMR imaging for neurosurgical planning continues to provide insights into how the brain works and how it responds to pathologic insults.

Electrophysiological low-frequency coherence and cross-frequency coupling contribute to BOLD connectivity

Brain networks are commonly defined using correlations between blood oxygen level-dependent (BOLD) signals in different brain areas. Although evidence suggests that gamma-band (30-100 Hz) neural activity contributes to local BOLD signals, the neural basis of interareal BOLD correlations is unclear. We first defined a visual network in monkeys based on converging evidence from interareal BOLD correlations during a fixation task, task-free state, and anesthesia, and then simultaneously recorded local field potentials (LFPs) from the same four network areas in the task-free state. Low-frequency oscillations (<20 Hz), and not gamma activity, predominantly contributed to interareal BOLD correlations. The low-frequency oscillations also influenced local processing by modulating gamma activity within individual areas. We suggest that such cross-frequency coupling links local BOLD signals to BOLD correlations across distributed networks.

Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data

In recent years, there has been growing enthusiasm that functional magnetic resonance imaging (MRI) could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement-related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to (1) examine the impact of emerging techniques for controlling for “micro-movements,” and (2) provide novel insights into the neural correlates of ADHD subtypes. Using support vector machine (SVM)-based multivariate pattern analysis (MVPA) we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C) and Inattentive (ADHD-I) subtypes demonstrated some overlapping (particularly sensorimotor systems), but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that resting-state functional connectivity MRI (rs-fcMRI) data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.

Dynamic reorganization of digit representations in somatosensory cortex of nonhuman primates after spinal cord injury

Somatosensory cortices of adult primates reactivate over time after sensory loss. The time course and the neural mechanisms underlying the cortical reactivation are not well understood. Here we report that longitudinal high-resolution functional magnetic resonance imaging (fMRI) studies on anesthetized squirrel monkeys revealed dynamic reorganizations of digit activations in area 3b, within 2 months after severely disrupting afferent inputs by dorsal column section. We found that digit regions in which inputs were severely disrupted exhibited fMRI tactile responses. Reorganization was characterized by an early moving away phase and a late returning phase, as indicated by spatial shifts of individual digit activation centers in relation to the pre-lesion activation sites. Subsequent optical imaging studies confirmed fMRI activations, and dense microelectrode penetrations identified weak neuronal activity at the reactivated sites. Activation zones detected by fMRI and optical imaging were significantly larger in input-deprived than normal input single-digit regions and were larger than regions defined by neuronal spiking activity. This study captures the dynamic reorganization of digit representations after dorsal column lesions and reveals differences between functional imaging and microelectrode recording maps. Our observations suggest that subthreshold activity plays an important role in the reactivation of deafferented cortex and could promote behavioral recovery.

Prospective active marker motion correction improves statistical power in BOLD fMRI

Group level statistical maps of blood oxygenation level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) have become a basic measurement for much of systems, cognitive and social neuroscience. A challenge in making inferences from these statistical maps is the noise and potential confounds that arise from the head motion that occurs within and between acquisition volumes. This motion results in the scan plane being misaligned during acquisition, ultimately leading to reduced statistical power when maps are constructed at the group level. In most cases, an attempt is made to correct for this motion through the use of retrospective analysis methods. In this paper, we use a prospective active marker motion correction (PRAMMO) system that uses radio frequency markers for real-time tracking of motion, enabling on-line slice plane correction. We show that the statistical power of the activation maps is substantially increased using PRAMMO compared to conventional retrospective correction. Analysis of our results indicates that the PRAMMO acquisition reduces the variance without decreasing the signal component of the BOLD (beta). Using PRAMMO could thus improve the overall statistical power of fMRI based BOLD measurements, leading to stronger inferences of the nature of processing in the human brain.

Ethanol modulates the neurovascular coupling

Despite some evidence of the underlying molecular mechanisms the neuronal basis of ethanol-induced effects on the neurovascular coupling that forms the BOLD (blood oxygenation level dependent) signal is poorly understood. In a recent fMRI (functional magnetic resonance imaging) study monitoring ethanol-induced changes of the BOLD signal a reduction of the amplitude and a prolongation of the BOLD signal were observed. However, the BOLD signal is assumed to consist of a complex superposition of different underlying signals. To gain insight how ethanol influences stimulus efficacy, oxygen extraction, transit time and vessel-related parameters the fMRI time series from the sensori-motor and the visual cortex were analyzed using the balloon model. The results show a region-dependent decrease of the stimulus efficacy to trigger a post-stimulus neurovascular response as well as a prolongation of the transit time through the venous compartment. Oxygen extraction, feedback mechanisms and other vessel-related parameters were not affected. The results may be interpreted as follows: the overall mechanisms of the neurovascular coupling are still acting well at the moderate ethanol level of about 0.8‰ (in particular the vessel-related parts), but the potency to evoke a neurovascular response is already compromised most obviously in the supplementary motor area responsible for complex synchronizing and planning processes.

Seeking tools for image fusion between computed tomography, structural and functional magnetic resonance methods for applications in neurosurgery

OBJECTIVE

To evaluate tools for the fusion of images generated by tomography and structural and functional magnetic resonance imaging.

METHODS

Magnetic resonance and functional magnetic resonance imaging were performed while a volunteer who had previously undergone cranial tomography performed motor and somatosensory tasks in a 3-Tesla scanner. Image data were analyzed with different programs, and the results were compared.

RESULTS

We constructed a flow chart of computational processes that allowed measurement of the spatial congruence between the methods. There was no single computational tool that contained the entire set of functions necessary to achieve the goal.

CONCLUSION

The fusion of the images from the three methods proved to be feasible with the use of four free-access software programs (OsiriX, Register, MRIcro and FSL). Our results may serve as a basis for building software that will be useful as a virtual tool prior to neurosurgery.

Molecular brain imaging in the multimodality era

Multimodality molecular brain imaging encompasses in vivo visualization, evaluation, and measurement of cellular/molecular processes. Instrumentation and software developments over the past 30 years have fueled advancements in multimodality imaging platforms that enable acquisition of multiple complementary imaging outcomes by either combined sequential or simultaneous acquisition. This article provides a general overview of multimodality neuroimaging in the context of positron emission tomography as a molecular imaging tool and magnetic resonance imaging as a structural and functional imaging tool. Several image examples are provided and general challenges are discussed to exemplify complementary features of the modalities, as well as important strengths and weaknesses of combined assessments. Alzheimer's disease is highlighted, as this clinical area has been strongly impacted by multimodality neuroimaging findings that have improved understanding of the natural history of disease progression, early disease detection, and informed therapy evaluation.

Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study

BACKGROUND

To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

METHOD

One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls.

RESULTS

At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035).

CONCLUSIONS

We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.

Attention-related networks in Alzheimer's disease: a resting functional MRI study

In addition to memory deficits, attentional impairment is a common manifestation of Alzheimer's disease (AD). The present study examines the abnormalities of attention-related functional networks in AD using resting functional MRI (fMRI) technique and evaluates the sensitivity and specificity of these networks as potential biomarkers compared with the default mode network (DMN). Group independent component analysis (Group ICA) was applied to fMRI data from 15 AD patients and 16 normal healthy elderly controls (NC) to derive the dorsal attention network (DAN) and the ventral attention network (VAN) which are respectively responsible for the endogenous attention orienting ("top-down") process and the exogenous attention re-orienting ("bottom-up") process. Receiver operating characteristic (ROC) curve analysis was performed for activity in core regions within each of these networks. Functional connectivity analysis revealed disrupted DAN and preserved (less impaired) VAN in AD patients compared with NC, which might indicate impairment of a "top-down" and intact "bottom-up" attentional processing mechanisms in AD. ROC curve analysis suggested that activity in the left intraparietal sulcus and left frontal eye field from DAN as well as the posterior cingulate cortex from the DMN could serve as sensitive and specific biomarkers distinguishing AD from NC.

Biomedical informatics--a confluence of disciplines?

BACKGROUND

Biomedical informatics is a broad discipline that borrows many methods and techniques from other disciplines.

OBJECTIVE

To reflect a) on the character of biomedical informatics and to determine whether it is multi-disciplinary or inter-disciplinary; b) on the question whether biomedical informatics is more than the sum of its supporting disciplines and c) on the position of biomedical informatics with respect to related disciplines.

METHOD

Inviting an international group of experts in biomedical informatics and related disciplines on the occasion of the 50th anniversary of Methods of Information in Medicine to present their viewpoints.

RESULTS AND CONCLUSIONS

This paper contains the reflections of a number of the invited experts on the character of biomedical informatics. Most of the authors agree that biomedical informatics is an interdisciplinary field of study where researchers with different scientific backgrounds alone or in combination carry out research. Biomedical informatics is a very broad scientific field and still expanding, yet comprised of a constructive aspect (designing and building systems). One author expressed that the essence of biomedical informatics, as opposed to related disciplines, lies in the modelling of the biomedical content. Interdisciplinarity also has consequences for education. Maintaining rigid disciplinary structures does not allow for sufficient adaptability to capitalize on important trends nor to leverage the influences these trends may have on biomedical informatics. It is therefore important for students to become aware of research findings in related disciplines. In this respect, it was also noted that the fact that many scientific fields use different languages and that the research findings are stored in separate bibliographic databases makes it possible that potentially connected findings will never be linked, despite the fact that these findings were published. Bridges between the sciences are needed for the success of biomedical informatics.

Quantitative fMRI and oxidative neuroenergetics

The discovery of functional magnetic resonance imaging (fMRI) has greatly impacted neuroscience. The blood oxygenation level-dependent (BOLD) signal, using deoxyhemoglobin as an endogenous paramagnetic contrast agent, exposes regions of interest in task-based and resting-state paradigms. However the BOLD contrast is at best a partial measure of neuronal activity, because the functional maps obtained by differencing or correlations ignore the total neuronal activity in the baseline state. Here we describe how studies of brain energy metabolism at Yale, especially with (13)C magnetic resonance spectroscopy and related techniques, contributed to development of quantitative functional brain imaging with fMRI by providing a reliable measurement of baseline energy. This narrative takes us on a journey, from molecules to mind, with illuminating insights about neuronal-glial activities in relation to energy demand of synaptic activity. These results, along with key contributions from laboratories worldwide, comprise the energetic basis for quantitative interpretation of fMRI data.

The Neuro/PsyGRID calibration experiment: identifying sources of variance and bias in multicenter MRI studies

Calibration experiments precede multicenter trials to identify potential sources of variance and bias. In support of future imaging studies of mental health disorders and their treatment, the Neuro/PsyGRID consortium commissioned a calibration experiment to acquire functional and structural MRI from twelve healthy volunteers attending five centers on two occasions. Measures were derived of task activation from a working memory paradigm, fractal scaling (Hurst exponent) from resting fMRI, and grey matter distributions from T(1) -weighted sequences. At each intracerebral voxel a fixed-effects analysis of variance estimated components of variance corresponding to factors of center, subject, occasion, and within-occasion order, and interactions of center-by-occasion, subject-by-occasion, and center-by-subject, the latter (since there is no intervention) a surrogate of the expected variance of the treatment effect standard error across centers. A rank order test of between-center differences was indicative of crossover or noncrossover subject-by-center interactions. In general, factors of center, subject and error variance constituted >90% of the total variance, whereas occasion, order, and all interactions were generally <5%. Subject was the primary source of variance (70%-80%) for grey-matter, with error variance the dominant component for fMRI-derived measures. Spatially, variance was broadly homogenous with the exception of fractal scaling measures which delineated white matter, related to the flip angle of the EPI sequence. Maps of P values for the associated F-tests were also derived. Rank tests were highly significant indicating the order of measures across centers was preserved. In summary, center effects should be modeled at the voxel-level using existing and long-standing statistical recommendations.

The future of functional MRI in clinical medicine

In the last 20 years or so, functional MRI has matured very rapidly from being an experimental imaging method in the hands of a few labs to being a very widely available and widely used workhorse of cognitive neuroscience and clinical neuroscience research internationally. FMRI studies have had a considerable impact on our understanding of brain system phenotypes of neurological and psychiatric disorders; and some impact already on development of new therapeutics. However, the direct benefit of fMRI to individual patients with brain disorders has so far been minimal. Here I provide a personal perspective on what has already been achieved, and imagine how the further development of fMRI over the medium term might lead to even greater engagement with clinical medicine.

A hybrid procedure for detecting global treatment effects in multivariate clinical trials: theory and applications to fMRI studies

In multivariate clinical trials, a key research endpoint is ascertaining whether a candidate treatment is more efficacious than an established alternative. This global endpoint is clearly of high practical value for studies, such as those arising from neuroimaging, where the outcome dimensions are not only numerous but they are also highly correlated and the available sample sizes are typically small. In this paper, we develop a two-stage procedure testing the null hypothesis of global equivalence between treatments effects and demonstrate its application to analysing phase II neuroimaging trials. Prior information such as suitable statistics of historical data or suitably elicited expert clinical opinions are combined with data collected from the first stage of the trial to learn a set of optimal weights. We apply these weights to the outcome dimensions of the second-stage responses to form the linear combination z and t tests statistics while controlling the test's false positive rate. We show that the proposed tests hold desirable asymptotic properties and characterise their power functions under wide conditions. In particular, by comparing the power of the proposed tests with that of Hotelling's T(2), we demonstrate their advantages when sample sizes are close to the dimension of the multivariate outcome. We apply our methods to fMRI studies, where we find that, for sufficiently precise first stage estimates of the treatment effect, standard single-stage testing procedures are outperformed.

Alcohol induced epigenetic perturbations during the inflammatory stage of fracture healing

It is well recognized by orthopedic surgeons that fractures of alcoholics are more difficult to heal successfully and have a higher incidence of non-union, but the mechanism of alcohol's effect on fracture healing is unknown. In order to give direction for the study of the effects of alcohol on fracture healing, we propose to identify gene expression and microRNA changes during the early stages of fracture healing that might be attributable to alcohol consumption. As the inflammatory stage appears to be the most critical for successful fracture healing, this paper focuses on the events at day three following fracture or the stage of inflammation. Sprague-Dawley rats were placed on an ethanol-containing or pair-fed Lieber and DeCarli diet for four weeks prior to surgical fracture. Following insertion of a medullary pin, a closed mid-diaphyseal fracture was induced using a Bonnarens and Einhorn fracture device. At three days' post-fracture, the region of the fracture calluses was harvested from the right hind-limb. RNA was extracted and microarray analysis was conducted against the entire rat genome. There were 35 genes that demonstrated significant increased expression due to alcohol consumption and 20 that decreased due to alcohol. In addition, the expression of 20 microRNAs was increased and six decreased. In summary, while it is recognized that mRNA levels may or may not represent protein levels successfully produced by the cell, these studies reveal changes in gene expression that support the hypothesis that alcohol consumption affects events involved with inflammation. MicroRNAs are known to modulate mRNA and these findings were consistent with much of what was seen with mRNA microarray analysis, especially the involvement of smad4 which was demonstrated by mRNA microarray, microRNA and polymerase chain reaction.

Recent progress in high-resolution functional MRI

PURPOSE OF REVIEW

For functional MRI (fMRI), as for any imaging technique, the higher the spatial resolution, the more the details it can reveal. This review will discuss the factors restricting the spatial resolution of fMRI, describe high-resolution fMRI (HR-fMRI) applications in neuroscience and outline a few research areas for future HR-fMRI studies.

RECENT FINDINGS

HR-fMRI has been successfully used to map fine cortical architectures and reveal cortical laminar structures and subcortical structures. HR-fMRI has also played important roles in resolving controversies regarding modular representations in the ventral visual pathway and interpretations of multivariate pattern analysis results.

SUMMARY

Real-time HR-fMRI as well as high-resolution anatomical MRI may emerge as indispensable tools for surgical planning, diagnosis of neurological diseases and targeting of deep brain stimulation.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

Donepezil impairs memory in healthy older subjects: behavioural, EEG and simultaneous EEG/fMRI biomarkers

Rising life expectancies coupled with an increasing awareness of age-related cognitive decline have led to the unwarranted use of psychopharmaceuticals, including acetylcholinesterase inhibitors (AChEIs), by significant numbers of healthy older individuals. This trend has developed despite very limited data regarding the effectiveness of such drugs on non-clinical groups and recent work indicates that AChEIs can have negative cognitive effects in healthy populations. For the first time, we use a combination of EEG and simultaneous EEG/fMRI to examine the effects of a commonly prescribed AChEI (donepezil) on cognition in healthy older participants. The short- and long-term impact of donepezil was assessed using two double-blind, placebo-controlled trials. In both cases, we utilised cognitive (paired associates learning (CPAL)) and electrophysiological measures (resting EEG power) that have demonstrated high-sensitivity to age-related cognitive decline. Experiment 1 tested the effects of 5 mg/per day dosage on cognitive and EEG markers at 6-hour, 2-week and 4-week follow-ups. In experiment 2, the same markers were further scrutinised using simultaneous EEG/fMRI after a single 5 mg dose. Experiment 1 found significant negative effects of donepezil on CPAL and resting Alpha and Beta band power. Experiment 2 replicated these results and found additional drug-related increases in the Delta band. EEG/fMRI analyses revealed that these oscillatory differences were associated with activity differences in the left hippocampus (Delta), right frontal-parietal network (Alpha), and default-mode network (Beta). We demonstrate the utility of simple cognitive and EEG measures in evaluating drug responses after acute and chronic donepezil administration. The presentation of previously established markers of age-related cognitive decline indicates that AChEIs can impair cognitive function in healthy older individuals. To our knowledge this is the first study to identify the precise neuroanatomical origins of EEG drug markers using simultaneous EEG/fMRI. The results of this study may be useful for evaluating novel drugs for cognitive enhancement.

The effects of nicotine replacement on cognitive brain activity during smoking withdrawal studied with simultaneous fMRI/EEG

Impaired attention ('difficulty concentrating') is a cognitive symptom of nicotine withdrawal that may be an important contributor to smoking relapse. However, the neurobiological basis of this effect and the potentially beneficial effects of nicotine replacement therapy both remain unclear. We used functional MRI with simultaneous electroencephalogram (EEG) recording to define brain activity correlates of cognitive impairment with short-term smoking cessation in habitual smokers and the effects of nicotine replacement. We found that irrespective of treatment (ie nicotine or placebo) EEG α power was negatively correlated with increased activation during performance of a rapid visual information processing (RVIP) task in dorsolateral prefrontal, dorsal anterior cingulate, parietal, and insular cortices, as well as, caudate, and thalamus. Relative to placebo, nicotine replacement further increased the α-correlated activation across these regions. We also found that EEG α power was negatively correlated with RVIP-induced deactivation in regions comprising the 'default mode' network (ie angular gyrus, cuneus, precuneus, posterior cingulate, and ventromedial prefrontal cortex). These α-correlated deactivations were further reduced by nicotine. These findings confirm that effects of nicotine on cognition during short-term smoking cessation occur with modulation of neuronal sources common to the generation of both the blood oxygen-level-dependent and α EEG signals. Our observations thus demonstrate that nicotine replacement in smokers has direct pharmacological effects on brain neuronal activity modulating cognitive networks.

Functional neuroimaging of the oculomotor brainstem network in humans

The cortical systems involved in eye movement control in humans have been investigated extensively using fMRI. In contrast, there is virtually no data concerning the functional status of the human oculomotor brainstem nuclei. This lack of evidence has usually been explained by technical constraints of EPI based imaging and anatomical characteristics of the brainstem. Against this assumption, we successfully localised nuclei of the oculomotor system using high-resolution fMRI based on standard EPI sequences in a group of healthy subjects executing reflexive horizontal saccades. A random-effects group analysis revealed task-related BOLD increases in the superior colliculus, the oculomotor nucleus, the abducens nucleus and in the paramedian pontine reticular formation. This group analysis was complemented by individual positive findings in up to 94% of single subject analyses. A visual control paradigm led to increased signal levels in the superior colliculus consistent with its visual properties but no corresponding signal changes in other brainstem nuclei. These results are consistent with findings in animal studies and demonstrate the feasibility to detect BOLD signal increases associated with oculomotor tasks even in the human brainstem using conventional EPI imaging techniques.

Novel frontiers in ultra-structural and molecular MRI of the brain

PURPOSE OF REVIEW

Recent developments in the MRI of the brain continue to expand its use in basic and clinical neuroscience. This review highlights some areas of recent progress.

RECENT FINDINGS

Higher magnetic field strengths and improved signal detectors have allowed improved visualization of the various properties of the brain, facilitating the anatomical definition of function-specific areas and their connections. For example, by sensitizing the MRI signal to the magnetic susceptibility of tissue, it is starting to become possible to reveal the laminar structure of the cortex and identify millimeter-scale fiber bundles. Using exogenous contrast agents, and innovative ways to manipulate contrast, it is becoming possible to highlight specific fiber tracts and cell populations. These techniques are bringing us closer to understanding the evolutionary blueprint of the brain, improving the detection and characterization of disease, and help to guide treatment.

SUMMARY

Recent MRI techniques are leading to more detailed and more specific contrast in the study of the brain.

Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia II: developing imaging biomarkers to enhance treatment development for schizophrenia and related disorders

The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative, funded by an R13 from the National Institute of Mental Health, seeks to enhance translational research in treatment development for impaired cognition in schizophrenia by developing tools from cognitive neuroscience into useful measures of treatment effects on behavior and brain function. An initial series of meetings focused on the selection of a new set of tasks from cognitive neuroscience for the measurement of treatment effects on specific cognitive and neural systems. Subsequent validation and optimization studies are underway and a subset of validated measures with well-characterized psychometric properties will be generally available in 2011. This article describes results of the first meeting of the second phase of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia, which seeks to develop imaging biomarkers and improved animal models to enhance translational research. In this meeting, we considered issues related to the use of methods such as functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and transcranial magnetic simulation as biomarkers for treatment development. We explored the biological nature of the signals measured by each method, their validity and reliability as measures of cognition-related neural activity, potential confounds related to drug effects on the signal of interest, and conceptual, methodological, and pragmatic issues related to their use in preclinical, first into human, and multicenter phase II and III studies. This overview article describes the background and goals of the meeting together with a summary of the major issues discussed in more detail in the accompanying articles appearing in this issue of Biological Psychiatry.

Can Functional Magnetic Resonance Imaging Improve Success Rates in CNS Drug Discovery?

INTRODUCTION: The bar for developing new treatments for CNS disease is getting progressively higher and fewer novel mechanisms are being discovered, validated and developed. The high costs of drug discovery necessitate early decisions to ensure the best molecules and hypotheses are tested in expensive late stage clinical trials. The discovery of brain imaging biomarkers that can bridge preclinical to clinical CNS drug discovery and provide a 'language of translation' affords the opportunity to improve the objectivity of decision-making. AREAS COVERED: This review discusses the benefits, challenges and potential issues of using a science based biomarker strategy to change the paradigm of CNS drug development and increase success rates in the discovery of new medicines. The authors have summarized PubMed and Google Scholar based publication searches to identify recent advances in functional, structural and chemical brain imaging and have discussed how these techniques may be useful in defining CNS disease state and drug effects during drug development. EXPERT OPINION: The use of novel brain imaging biomarkers holds the bold promise of making neuroscience drug discovery smarter by increasing the objectivity of decision making thereby improving the probability of success of identifying useful drugs to treat CNS diseases. Functional imaging holds the promise to: (1) define pharmacodynamic markers as an index of target engagement (2) improve translational medicine paradigms to predict efficacy; (3) evaluate CNS efficacy and safety based on brain activation; (4) determine brain activity drug dose-response relationships and (5) provide an objective evaluation of symptom response and disease modification.

Paradigm shift in translational neuroimaging of CNS disorders

During the last two decades, functional neuroimaging technology, especially functional magnetic resonance imaging (fMRI), has improved tremendously, with new attention towards resting-state functional connectivity of the brain. This development has allowed scientists to study changes in brain structure and function, and probe these two properties under conditions of evoked stimulation, disease and drug administration. In the domain of functional imaging, the identification and characterization of central nervous system (CNS) functional networks have emerged as potential biomarkers for CNS disorders in humans. Recent attempts to translate clinical neuroimaging methodology to preclinical studies have also been carried out, which offer new opportunities in translational neuroscience research. In this paper, we review recent developments in structural and functional MRI and their use to probe functional connectivity in various CNS disorders such as schizophrenia, mood disorders, Alzheimer's disease (AD) and pain.

A joint acquisition-reconstruction paradigm for correcting inhomogeneity artifacts in MR echo planar imaging

One of the main sources of signal degradation in rapid MR acquisitions, such as Echo Planar Imaging (EPI), is magnetic field variations caused by field inhomogeneities and susceptibility gradients. If unaccounted for during the reconstruction process, this spatially-varying field can cause severe image artifacts. In this paper, we show that correcting for the resulting degradations can be formulated as a blind image deconvolution problem. We propose a novel joint acquisition-processing paradigm to solve this problem. We describe a practical implementation of this paradigm using a multi-image acquisition strategy and a corresponding joint estimation-reconstruction algorithm. The estimation step computes the spatial distribution of the field maps, while the reconstruction step yields a Minimum Mean Squared Error (MMSE) estimate of the imaged slice. Our simulations show that this proposed joint acquisition-reconstruction method is robust and efficient, offering factors of improvement in the quality of the reconstructed image as compared to other traditional methods.

Longitudinal functional magnetic resonance imaging in animal models

Functional magnetic resonance imaging (fMRI) has had an essential role in furthering our understanding of brain physiology and function. fMRI techniques are nowadays widely applied in neuroscience research, as well as in translational and clinical studies. The use of animal models in fMRI studies has been fundamental in helping elucidate the mechanisms of cerebral blood-flow regulation, and in the exploration of basic neuroscience questions, such as the mechanisms of perception, behavior, and cognition. Because animals are inherently non-compliant, most fMRI performed to date have required the use of anesthesia, which interferes with brain function and compromises interpretability and applicability of results to our understanding of human brain function. An alternative approach that eliminates the need for anesthesia involves training the animal to tolerate physical restraint during the data acquisition. In the present chapter, we review these two different approaches to obtaining fMRI data from animal models, with a specific focus on the acquisition of longitudinal data from the same subjects.

Pathophysiological interference with neurovascular coupling - when imaging based on hemoglobin might go blind

Assessing neuronal activity by non-invasive functional brain imaging techniques which are based on the hemodynamic response depends totally on the physiological cascade of metabolism and blood flow. At present, functional brain imaging with near infrared spectroscopy (NIRS) or BOLD-fMRI is widely used in cognitive neuroscience in healthy subjects where neurovascular coupling and cerebrovascular reactivity can be assumed to be intact. Local activation studies as well as studies investigating functional connectivity between brain regions of the resting brain provide a rapidly increasing body of knowledge on brain function in humans and animals. Furthermore, functional NIRS and MRI techniques are increasingly being used in patients with severe brain diseases and this use might gain more and more importance for establishing their use in the clinical routine. However, more and more experimental evidence shows that changes in baseline physiological parameters, pharmacological interventions, or disease-related vascular changes may significantly alter the normal response of blood flow and blood oxygenation and thus may lead to misinterpretation of neuronal activity. In this article we present examples of recent experimental findings on pathophysiological changes of neurovascular coupling parameters in animals and discuss their potential implications for functional imaging based on hemodynamic signals such as fNIRS or BOLD-fMRI. To enable correct interpretation of neuronal activity by vascular signals, future research needs to deepen our understanding of the basic mechanisms of neurovascular coupling and the specific characteristics of disturbed neurovascular coupling in the diseased brain.

Regional and hemispheric determinants of language laterality: implications for preoperative fMRI

Language is typically a function of the left hemisphere but the right hemisphere is also essential in some healthy individuals and patients. This inter-subject variability necessitates the localization of language function, at the individual level, prior to neurosurgical intervention. Such assessments are typically made by comparing left and right hemisphere language function to determine "language lateralization" using clinical tests or fMRI. Here, we show that language function needs to be assessed at the region and hemisphere specific level, because laterality measures can be misleading. Using fMRI data from 82 healthy participants, we investigated the degree to which activation for a semantic word matching task was lateralized in 50 different brain regions and across the entire cortex. This revealed two novel findings. First, the degree to which language is lateralized across brain regions and between subjects was primarily driven by differences in right hemisphere activation rather than differences in left hemisphere activation. Second, we found that healthy subjects who have relatively high left lateralization in the angular gyrus also have relatively low left lateralization in the ventral precentral gyrus. These findings illustrate spatial heterogeneity in language lateralization that is lost when global laterality measures are considered. It is likely that the complex spatial variability we observed in healthy controls is more exaggerated in patients with brain damage. We therefore highlight the importance of investigating within hemisphere regional variations in fMRI activation, prior to neuro-surgical intervention, to determine how each hemisphere and each region contributes to language processing.

Neurovascular and Neurometabolic Couplings in Dynamic Calibrated fMRI: Transient Oxidative Neuroenergetics for Block-Design and Event-Related Paradigms

Functional magnetic resonance imaging (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping brain activity. Interest in quantitative fMRI has renewed awareness in importance of oxidative neuroenergetics, as reflected by cerebral metabolic rate of oxygen consumption(CMRO2), for supporting brain function. Relationships between BOLD signal and the underlying neurophysiological parameters have been elucidated to allow determination of dynamic changes inCMRO2 by "calibrated fMRI," which require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and volume (CBV). But how doCMRO2 changes, steady-state or transient, derived from calibrated fMRI compare with neural activity recordings of local field potential (LFP) and/or multi-unit activity (MUA)? Here we discuss recent findings primarily from animal studies which allow high magnetic fields studies for superior BOLD sensitivity as well as multi-modal CBV and CBF measurements in conjunction with LFP and MUA recordings from activated sites. A key observation is that while relationships between neural activity and sensory stimulus features range from linear to non-linear, associations between hyperemic components (BOLD, CBF, CBV) and neural activity (LFP, MUA) are almost always linear. More importantly, the results demonstrate good agreement between the changes inCMRO2 and independent measures of LFP or MUA. The tight neurovascular and neurometabolic couplings, observed from steady-state conditions to events separated by <200 ms, suggest rapid oxygen equilibration between blood and tissue pools and thus calibrated fMRI at high magnetic fields can provide high spatiotemporal mapping ofCMRO2 changes.

Functional Magnetic Resonance Imaging and Diffusion Tensor Tractography Incorporated Into an Intraoperative 3-Dimensional Ultrasound-Based Neuronavigation System

BACKGROUND

Functional neuronavigation with intraoperative 3-dimensional (3D) ultrasound may facilitate safer brain lesion resections than conventional neuronavigation.

OBJECTIVE

In this study, functional magnetic resonance imaging (fMRI) and diffusion tensor tractography (DTT) were used to map eloquent areas. We assessed the use of fMRI and DTT for preoperative assessments and determined whether using these data together with 3D ultrasound during surgery enabled safer lesion resection.

METHODS

We reviewed 51 consecutive patients with intracranial lesions in whom fMRI with or without DTT was used to map eloquent areas. To assess a possible impact of fMRI/DTT, we reviewed and analyzed the quality of the fMRI/DTT data, any change in therapeutic strategies, lesion to eloquent area distance (LEAD), extent of resection, and clinical outcome.

RESULTS

As a result of the fMRI/DTT mapping, the therapeutic strategies were changed in 4 patients. The median tumor residue for glioma patients was 11% (n = 33) and 0% for nonglioma lesions (n = 12). For gliomas, there was a significant correlation between decreasing LEAD and increasing tumor residue. Of the glioma patients, 42% underwent gross total resection (≥ 95%) and 12% suffered neurological worsening after surgery as a result of complications. Of glioma patients with an LEAD of ≤ 5 mm, 24% underwent gross total resection and 10% experienced neurological deterioration.

CONCLUSION

This study demonstrates that preoperative fMRI and DTT had direct consequences for therapeutic strategies and indicates their impact on intraoperative strategies to spare eloquent cortex and tracts. Functional neuronavigation combined with intraoperative 3D ultrasound can, in most patients, enable resection of brain lesions with general anesthesia without jeopardizing neurological function.

Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping

Neuroimaging advances have given rise to major progress in neurosciences and neurology, as ever more subtle and specific imaging methods reveal new aspects of the brain. One major limitation of current methods is the spatial scale of the information available. We present an approach to gain spatial resolution using post-processing methods based on diffusion MRI fiber-tracking, to reveal structures beyond the resolution of the acquired imaging voxel; we term such a method as super-resolution track-density imaging (TDI). A major unmet challenge in imaging is the identification of abnormalities in white matter as a cause of illness; super-resolution TDI is shown to produce high-quality white matter images, with high spatial resolution and outstanding anatomical contrast. A unique property of these maps is demonstrated: their spatial resolution and signal-to-noise ratio can be tailored depending on the chosen image resolution and total number of fiber-tracks generated. Super-resolution TDI should greatly enhance the study of white matter in disorders of the brain and mind.