Reproducibility of functional MR imaging: preliminary results of prospective multi-institutional study performed by Biomedical Informatics Research Network

A model-based approach to assess reproducibility for large-scale high-throughput MRI-based studies

Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.

Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed

Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

Automatic identification of atypical clinical fMRI results

Purpose

Functional MRI is not routinely used for neurosurgical planning despite potential important advantages, due to difficulty of determining quality. We introduce a novel method for objective evaluation of fMRI scan quality, based on activation maps. A template matching analysis (TMA) is presented and tested on data from two clinical fMRI protocols, performed by healthy controls in seven clinical centers. Preliminary clinical utility is tested with data from low-grade glioma patients.

Methods

Data were collected from 42 healthy subjects from seven centers, with standardized finger tapping (FT) and verb generation (VG) tasks. Copies of these “typical” data were deliberately analyzed incorrectly to assess feasibility of identifying them as “atypical.” Analyses of the VG task administered to 32 tumor patients assessed sensitivity of the TMA method to anatomical abnormalities.

Results

TMA identified all atypical activity maps for both tasks, at the cost of incorrectly classifying 3.6 (VG)–6.5% (FT) of typical maps as atypical. For patients, the average TMA was significantly higher than atypical healthy scans, despite localized anatomical abnormalities caused by a tumor.

Conclusion

This study supports feasibility of TMA for objective identification of atypical activation patterns for motor and verb generation fMRI protocols. TMA can facilitate the use and evaluation of clinical fMRI in hospital settings that have limited access to fMRI experts. In a clinical setting, this method could be applied to automatically flag fMRI scans showing atypical activation patterns for further investigation to determine whether atypicality is caused by poor scan data quality or abnormal functional topography.

Standardization of Small Animal Imaging-Current Status and Future Prospects

The benefit of small animal imaging is directly linked to the validity and reliability of the collected data. If the data (regardless of the modality used) are not reproducible and/or reliable, then the outcome of the data is rather questionable. Therefore, standardization of the use of small animal imaging equipment, as well as of animal handling in general, is of paramount importance. In a recent paper, guidance for efficient small animal imaging quality control was offered and discussed, among others, the use of phantoms in setting up a quality control program (Osborne et al. 2016). The same phantoms can be used to standardize image quality parameters for multi-center studies or multi-scanners within center studies. In animal experiments, the additional complexity due to animal handling needs to be addressed to ensure standardized imaging procedures. In this review, we will address the current status of standardization in preclinical imaging, as well as potential benefits from increased levels of standardization.

Effects of acute Δ9-tetrahydrocannabinol on next-day extinction recall is mediated by post-extinction resting-state brain dynamics

We have previously demonstrated that an acute dose of Δ9-tetrahydrocanninbinol (THC), administered prior to extinction learning, facilitates later recall of extinction learning and modulates the underlying neural circuitry, including the ventromedial prefrontal cortex (vmPFC), hippocampus (HPC), and amygdala (AMYG). It remains unknown whether THC-induced changes in fear-extinction neural circuitry can be detected following extinction learning, which may reflect ongoing processes involved consolidation of the extinction memory. To address this gap, we used a randomized, double-blind, placebo-controlled, between-subjects design to compare acute pharmacological effects of THC (7.5 mg) vs. placebo (PBO) on post-extinction resting-state functional connectivity (RS-FC) within fear-extinction circuitry in 77 healthy adults (THC = 40; PBO = 37). RS-FC was examined between vmPFC, HPC, and AMYG using two complementary approaches: 1) static RS-FC (average correlation in ROI-ROI pairs across the entire scan); and 2) dynamic (i.e., time-varying) RS-FC (sliding window correlation time series' variance). RS-FC was then linked to behavioral and brain measures of extinction recall. Compared to PBO, THC administration was associated with lower AMYG-HPC static RS-FC, but higher AMYG-vmPFC dynamic RS-FC. Lower AMYG-HPC static RS-FC was associated with higher HPC activation, as well as, better extinction recall. Moreover, lower AMYG-HPC static RS-FC following extinction learning mediated the link between THC administration and extinction recall. Post-extinction RS-FC patterns may reflect sustained effects of THC on fear-extinction circuitry even in the absence of an overt task, and/or effects of ongoing processes that serve to strengthen the neural connections supporting the consolidation of the memory and better extinction recall.

Multicenter stability of resting state fMRI in the detection of Alzheimer's disease and amnestic MCI

Background

In monocentric studies, patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia exhibited alterations of functional cortical connectivity in resting-state functional MRI (rs-fMRI) analyses. Multicenter studies provide access to large sample sizes, but rs-fMRI may be particularly sensitive to multiscanner effects.

Methods

We used data from five centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 367 cases, including AD patients, MCI patients and healthy older controls, to assess the influence of the distributed acquisition on the group effects. We calculated accuracy of group discrimination based on whole brain functional connectivity of the posterior cingulate cortex (PCC) using pooled samples as well as second-level analyses across site-specific group contrast maps.

Results

We found decreased functional connectivity in AD patients vs. controls, including clusters in the precuneus, inferior parietal cortex, lateral temporal cortex and medial prefrontal cortex. MCI subjects showed spatially similar, but less pronounced, differences in PCC connectivity when compared to controls. Group discrimination accuracy for AD vs. controls (MCI vs. controls) in the test data was below 76% (72%) based on the pooled analysis, and even lower based on the second level analysis stratified according to scanner. Only a subset of quality measures was useful to detect relevant scanner effects.

Conclusions

Multicenter rs-fMRI analysis needs to employ strict quality measures, including visual inspection of all the data, to avoid seriously confounded group effects. While pending further confirmation in biomarker stratified samples, these findings suggest that multicenter acquisition limits the use of rs-fMRI in AD and MCI diagnosis.

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Diagnostic accuracy of multicenter rs-fMRI in AD and MCI

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Quality metrics for multicenter rs-fMRI that should be used

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Quality metrics for multicenter rs-fMRI that should not be used

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Multicenter rs-fMRI will have limited diagnostic use in clinical routine diagnosis

Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia

BACKGROUND

The approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG) often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI) interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT) in PD/AG.

METHODS

This analysis is based on pretreatment fMRI data during an interoceptive challenge from a multicenter trial of the German PANIC-NET. Patients with DSM-IV PD/AG were dichotomized as responders (n = 30) or non-responders (n = 29) based on the primary outcome (Hamilton Anxiety Scale Reduction ≥50%) after 6 weeks of CBT (2 h/week). fMRI parametric maps were used as features for response classification with linear support vector machines (SVM) with or without automated feature selection. Predictive accuracies were assessed using cross validation and permutation testing. The influence of methodological parameters and the predictive ability for specific interoception-related symptom reduction were further evaluated.

RESULTS

SVM did not reach sufficient overall predictive accuracies (38.0-54.2%) for anxiety reduction in the primary outcome. In the exploratory analyses, better accuracies (66.7%) were achieved for predicting interoception-specific symptom relief as an alternative outcome domain. Subtle information regarding this alternative response criterion but not the primary outcome was revealed by post hoc univariate comparisons.

CONCLUSION

In contrast to reports on other neurofunctional probes, SVM based on an interoception paradigm was not able to reliably predict individual response to CBT. Results speak against the clinical applicability of this technique.

Neural correlates of three types of negative life events during angry face processing in adolescents

Negative life events (NLE) contribute to anxiety and depression disorders, but their relationship with brain functioning in adolescence has rarely been studied. We hypothesized that neural response to social threat would relate to NLE in the frontal-limbic emotional regions. Participants (N = 685) were drawn from the Imagen database of 14-year-old community adolescents recruited in schools. They underwent functional MRI while viewing angry and neutral faces, as a probe to neural response to social threat. Lifetime NLEs were assessed using the 'distress', 'family' and 'accident' subscales from a life event dimensional questionnaire. Relationships between NLE subscale scores and neural response were investigated. Links of NLE subscales scores with anxiety or depression outcomes at the age of 16 years were also investigated. Lifetime 'distress' positively correlated with ventral-lateral orbitofrontal and temporal cortex activations during angry face processing. 'Distress' scores correlated with the probabilities of meeting criteria for Generalized Anxiety Disorder or Major Depressive Disorder at the age of 16 years. Lifetime 'family' and 'accident' scores did not relate with neural response or follow-up conditions, however. Thus, different types of NLEs differentially predicted neural responses to threat during adolescence, and differentially predicted a de novo internalizing condition 2 years later. The deleterious effect of self-referential NLEs is suggested.

Between- and within-site variability of fMRI localizations

This study provides first data about the spatial variability of fMRI sensorimotor localizations when investigating the same subjects at different fMRI sites. Results are comparable to a previous patient study. We found a median between-site variability of about 6 mm independent of task (motor or sensory) and experimental standardization (high or low). An intraclass correlation coefficient analysis using data quality measures indicated a major influence of the fMRI site on variability. In accordance with this, within-site localization variability was considerably lower (about 3 mm). We conclude that the fMRI site is a considerable confound for localization of brain activity. However, when performed by experienced clinical fMRI experts, brain pathology does not seem to have a relevant impact on the reliability of fMRI localizations. Hum Brain Mapp 37:2151-2160, 2016. © 2016 Wiley Periodicals, Inc.

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI

Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

Neural Correlates of Schizophrenia Negative Symptoms: Distinct Subtypes Impact Dissociable Brain Circuits

BACKGROUND

The negative symptoms of schizophrenia include deficits in emotional expression and motivation. These deficits are stable over the course of illness and respond poorly to current medications. Previous studies have focused on negative symptoms as a single category; however, individual symptoms might be related to separate neurological disturbances. We analyzed data from the Functional Biomedical Informatics Research Network dataset to explore the relationship between individual negative symptoms and functional brain activity during an auditory oddball task.

METHODS

Functional magnetic resonance imaging was conducted on 89 schizophrenia patients and 106 healthy controls during a two-tone auditory oddball task. Blood oxygenation level-dependent (BOLD) signal during the target tone was correlated with severity of five negative symptom domains from the Scale for the Assessment of Negative Symptoms.

RESULTS

The severity of alogia, avolition/apathy and anhedonia/asociality was negatively correlated with BOLD activity in distinct sets of brain regions associated with processing of the target tone, including basal ganglia, thalamus, insular cortex, prefrontal cortex, posterior cingulate and parietal cortex.

CONCLUSIONS

Individual symptoms were related to different patterns of functional activation during the oddball task, suggesting that individual symptoms might arise from distinct neural mechanisms. This work has potential to inform interventions that target these symptom-related neural disruptions.

Reliability of an fMRI paradigm for emotional processing in a multisite longitudinal study

Multisite neuroimaging studies can facilitate the investigation of brain-related changes in many contexts, including patient groups that are relatively rare in the general population. Though multisite studies have characterized the reliability of brain activation during working memory and motor functional magnetic resonance imaging tasks, emotion processing tasks, pertinent to many clinical populations, remain less explored. A traveling participants study was conducted with eight healthy volunteers scanned twice on consecutive days at each of the eight North American Longitudinal Prodrome Study sites. Tests derived from generalizability theory showed excellent reliability in the amygdala ( Eρ2 = 0.82), inferior frontal gyrus (IFG; Eρ2 = 0.83), anterior cingulate cortex (ACC; Eρ2 = 0.76), insula ( Eρ2 = 0.85), and fusiform gyrus ( Eρ2 = 0.91) for maximum activation and fair to excellent reliability in the amygdala ( Eρ2 = 0.44), IFG ( Eρ2 = 0.48), ACC ( Eρ2 = 0.55), insula ( Eρ2 = 0.42), and fusiform gyrus ( Eρ2 = 0.83) for mean activation across sites and test days. For the amygdala, habituation ( Eρ2 = 0.71) was more stable than mean activation. In a second investigation, data from 111 healthy individuals across sites were aggregated in a voxelwise, quantitative meta-analysis. When compared with a mixed effects model controlling for site, both approaches identified robust activation in regions consistent with expected results based on prior single-site research. Overall, regions central to emotion processing showed strong reliability in the traveling participants study and robust activation in the aggregation study. These results support the reliability of blood oxygen level-dependent signal in emotion processing areas across different sites and scanners and may inform future efforts to increase efficiency and enhance knowledge of rare conditions in the population through multisite neuroimaging paradigms.

Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: analysis from the North American Prodrome Longitudinal Study

Multi-site neuroimaging studies offer an efficient means to study brain functioning in large samples of individuals with rare conditions; however, they present new challenges given that aggregating data across sites introduces additional variability into measures of interest. Assessing the reliability of brain activation across study sites and comparing statistical methods for pooling functional data are critical to ensuring the validity of aggregating data across sites. The current study used two samples of healthy individuals to assess the feasibility and reliability of aggregating multi-site functional magnetic resonance imaging (fMRI) data from a Sternberg-style verbal working memory task. Participants were recruited as part of the North American Prodrome Longitudinal Study (NAPLS), which comprises eight fMRI scanning sites across the United States and Canada. In the first study sample (n=8), one participant from each home site traveled to each of the sites and was scanned while completing the task on two consecutive days. Reliability was examined using generalizability theory. Results indicated that blood oxygen level-dependent (BOLD) signal was reproducible across sites and was highly reliable, or generalizable, across scanning sites and testing days for core working memory ROIs (generalizability ICCs=0.81 for left dorsolateral prefrontal cortex, 0.95 for left superior parietal cortex). In the second study sample (n=154), two statistical methods for aggregating fMRI data across sites for all healthy individuals recruited as control participants in the NAPLS study were compared. Control participants were scanned on one occasion at the site from which they were recruited. Results from the image-based meta-analysis (IBMA) method and mixed effects model with site covariance method both showed robust activation in expected regions (i.e. dorsolateral prefrontal cortex, anterior cingulate cortex, supplementary motor cortex, superior parietal cortex, inferior temporal cortex, cerebellum, thalamus, basal ganglia). Quantification of the similarity of group maps from these methods confirmed a very high (96%) degree of spatial overlap in results. Thus, brain activation during working memory function was reliable across the NAPLS sites and both the IBMA and mixed effects model with site covariance methods appear to be valid approaches for aggregating data across sites. These findings indicate that multi-site functional neuroimaging can offer a reliable means to increase power and generalizability of results when investigating brain function in rare populations and support the multi-site investigation of working memory function in the NAPLS study, in particular.

Are power calculations useful? A multicentre neuroimaging study

There are now many reports of imaging experiments with small cohorts of typical participants that precede large-scale, often multicentre studies of psychiatric and neurological disorders. Data from these calibration experiments are sufficient to make estimates of statistical power and predictions of sample size and minimum observable effect sizes. In this technical note, we suggest how previously reported voxel-based power calculations can support decision making in the design, execution and analysis of cross-sectional multicentre imaging studies. The choice of MRI acquisition sequence, distribution of recruitment across acquisition centres, and changes to the registration method applied during data analysis are considered as examples. The consequences of modification are explored in quantitative terms by assessing the impact on sample size for a fixed effect size and detectable effect size for a fixed sample size. The calibration experiment dataset used for illustration was a precursor to the now complete Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS). Validation of the voxel-based power calculations is made by comparing the predicted values from the calibration experiment with those observed in MRC-AIMS. The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation. In summary, power calculations offer a validated, quantitative means of making informed choices on important factors that influence the outcome of studies that consume significant resources.

How to produce personality neuroscience research with high statistical power and low additional cost

Personality neuroscience involves examining relations between cognitive or behavioral variability and neural variables like brain structure and function. Such studies have uncovered a number of fascinating associations but require large samples, which are expensive to collect. Here, we propose a system that capitalizes on neuroimaging data commonly collected for separate purposes and combines it with new behavioral data to test novel hypotheses. Specifically, we suggest that groups of researchers compile a database of structural (i.e., anatomical) and resting-state functional scans produced for other task-based investigations and pair these data with contact information for the participants who contributed the data. This contact information can then be used to collect additional cognitive, behavioral, or individual-difference data that are then reassociated with the neuroimaging data for analysis. This would allow for novel hypotheses regarding brain-behavior relations to be tested on the basis of large sample sizes (with adequate statistical power) for low additional cost. This idea can be implemented at small scales at single institutions, among a group of collaborating researchers, or perhaps even within a single lab. It can also be implemented at a large scale across institutions, although doing so would entail a number of additional complications.

Task-modulated coactivation of vergence neural substrates

While functional magnetic resonance imaging (fMRI) has identified which regions of interests (ROIs) are functionally active during a vergence movement (inward or outward eye rotation), task-modulated coactivation between ROIs is less understood. This study tested the following hypotheses: (1) significant task-modulated coactivation would be observed between the frontal eye fields (FEFs), the posterior parietal cortex (PPC), and the cerebellar vermis (CV); (2) significantly more functional activity and task-modulated coactivation would be observed in binocularly normal controls (BNCs) compared with convergence insufficiency (CI) subjects; and (3) after vergence training, the functional activity and task-modulated coactivation would increase in CIs compared with their baseline measurements. A block design of sustained fixation versus vergence eye movements stimulated activity in the FEFs, PPC, and CV. fMRI data from four CI subjects before and after vergence training were compared with seven BNCs. Functional activity was assessed using the blood oxygenation level dependent (BOLD) percent signal change. Task-modulated coactivation was assessed using an ROI-based task-modulated coactivation analysis that revealed significant correlation between the FEF, PPC, and CV ROIs. Prior to vergence training, the CIs had a reduced BOLD percent signal change compared with BNCs for the CV (p<0.05), FEFs, and PPC (p<0.01). The BOLD percent signal change increased within the CV, FEF, and PPC ROIs (p<0.001) as did the task-modulated coactivation between the FEFs and CV as well as the PPC and CV (p<0.05) when comparing the CI pre- and post-training datasets. Results from the Convergence Insufficiency Symptom Survey were correlated to the percent BOLD signal change from the FEFs and CV (p<0.05).

Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery

OBJECTIVE

The advent of functional MRI (fMRI) enables the identification of brain regions recruited for specific behavioral tasks. Most fMRI studies focus on group effects in single tasks, which limits applicability where assessment of individual differences and multiple brain systems is needed.

METHOD

We demonstrate the feasibility of concurrently measuring fMRI activation patterns and performance on a computerized neurocognitive battery (CNB) in 212 healthy individuals at 2 sites. Cross-validated sparse regression of regional brain amplitude and extent of activation were used to predict concurrent performance on 6 neurocognitive tasks: abstraction/mental flexibility, attention, emotion processing, and verbal, face, and spatial memory.

RESULTS

Brain activation was task responsive and domain specific, as reported in previous single-task studies. Prediction of performance was robust for most tasks, particularly for abstraction/mental flexibility and visuospatial memory.

CONCLUSIONS

The feasibility of administering a comprehensive neuropsychological battery in the scanner was established, and task-specific brain activation patterns improved prediction beyond demographic information. This benchmark index of performance-associated brain activation can be applied to link brain activation with neurocognitive performance during standardized testing. This first step in standardizing a neurocognitive battery for use in fMRI may enable quantitative assessment of patients with brain disorders across multiple cognitive domains. Such data may facilitate identification of neural dysfunction associated with poor performance, allow for identification of individuals at risk for brain disorders, and help guide early intervention and rehabilitation of neurocognitive deficits.

Use of functional imaging across clinical phases in CNS drug development

The use of novel brain biomarkers using nuclear magnetic resonance imaging holds potential of making central nervous system (CNS) drug development more efficient. By evaluating changes in brain function in the disease state or drug effects on brain function, the technology opens up the possibility of obtaining objective data on drug effects in the living awake brain. By providing objective data, imaging may improve the probability of success of identifying useful drugs to treat CNS diseases across all clinical phases (I-IV) of drug development. The evolution of functional imaging and the promise it holds to contribute to drug development will require the development of standards (including good imaging practice), but, if well integrated into drug development, functional imaging can define markers of CNS penetration, drug dosing and target engagement (even for drugs that are not amenable to positron emission tomography imaging) in phase I; differentiate objective measures of efficacy and side effects and responders vs non-responders in phase II, evaluate differences between placebo and drug in phase III trials and provide insights into disease modification in phase IV trials.

GABA in the insula - a predictor of the neural response to interoceptive awareness

The insula has been identified as a key region involved in interoceptive awareness. Whilst imaging studies have investigated the neural activation patterns in this region involved in intero- and exteroceptive awareness, the underlying biochemical mechanisms still remain unclear. In order to investigate these, a well-established fMRI task targeting interoceptive awareness (heartbeat counting) and exteroceptive awareness (tone counting) was combined with magnetic resonance spectroscopy (MRS). Controlling for physiological noise, neural activity in the insula during intero- and exteroceptive awareness was confirmed in an independent data sample using the same fMRI design. Focussing on MRS values from the left insula and combining them with neural activity during intero- and exteroceptive awareness in the same healthy individuals, we demonstrated that GABA concentration in a region highly involved in interoceptive processing is correlated with neural responses to interoceptive stimuli, as opposed to exteroceptive stimuli. In addition, both GABA and interoceptive signal changes in the insula predicted the degree of depressed affect, as measured by the Beck Hopelessness Scale. On the one hand, the association between GABA concentration and neural activity during interoceptive awareness provides novel insight into the biochemical underpinnings of insula function and interoception. On the other, through the additional association of both GABA and neural activity during interoception with depressed affect, these data also bear potentially important implications for psychiatric disorders like depression and anxiety, where GABAergic deficits, altered insula function and abnormal affect coincide.

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.

Sensitivity to white matter FMRI activation increases with field strength

Functional magnetic resonance imaging (fMRI) activation in white matter is controversial. Given that many of the studies that report fMRI activation in white matter used high field MRI systems, we investigated the field strength dependence of sensitivity to white matter fMRI activation. In addition, we evaluated the temporal signal to noise ratio (tSNR) of the different tissue types as a function of field strength. Data were acquired during a motor task (finger tapping) at 1.5 T and 4 T. Group and individual level activation results were considered in both the sensorimotor cortex and the posterior limb of the internal capsule. We found that sensitivity increases associated with field strength were greater for white matter than gray matter. The analysis of tSNR suggested that white matter might be less susceptible to increases in physiological noise related to increased field strength. We therefore conclude that high field MRI may be particularly advantageous for fMRI studies aimed at investigating activation in both gray and white matter.

External awareness and GABA--a multimodal imaging study combining fMRI and [18F]flumazenil-PET

Awareness is an essential feature of the human mind that can be directed internally, that is, toward our self, or externally, that is, toward the environment. The combination of internal and external information is crucial to constitute our sense of self. Although the underlying neuronal networks, the so-called intrinsic and extrinsic systems, have been well-defined, the associated biochemical mechanisms still remain unclear. We used a well-established functional magnetic resonance imaging (fMRI) paradigm for internal (heartbeat counting) and external (tone counting) awareness and combined this technique with [(18)F]FMZ-PET imaging in the same healthy subjects. Focusing on cortical midline regions, the results showed that both stimuli types induce negative BOLD responses in the mPFC and the precuneus. Carefully controlling for structured noise in fMRI data, these results were also confirmed in an independent data sample using the same paradigm. Moreover, the degree of the GABAA receptor binding potential within these regions was correlated with the neuronal activity changes associated with external, rather than internal awareness when compared to fixation. These data support evidence that the inhibitory neurotransmitter GABA is an influencing factor in the differential processing of internally and externally guided awareness. This in turn has implications for our understanding of the biochemical mechanisms underlying awareness in general and its potential impact on psychiatric disorders.

A systematic review of the utility of 1.5 versus 3 Tesla magnetic resonance brain imaging in clinical practice and research

OBJECTIVE

MRI at 3 T is said to be more accurate than 1.5 T MR, but costs and other practical differences mean that it is unclear which to use.

METHODS

We systematically reviewed studies comparing diagnostic accuracy at 3 T with 1.5 T. We searched MEDLINE, EMBASE and other sources from 1 January 2000 to 22 October 2010 for studies comparing diagnostic accuracy at 1.5 and 3 T in human neuroimaging. We extracted data on methodology, quality criteria, technical factors, subjects, signal-to-noise, diagnostic accuracy and errors according to QUADAS and STARD criteria.

RESULTS

Amongst 150 studies (4,500 subjects), most were tiny, compared old 1.5 T with new 3 T technology, and only 22 (15 %) described diagnostic accuracy. The 3 T images were often described as "crisper", but we found little evidence of improved diagnosis. Improvements were limited to research applications [functional MRI (fMRI), spectroscopy, automated lesion detection]. Theoretical doubling of the signal-to-noise ratio was not confirmed, mostly being 25 %. Artefacts were worse and acquisitions took slightly longer at 3 T.

CONCLUSION

Objective evidence to guide MRI purchasing decisions and routine diagnostic use is lacking. Rigorous evaluation accuracy and practicalities of diagnostic imaging technologies should be the routine, as for pharmacological interventions, to improve effectiveness of healthcare.

KEY POINTS

• Higher field strength MRI may improve image quality and diagnostic accuracy. • There are few direct comparisons of 1.5 and 3 T MRI. • Theoretical doubling of the signal-to-noise ratio in practice was only 25 %. • Objective evidence of improved routine clinical diagnosis is lacking. • Other aspects of technology improved images more than field strength.

A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury

Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.

Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.

BSMac: a MATLAB toolbox implementing a Bayesian spatial model for brain activation and connectivity

We present a statistical and graphical visualization MATLAB toolbox for the analysis of functional magnetic resonance imaging (fMRI) data, called the Bayesian Spatial Model for activation and connectivity (BSMac). BSMac simultaneously performs whole-brain activation analyses at the voxel and region of interest (ROI) levels as well as task-related functional connectivity (FC) analyses using a flexible Bayesian modeling framework (Bowman et al., 2008). BSMac allows for inputting data in either Analyze or Nifti file formats. The user provides information pertaining to subgroup memberships, scanning sessions, and experimental tasks (stimuli), from which the design matrix is constructed. BSMac then performs parameter estimation based on Markov Chain Monte Carlo (MCMC) methods and generates plots for activation and FC, such as interactive 2D maps of voxel and region-level task-related changes in neural activity and animated 3D graphics of the FC results. The toolbox can be downloaded from http://www.sph.emory.edu/bios/CBIS/. We illustrate the BSMac toolbox through an application to an fMRI study of working memory in patients with schizophrenia.

Functional magnetic resonance imaging of semantic memory as a presymptomatic biomarker of Alzheimer's disease risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer's disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Reliability and convergent validity of different BOLD MRI frameworks for data acquisition in experimental arthritis

RATIONALE AND OBJECTIVES

The clinimetric properties of blood oxygen level‒dependent (BOLD) magnetic resonance imaging (MRI) for assessment of musculoskeletal changes have been poorly investigated. The study objectives were to assess the interframework reliability of data acquisition of BOLD MRI and to test its convergent validity in chronic arthritis in a rabbit model of inflammatory arthritis as compared with corresponding clinical and laboratory measures.

MATERIALS AND METHODS

One of the knees of 12 New Zealand male white rabbits was injected with a 1% carrageenin solution, and the contralateral (control) one was not. Twelve rabbits were euthanized on day 28 of arthritis (chronic arthritis). Clinical (joint diameters), laboratory (serum amyloid A concentration), and BOLD MRI measurements were obtained on days 0, 1, and 28 of arthritis. Twenty paradigms of data acquisition and analysis were applied.

RESULTS

The most reliable MRI parameters set, regardless of threshold values used for data analysis, was spiral technique (level 1), 40 ms of echo time (level 2), 60 seconds of on_ and off_ paradigm (level 3) and carbogen mixture of gases (95% O2 + 5% CO2) (level 4). With regard to construct validity, BOLD imaging correlated moderately (r = -.54, P < .0001) with knee diameters, and weakly (r = -.35, P = .01) with laboratory indices (high threshold for analysis).

CONCLUSION

BOLD MRI has a substantial or excellent interframework reliability for assessment of arthritic rabbit knees; however, it correlates only moderately or poorly with clinical and laboratory measures. Nevertheless, this study supports further validation of BOLD MRI for assessment of soft tissue changes in a rabbit model of arthritis.

Creating probabilistic maps of the face network in the adolescent brain: a multicentre functional MRI study

Large-scale magnetic resonance (MR) studies of the human brain offer unique opportunities for identifying genetic and environmental factors shaping the human brain. Here, we describe a dataset collected in the context of a multi-centre study of the adolescent brain, namely the IMAGEN Study. We focus on one of the functional paradigms included in the project to probe the brain network underlying processing of ambiguous and angry faces. Using functional MR (fMRI) data collected in 1,110 adolescents, we constructed probabilistic maps of the neural network engaged consistently while viewing the ambiguous or angry faces; 21 brain regions responding to faces with high probability were identified. We were also able to address several methodological issues, including the minimal sample size yielding a stable location of a test region, namely the fusiform face area (FFA), as well as the effect of acquisition site (eight sites) and scanner (four manufacturers) on the location and magnitude of the fMRI response to faces in the FFA. Finally, we provided a comparison between male and female adolescents in terms of the effect sizes of sex differences in brain response to the ambiguous and angry faces in the 21 regions of interest. Overall, we found a stronger neural response to the ambiguous faces in several cortical regions, including the fusiform face area, in female (vs. male) adolescents, and a slightly stronger response to the angry faces in the amygdala of male (vs. female) adolescents.

A multicentre study of motor functional connectivity changes in patients with multiple sclerosis

In this multicentre study involving eight European centres, we characterized the spatial pattern of functional connectivity (FC) in the sensorimotor network from 61 right-handed patients with multiple sclerosis (MS) and 74 age-matched healthy subjects assessed with the use of functional magnetic resonance imaging (fMRI) and a simple motor task of their right dominant hand. FC was investigated by using: (i) voxel-wise correlations between the left sensorimotor cortex (SMC) and any other area in the brain; and (ii) bivariate correlations between time series extracted from several regions of interest (ROIs) belonging to the sensorimotor network. Both healthy controls and MS patients had significant FC between the left SMC and several areas of the sensorimotor network, including the bilateral postcentral and precentral gyri, supplementary motor area, middle frontal gyri, insulae, secondary somatosensory cortices, thalami, and right cerebellum. Voxel-wise assessment of FC revealed increased connectivity between the left SMC and the right precentral gyrus, right middle frontal gyrus (MFG) and bilateral postcentral gyri in MS patients as compared with controls. ROI analysis also showed a widespread pattern of altered connectivity, characterized by increased FC between the right MFG, the left insula and the right inferior frontal gyrus in comparison with many regions of the sensorimotor network. These results provide further evidence for increased bihemispheric contributions to motor control in patients with MS relative to healthy controls. They further suggest that multicentre fMRI studies of FC changes are possible, and provide a potential imaging biomarker for use in experimental therapeutic studies directed at enhancing adaptive plasticity in the disease.

Quantification of accuracy and precision of multi-center DTI measurements: a diffusion phantom and human brain study

The inter-site and intra-site variability of system performance of MRI scanners (due to site-dependent and time-variant variations) can have significant adverse effects on the integration of multi-center DTI data. Measurement errors in accuracy and precision of each acquisition determine both the inter-site and intra-site variability. In this study, multiple scans of an identical isotropic diffusion phantom and of the brain of a traveling human volunteer were acquired at MRI scanners from the same vendor and with similar configurations at three sites. We assessed the feasibility of multi-center DTI studies by direct quantification of accuracy and precision of each dataset. Accuracy was quantified via comparison to carefully constructed gold standard datasets while precision (the within-scan variability) was estimated by wild bootstrap analysis. The results from both the phantom and human data suggest that the inter-site variation in system performance, although relatively small among scanners of the same vendor, significantly affects DTI measurement accuracy and precision and therefore the effectiveness for the integration of multi-center DTI measurements. Our results also highlight the value of a DTI-specific phantom in identifying and quantifying measurement errors due to site-dependent variations in the system performance, and its usefulness for quality assurance/quality control in multi-center DTI studies. In addition, we observed that the within-scan variability of each data acquisition, as assessed by wild bootstrap analysis, is of the same magnitude as the inter-site and intra-site variability. We propose that by weighing datasets based on their variability, as evaluated by wild bootstrap analysis, one can improve the quality of the dataset. This approach will provide a more effective integration of datasets from multi-center DTI studies.

Between- and within-scanner variability in the CaliBrain study n-back cognitive task

Psychiatric neuroimaging techniques are likely to improve understanding of the brain in health and disease, but studies tend to be small, based in one imaging centre and of unclear generalisability. Multicentre studies have great appeal but face problems if functional magnetic resonance imaging (fMRI) data from different centres are to be combined. Fourteen healthy volunteers had two brain scans on different days at three scanners. Considerable effort was first made to use similar scanning sequences and standardise task implementation across centres. The n-back cognitive task was used to investigate between- and within-scanner reproducibility and reliability. Both the functional imaging and behavioural results were in good accord with the existing literature. We found no significant differences in the activation/deactivation maps between scanners, or between repeat visits to the same scanners. Between- and within-scanner reproducibility and reliability was very similar. However, the smoothness of images from the scanners differed, suggesting that smoothness equalization might further reduce inter-scanner variability. Our results for the n-back task suggest it is possible to acquire fMRI data from different scanners which allows pooling across centres, when the same field strength scanners are used and scanning sequences and paradigm implementations are standardised.

Multisite reliability of cognitive BOLD data

Investigators perform multi-site functional magnetic resonance imaging studies to increase statistical power, to enhance generalizability, and to improve the likelihood of sampling relevant subgroups. Yet undesired site variation in imaging methods could off-set these potential advantages. We used variance components analysis to investigate sources of variation in the blood oxygen level-dependent (BOLD) signal across four 3-T magnets in voxelwise and region-of-interest (ROI) analyses. Eighteen participants traveled to four magnet sites to complete eight runs of a working memory task involving emotional or neutral distraction. Person variance was more than 10 times larger than site variance for five of six ROIs studied. Person-by-site interactions, however, contributed sizable unwanted variance to the total. Averaging over runs increased between-site reliability, with many voxels showing good to excellent between-site reliability when eight runs were averaged and regions of interest showing fair to good reliability. Between-site reliability depended on the specific functional contrast analyzed in addition to the number of runs averaged. Although median effect size was correlated with between-site reliability, dissociations were observed for many voxels. Brain regions where the pooled effect size was large but between-site reliability was poor were associated with reduced individual differences. Brain regions where the pooled effect size was small but between-site reliability was excellent were associated with a balance of participants who displayed consistently positive or consistently negative BOLD responses. Although between-site reliability of BOLD data can be good to excellent, acquiring highly reliable data requires robust activation paradigms, ongoing quality assurance, and careful experimental control.

Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing

In EPI‐fMRI acquisitions, various readout bandwidth (BW) values are used as a function of gradients' characteristics of the MR scanner system. Echo spacing (ES) is another fundamental parameter of EPI‐fMRI sequences, but the employed ES value is not usually reported in fMRI studies. Nyquist ghost is a typical EPI artifact that can degrade the overall quality of fMRI time series. In this work, the authors assessed the basic effect of BW and ES for two clinical 1.5 T MR scanner systems (scanner‐A, scanner‐B) on Nyquist ghost of gradient‐echo EPI‐fMRI sequences. BW range was: scanner‐A, 1953‐3906 Hz/pixel; scanner‐B, 1220‐2894 Hz/pixel. ES range was: scanner‐A, scanner‐B: 0.75‐1.33 ms. The ghost‐to‐signal ratio of time series acquisition (GSRts) and drift of ghost‐to‐signal ratio (DRGSR) were measured in a water phantom. For both scanner‐A (93% of variation) and scanner‐B (102% of variation) the mean GSRts significantly increased with increasing BW. GSRts values of scanner‐A did not significantly depended on ES. On the other hand, GSRts values of scanner‐B significantly varied with ES, showing a downward trend (81% of variation) with increasing ES. In addition, a GSRts spike point at ES=1.05ms indicating a potential resonant effect was revealed. For both scanners, no significant effect of ES on DRGSR was revealed. DRGSR values of scanner‐B did not significantly vary with BW, whereas DRGSR values of scanner‐A significantly depended on BW showing an upward trend from negative to positive values with increasing BW. GSRts and DRGSR can significantly vary with BW and ES, and the specific pattern of variation may depend on gradients performances, EPI sequence calibrations and functional design of radiofrequency coil. Thus, each MR scanner system should be separately characterized. In general, the employment of low BW values seems to reduce the intensity and temporal variation of Nyquist ghost in EPI‐fMRI time series. On the other hand, the use of minimum ES value might not be entirely advantageous when the MR scanner is characterized by gradients with low performances and suboptimal EPI sequence calibration.

PACS numbers: 87.61.‐c, 87.61.Qr, 87.61.Hk

A Bayesian mixture approach to modeling spatial activation patterns in multisite fMRI data

We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated observations on an individual or images from different individuals in a clinical study. Instead of taking the traditional approach of voxel-by-voxel analysis, we directly model the shape of activation patterns by representing each activation cluster in an image as a Gaussian-shaped surface. We assume that there is an unknown true template pattern and that each observed image is a noisy realization of this template. We model an individual image using a mixture of experts model with each component representing a spatial activation cluster. Taking a nonparametric Bayesian approach, we use a hierarchical Dirichlet process to extract common activation clusters from multiple images and estimate the number of such clusters automatically. We further extend the model by adding random effects to the shape parameters to allow for image-specific variation in the activation patterns. Using a Bayesian framework, we learn the shape parameters for both image-level activation patterns and the template for the set of images by sampling from the posterior distribution of the parameters. We demonstrate our model on a dataset collected in a large multisite fMRI study.

Multi-site characterization of an fMRI working memory paradigm: reliability of activation indices

Neuroimaging studies are facilitated significantly when it is possible to recruit subjects and acquire data at multiple sites. However, the use of different scanners and acquisition protocols is a potential source of variability in multi-site data. In this work we present a multi-site study of the reliability of fMRI activation indices, where 10 healthy volunteers were scanned at 4 different sites while performing a working memory paradigm. Our results indicate that, even with different scanner manufacturers and field strengths, activation variability due to site differences is small compared to variability due to subject differences in this cognitive task, provided we choose an appropriate activation measure.

How reliable are the results from functional magnetic resonance imaging?

Functional magnetic resonance imaging (fMRI) is one of the most important methods for in vivo investigation of cognitive processes in the human brain. Within the last two decades, an explosion of research has emerged using fMRI, revealing the underpinnings of everything from motor and sensory processes to the foundations of social cognition. While these results have revealed the potential of neuroimaging, important questions regarding the reliability of these results remain unanswered. In this paper, we take a close look at what is currently known about the reliability of fMRI findings. First, we examine the many factors that influence the quality of acquired fMRI data. We also conduct a review of the existing literature to determine if some measure of agreement has emerged regarding the reliability of fMRI. Finally, we provide commentary on ways to improve fMRI reliability and what questions remain unanswered. Reliability is the foundation on which scientific investigation is based. How reliable are the results from fMRI?

Combining spatial priors and anatomical information for fMRI detection

In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.

Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies

The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (the pooled analysis of raw data) or multi-site studies, which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated.

Abnormal connectivity of the sensorimotor network in patients with MS: a multicenter fMRI study

In this multicenter study, we used dynamic causal modeling to characterize the abnormalities of effective connectivity of the sensorimotor network in 61 patients with multiple sclerosis (MS) compared with 74 age-matched healthy subjects. We also investigated the correlation of such abnormalities with findings derived from structural MRI. In a subgroup of subjects, diffusion tensor (DT) MRI metrics of the corpus callosum and the left corticospinal tract (CST) were also assessed. MS patients showed increased effective connectivity relative to controls between: (a) the left primary SMC and the left dorsal premotor cortex (PMd), (b) the left PMd and the supplementary motor areas (SMA), (c) the left secondary sensorimotor cortex (SII) and the SMA, (d) the right SII and the SMA, (e) the left SII and the right SII, and (f) the right SMC and the SMA. MS patients had relatively reduced effective connectivity between the left SMC and the right cerebellum. No interaction was found between disease group and center. Coefficients of altered connectivity were weakly correlated with brain T2 LV, but moderately correlated with DT MRI-measured damage of the left CST. In conclusion, large multicenter fMRI studies of effective connectivity changes in diseased people are feasible and can facilitate studies with sample size large enough for robust outcomes. Increased effective connectivity in the patients for the simple motor task suggests local network modulation contributing to enhanced long-distance effective connectivity in MS patients. This extends and generalizes previous evidence that enhancement of effective connectivity may provide an important compensatory mechanism in MS.

Functional Magnetic Resonance Imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task

Multicentre MRI studies offer great potential to increase study power and flexibility, but it is not yet clear how reproducible the results from multiple centres may be. Here we present results from the multicentre study 'CaliBrain', examining the reproducibility of fMRI data within and between three sites. Fourteen subjects were scanned twice on three 1.5 T GE scanners using an identical scanning protocol. We present data from a motor task with three conditions, sequential and random finger tapping and rest. Similar activation maps were obtained for each site and visit; brain areas consistently activated during the task included the premotor, primary motor and supplementary motor areas, the striatum and cerebellum. Reproducibility was evaluated within and between sites by comparing the extent and spatial agreement of activation maps at both the subject and group levels. The results were within the range previously reported for similar tasks on single scanners and both measures were found to be comparable within and between sites, with between site reproducibility similar to the within site measures. A variance components analysis was used to examine the effects of site, subject and visit. The contributions of site and visit were small and reproducibility was similar between and within sites, whereas the variance between subjects, and unexplained variance was large. These findings suggest that we can have confidence in combined results from multicentre fMRI studies, at least when a consistent protocol is followed on similar machines in all participating scanning sites and care is taken to select homogeneous subject groups.

Current trends and challenges in MRI acquisitions to investigate brain function

Functional magnetic resonance imaging (fMRI) studies using the blood oxygenation level dependent (BOLD) response have become a widely used tool for noninvasive assessment of functional organization of the brain. Yet the technique is still fairly new, with many significant challenges remaining. Capitalizing on additional contrast mechanisms available with MRI, several other functional imaging techniques have been developed that potentially provide improved quantification or specificity of neuronal function. This article reviews the challenges and the current state of the art in MRI-based methods of imaging cognitive function.