Pitfalls in fMRI

Structural and functional brain correlates of socioeconomic status across the life span: A systematic review

It is well-established that higher socioeconomic status (SES) is associated with improved brain health. However, the effects of SES across different life stages on brain structure and function is still equivocal. In this systematic review, we aimed to synthesise findings from life course neuroimaging studies that investigated the structural and functional brain correlates of SES across the life span. The results indicated that higher SES across different life stages were independently and cumulatively related to neural outcomes typically reflective of greater brain health (e.g., increased cortical thickness, grey matter volume, fractional anisotropy, and network segregation) in adult individuals. The results also demonstrated that the corticolimbic system was most commonly impacted by socioeconomic disadvantages across the life span. This review highlights the importance of taking into account SES across the life span when studying its effects on brain health. It also provides directions for future research including the need for longitudinal and multimodal research that can inform effective policy interventions tailored to specific life stages.

Characterizing the supraspinal sensorimotor control of walking using MRI-compatible system: a systematic review

BACKGROUND

The regulation of gait is critical to many activities of everyday life. When walking, somatosensory information obtained from mechanoreceptors throughout body is delivered to numerous supraspinal networks and used to execute the appropriate motion to meet ever-changing environmental and task demands. Aging and age-related conditions oftentimes alter the supraspinal sensorimotor control of walking, including the responsiveness of the cortical brain regions to the sensorimotor inputs obtained from the peripheral nervous system, resulting in diminished mobility in the older adult population. It is thus important to explicitly characterize such supraspinal sensorimotor elements of walking, providing knowledge informing novel rehabilitative targets. The past efforts majorly relied upon mental imagery or virtual reality to study the supraspinal control of walking. Recent efforts have been made to develop magnetic resonance imaging (MRI)-compatible devices simulating specific somatosensory and/or motor aspects of walking. However, there exists large variance in the design and functionality of these devices, and as such inconsistent functional MRI (fMRI) observations.

METHODS

We have therefore completed a systematic review to summarize current achievements in the development of these MRI-compatible devices and synthesize available imaging results emanating from studies that have utilized these devices.

RESULTS

The device design, study protocol and neuroimaging observations of 26 studies using 13 types of devices were extracted. Three of these devices can provide somatosensory stimuli, eight motor stimuli, and two both types of stimuli. Our review demonstrated that using these devices, fMRI data of brain activation can be successfully obtained when participants remain motionless and experience sensorimotor stimulation during fMRI acquisition. The activation in multiple cortical (e.g., primary sensorimotor cortex) and subcortical (e.g., cerebellum) regions has been each linked to these types of walking-related sensorimotor stimuli.

CONCLUSION

The observations of these publications suggest the promise of implementing these devices to characterize the supraspinal sensorimotor control of walking. Still, the evidence level of these neuroimaging observations was still low due to small sample size and varied study protocols, which thus needs to be confirmed via studies with more rigorous design.

Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate

Reliability and robustness of resting state functional connectivity MRI (rs-fcMRI) relies, in part, on minimizing the influence of head motion on measured brain signals. The confounding effects of head motion on functional connectivity have been extensively studied in adults, but its impact on newborn brain connectivity remains unexplored. Here, using a large newborn data set consisting of 159 rs-fcMRI scans acquired in the Developing Brain Institute at Children's National Hospital and 416 scans from The Developing Human Connectome Project (dHCP), we systematically investigated associations between head motion and rs-fcMRI. Head motion during the scan significantly affected connectivity at sensory-related networks and default mode networks, and at the whole brain scale; the direction of motion effects varied across the whole brain. Comparing high- versus low-head motion groups suggested that head motion can impact connectivity estimates across the whole brain. Censoring of high-motion volumes using frame-wise displacement significantly reduced the confounding effects of head motion on neonatal rs-fcMRI. Lastly, in the dHCP data set, we demonstrated similar persistent associations between head motion and network connectivity despite implementing a standard denoising strategy. Collectively, our results highlight the importance of using rigorous head motion correction in preprocessing neonatal rs-fcMRI to yield reliable estimates of brain activity.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

The backbone network of dynamic functional connectivity

Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting-state fMRI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting-state fMRI dataset, and provide further discussion on various aspects and advantages of it.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

Preoperative functional MRI use in neurooncology patients: a clinician survey

OBJECTIVE

Functional MRI (fMRI) is increasingly being investigated for use in neurosurgical patient care. In the current study, the authors characterize the clinical use of fMRI by surveying neurosurgeons' use of and attitudes toward fMRI as a surgical planning tool in neurooncology patients.

METHODS

A survey was developed to inquire about clinicians' use of and experiences with preoperative fMRI in the neurooncology patient population, including example case images. The survey was distributed to all neurosurgical departments with a residency program in the US.

RESULTS

After excluding incomplete surveys and responders that do not use fMRI (n = 11), 50 complete responses were included in the final analysis. Responders were predominantly from academic programs (88%), with 20 years or more in practice (40%), with a main area of practice in neurooncology (48%) and treating an adult population (90%). All 50 responders currently use fMRI in neurooncology patients, mostly for low- (94%) and high-grade glioma (82%). The leading decision factors for ordering fMRI were location of mass in dominant hemisphere, location in a functional area, motor symptoms, and aphasia. Across 10 cases, language fMRI yielded the highest interrater reliability agreement (Fleiss' kappa 0.437). The most common reasons for ordering fMRI were to identify language laterality, plan extent of resection, and discuss neurological risks with patients. Clinicians reported that fMRI results were not obtained when ordered a median 10% of the time and were suboptimal a median 27% of the time. Of responders, 70% reported that they had ever resected an fMRI-positive functional site, of whom 77% did so because the site was "cleared" by cortical stimulation. Responders reported disagreement between fMRI and awake surgery 30% of the time. Overall, 98% of responders reported that if results of fMRI and intraoperative mapping disagreed, they would rely on intraoperative mapping.

CONCLUSIONS

Although fMRI is increasingly being adopted as a practical preoperative planning tool for brain tumor resection, there remains a substantial degree of discrepancy with regard to its current use and presumed utility. There is a need for further research to evaluate the use of preoperative fMRI in neurooncology patients. As fMRI continues to gain prominence, it will be important for clinicians to collectively share best practices and develop guidelines for the use of fMRI in the preoperative planning phase of brain tumor patients.

Altered functional activity in bipolar disorder: A comprehensive review from a large-scale network perspective

BACKGROUND

Growing literature continues to identify brain regions that are functionally altered in bipolar disorder. However, precise functional network correlates of bipolar disorder have yet to be determined due to inconsistent results. The overview of neurological alterations from a large-scale network perspective may provide more comprehensive results and elucidate the neuropathology of bipolar disorder. Here, we critically review recent neuroimaging research on bipolar disorder using a network-based approach.

METHODS

A systematic search was conducted on studies published from 2009 through 2019 in PubMed and Google Scholar. Articles that utilized functional magnetic resonance imaging technique to examine altered functional activity of major regions belonging to a large-scale brain network in bipolar disorder were selected.

RESULTS

A total of 49 studies were reviewed. Within-network hypoconnectivity was reported in bipolar disorder at rest among the default mode, salience, and central executive networks. In contrast, when performing a cognitive task, hyperconnectivity among the central executive network was found. Internetwork functional connectivity in the brain of bipolar disorder was greater between the salience and default mode networks, while reduced between the salience and central executive networks at rest, compared to control.

CONCLUSION

This systematic review suggests disruption in the functional activity of large-scale brain networks at rest as well as during a task stimuli in bipolar disorder. Disrupted intra- and internetwork functional connectivity that are also associated with clinical symptoms suggest altered functional connectivity of and between large-scale networks plays an important role in the pathophysiology of bipolar disorder.

Educational fMRI: From the Lab to the Classroom

Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.

Gradient theories of brain activation: A novel application to studying the parental brain

PURPOSE OF REVIEW

Parental brain research primarily employs general-linear-model-based (GLM-based) analyses to assess blood-oxygenation-level-dependent responses to infant auditory and visual cues, reporting common responses in shared cortical and subcortical structures. However, this approach does not reveal intermixed neural substrates related to different sensory modalities. We consider this notion in studying the parental brain.

RECENT FINDINGS

Spatial independent component analysis (sICA) has been used to separate mixed source signals from overlapping functional networks. We explore relative differences between GLM-based analysis and sICA as applied to an fMRI dataset acquired from women while they listened to infant cries or viewed infant sad faces.

SUMMARY

There is growing appreciation for the value of moving beyond GLM-based analyses to consider brain functional organization as continuous, distributive, and overlapping gradients of neural substrates related to different sensory modalities. Preliminary findings suggest sICA can be applied to the study of the parental brain.

A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data

Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensures a relatively high signal-to-noise ratio. However, for pre-surgical functional magnetic resonance imaging analysis, where spatial accuracy is paramount, high-resolution functional magnetic resonance imaging may play an important role with its greater spatial resolution. High spatial resolution comes at the cost of a smaller signal-to-noise ratio. This begs the question as to whether we can leverage the higher signal-to-noise ratio of a standard functional magnetic resonance imaging study with the greater spatial accuracy of a high-resolution functional magnetic resonance imaging study in a pre-operative patient. To answer this question, we propose to regress the statistic image from a high resolution scan onto the statistic image obtained from a standard resolution scan using a mixed-effects model with spatially varying coefficients. We evaluate our model via simulation studies and we compare its performance with a recently proposed model that operates at a single spatial resolution. We apply and compare the two models on data from a patient awaiting tumor resection. Both simulation study results and the real data analysis demonstrate that our newly proposed model indeed leverages the larger signal-to-noise ratio of the standard spatial resolution scan while maintaining the advantages of the high spatial resolution scan.

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.

Challenges and techniques for presurgical brain mapping with functional MRI

Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.

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fMRI is increasingly used for presurgical brain mapping for surgical planning.

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Understanding of the limitations of fMRI is critical for its clinical use.

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Clinical fMRI's challenges and potential solutions are discussed.

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Intraoperative use and validation of fMRI are discussed.

Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline

PURPOSE

Recent evidence indicates that caffeine may have a beneficial effect on cognitive decline and dementia. The current investigation assessed the effect of acute caffeine administration on working memory during the earliest stage of cognitive decline in elderly participants.

METHODS

The study includes consecutive 45 elderly controls and 18 individuals with mild cognitive impairment (MCI, 71.6 ± 4.7 years, 7 females). During neuropsychological follow-up at 18 months, 24 controls remained stable (sCON, 70.0 ± 4.3 years, 11 women), while the remaining 21 showed subtle cognitive deterioration (dCON, 73.4 ± 5.9 years, 14 women). All participants underwent an established 2-back working task in a crossover design of 200 mg caffeine versus placebo. Data analysis included task-related general linear model and functional connectivity tensorial independent component analysis.

RESULTS

Working memory behavioral performances did not differ between sCON and dCON, while MCI was slower and less accurate than both control groups (p < 0.05). The dCON group had a less pronounced effect of acute caffeine administration essentially restricted to the right hemisphere (p < 0.05 corrected) and reduced default mode network (DMN) deactivation compared to sCON (p < 0.01 corrected).

CONCLUSION

dCON cases are characterized by decreased sensitivity to caffeine effects on brain activation and DMN deactivation. These complex fMRI patterns possibly reflect the instable status of these cases with intact behavioral performances despite already existing functional alterations in neocortical circuits.

Preliminary Report on Neuroanatomical Differences Among Reading Disabled, Nonverbally Gifted, and Gifted-Reading Disabled College Students

Regional comparisons (cortical surface area and thickness) were performed on a well described sample of adults with reading disability alone (RD), nonverbal giftedness alone (G), and reading disability and nonverbal giftedness combined (GRD). These anatomical results are considered in relation to behavioral and functional work previously reported on this sample. GRD-RD regional differences were found in both hemispheres and were more common than GRD-G differences. Regional differences were found in the temporal, parietal, occipital and frontal lobes. While these data are preliminary given the small sample sizes, they suggest future avenues of research on the neurodevelopment of atypical samples.

Prospective motion correction in functional MRI

Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no-motion conditions, which motivates further research in the area of adaptive dynamic imaging.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

The neural mechanisms of hallucinations: A quantitative meta-analysis of neuroimaging studies

Activation likelihood estimation meta-analysis of functional neuroimaging data was used to investigate the neural mechanisms underlying auditory-verbal and visual hallucinations (AVHs and VHs). Consistent activation across studies during AVHs, but not VHs, in Wernicke's and Broca's areas is consistent with involvement of speech and language processes in the experience of hearing voices when none are present. Similarly, greater activity in auditory cortex during AVHs and in visual cortex during VHs supports models proposing over-stimulation of sensory cortices in the generation of these perceptual anomalies. Activation across studies in the medial temporal lobe highlights a role for memory intrusions in the provision of content for AVHs, whereas insula activation may relate to the involvement of awareness and self-representation. Finally, activation in the paracingulate region of medial prefrontal cortex during AVHs is consistent with models implicating reality monitoring impairment in the misattribution of self-generated information as externally perceived. In the light of the results, the need for unified theoretical frameworks that account for the full range of hallucinatory experiences is discussed.

Pre-Surgical fMRI Data Analysis Using a Spatially Adaptive Conditionally Autoregressive Model

Spatial smoothing is an essential step in the analysis of functional magnetic resonance imaging (fMRI) data. One standard smoothing method is to convolve the image data with a three-dimensional Gaussian kernel that applies a fixed amount of smoothing to the entire image. In pre-surgical brain image analysis where spatial accuracy is paramount, this method, however, is not reasonable as it can blur the boundaries between activated and deactivated regions of the brain. Moreover, while in a standard fMRI analysis strict false positive control is desired, for pre-surgical planning false negatives are of greater concern. To this end, we propose a novel spatially adaptive conditionally autoregressive model with variances in the full conditional of the means that are proportional to error variances, allowing the degree of smoothing to vary across the brain. Additionally, we present a new loss function that allows for the asymmetric treatment of false positives and false negatives. We compare our proposed model with two existing spatially adaptive conditionally autoregressive models. Simulation studies show that our model outperforms these other models; as a real model application, we apply the proposed model to the pre-surgical fMRI data of two patients to assess peri- and intra-tumoral brain activity.

Functional brain alterations in anorexia nervosa: a scoping review

BACKGROUND

Neuroimaging allows for the identification of brain abnormalities and alterations that are associated with anorexia nervosa (AN). We performed a scoping review to map out the extent and nature of recent research activity on functional magnetic resonance imaging (fMRI) in individuals diagnosed with, or recovered from, AN (AN-REC).

MAIN TEXT

A literature search of PubMed, Psychinfo and Embase was conducted using the search terms "anorexia nervosa" AND "functional magnetic resonance imaging." We included fMRI studies that involved a comparison between individuals with AN or AN-REC and healthy controls published in English language between 2010 and 2015. A total of 49 papers were included, regardless of the experimental stimuli or paradigm.

CONCLUSIONS

Findings varied considerably across studies, reflecting methodological differences in study design, such as sample differences and experimental paradigms. Collectively, studies published during the past five years suggest altered activation in regions related to the fronto-striato and the limbic circuits, which are theorized to have an important role in the pathophysiology of AN.

Objective Bayesian fMRI analysis-a pilot study in different clinical environments

Functional MRI (fMRI) used for neurosurgical planning delineates functionally eloquent brain areas by time-series analysis of task-induced BOLD signal changes. Commonly used frequentist statistics protect against false positive results based on a p-value threshold. In surgical planning, false negative results are equally if not more harmful, potentially masking true brain activity leading to erroneous resection of eloquent regions. Bayesian statistics provides an alternative framework, categorizing areas as activated, deactivated, non-activated or with low statistical confidence. This approach has not yet found wide clinical application partly due to the lack of a method to objectively define an effect size threshold. We implemented a Bayesian analysis framework for neurosurgical planning fMRI. It entails an automated effect-size threshold selection method for posterior probability maps accounting for inter-individual BOLD response differences, which was calibrated based on the frequentist results maps thresholded by two clinical experts. We compared Bayesian and frequentist analysis of passive-motor fMRI data from 10 healthy volunteers measured on a pre-operative 3T and an intra-operative 1.5T MRI scanner. As a clinical case study, we tested passive motor task activation in a brain tumor patient at 3T under clinical conditions. With our novel effect size threshold method, the Bayesian analysis revealed regions of all four categories in the 3T data. Activated region foci and extent were consistent with the frequentist analysis results. In the lower signal-to-noise ratio 1.5T intra-operative scanner data, Bayesian analysis provided improved brain-activation detection sensitivity compared with the frequentist analysis, albeit the spatial extents of the activations were smaller than at 3T. Bayesian analysis of fMRI data using operator-independent effect size threshold selection may improve the sensitivity and certainty of information available to guide neurosurgery.

Alternative-based thresholding with application to presurgical fMRI

Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive-that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.

Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows for visualization of activated brain regions. Until recently, fMRI studies have focused on gray matter. There are two main reasons white matter fMRI remains controversial: (1) the blood oxygen level dependent (BOLD) fMRI signal depends on cerebral blood flow and volume, which are lower in white matter than gray matter and (2) fMRI signal has been associated with post-synaptic potentials (mainly localized in gray matter) as opposed to action potentials (the primary type of neural activity in white matter). Despite these observations, there is no direct evidence against measuring fMRI activation in white matter and reports of fMRI activation in white matter continue to increase. The questions underlying white matter fMRI activation are important. White matter fMRI activation has the potential to greatly expand the breadth of brain connectivity research, as well as improve the assessment and diagnosis of white matter and connectivity disorders. The current review provides an overview of the motivation to investigate white matter fMRI activation, as well as the published evidence of this phenomenon. We speculate on possible neurophysiologic bases of white matter fMRI signals, and discuss potential explanations for why reports of white matter fMRI activation are relatively scarce. We end with a discussion of future basic and clinical research directions in the study of white matter fMRI.

Subject-specific functional parcellation via prior based eigenanatomy

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.

BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis

Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography have growing popularity. However, there is a scarcity of solutions to perform optimized, intuitive, and consistent multimodal fMRI/DTI studies. Here we propose a new tool, brain connectivity analysis tool (BrainCAT), for an automated and standard multimodal analysis of combined fMRI/DTI data, using freely available tools. With a friendly graphical user interface, BrainCAT aims to make data processing easier and faster, implementing a fully automated data processing pipeline and minimizing the need for user intervention, which hopefully will expand the use of combined fMRI/DTI studies. Its validity was tested in an aging study of the default mode network (DMN) white matter connectivity. The results evidenced the cingulum bundle as the structural connector of the precuneus/posterior cingulate cortex and the medial frontal cortex, regions of the DMN. Moreover, mean fractional anisotropy (FA) values along the cingulum extracted with BrainCAT showed a strong correlation with FA values from the manual selection of the same bundle. Taken together, these results provide evidence that BrainCAT is suitable for these analyses.

Effect of disease and recovery on functional anatomy in brain tumor patients: insights from functional MRI and diffusion tensor imaging

Patients with brain tumors provide a unique opportunity to understand functional brain plasticity. Using advanced imaging techniques, such as functional MRI and diffusion tensor imaging, we have gained tremendous knowledge of brain tumor behavior, transformation, infiltration and destruction of nearby structures. Using these advanced techniques as an adjunct with more proven techniques, such as direct cortical stimulation, intraoperative navigation and advanced microsurgical techniques, we now are able to better formulate safer resection trajectories, perform larger resections at reduced risk and better counsel patients and their families about possible complications. Brain mapping in patients with brain tumors and other lesions has shown us that the old idea of fixed function of the adult cerebral cortex is not entirely true. Improving care for patients with brain lesions in the future will depend on better understanding of the functional organization and plasticity of the adult brain. Advanced noninvasive brain imaging will undoubtedly play a role in advancing this understanding.

Skeletal muscle BOLD MRI: from underlying physiological concepts to its usefulness in clinical conditions

Blood oxygenation-level dependent (BOLD) MRI has gained particular attention in functional brain imaging studies, where it can be used to localize areas of brain activation with high temporal resolution. To a higher degree than in the brain, skeletal muscles show extensive but transient alterations of blood flow between resting and activation state. Thus, there has been interest in the application of the BOLD effect in studying the physiology of skeletal muscles (healthy and diseased) and its possible application to clinical practice. This review outlines the potential of skeletal muscle BOLD MRI as a diagnostic tool for the evaluation of physiological and pathological alterations in the peripheral limb perfusion, such as in peripheral arterial occlusive disease. Moreover, current knowledge is summarized regarding the complex mechanisms eliciting BOLD effect in skeletal muscle. We describe technical fundaments of the procedure that should be taken into account when performing skeletal muscle BOLD MRI, including the most often applied paradigms to provoke BOLD signal changes and key parameters of the resulting time courses. Possible confounding effects in muscle BOLD imaging studies, like age, muscle fiber type, training state, and drug effects are also reviewed in detail.

Functional MRI applications in epilepsy surgery

Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that has grown rapidly in popularity over the past decade. It is already prevalent in psychology, cognitive and basic neuroscience research and is being used increasingly as a tool for clinical decision-making in epilepsy. It has been used to determine language location and laterality in patients, sometimes eliminating the need for invasive tests. fMRI can been used pre-surgically to guide resection margins, preserving eloquent cortex. Other fMRI paradigms assessing memory, visual and somatosensory systems have limited clinical applications currently, but show great promise. Simultaneous recording of electroencephalogram (EEG) and fMRI has also provided insights into the networks underlying seizure generation and is increasingly being used in epilepsy centres. In this review, we present some of the current clinical applications for fMRI in the pre-surgical assessment of epilepsy patients, and examine a number of new techniques that may soon become clinically relevant.

Efficient and robust estimation of blood oxygenation levels in single cerebral veins

Blood oxygenation level is an important measure that can be used alongside functional magnetic resonance imaging data in order to obtain closer correlates of neuronal activation. A robust estimate of this measure has thus far not been demonstrated. This is mainly due to the lack of knowledge of the underlying parameters which influence the numerical estimates of blood oxygenation. In this paper, we present a systematic analysis of the estimation performance of venous hemoglobin oxygen saturation [Formula: see text] as a function of noise, physiologic and geometric parameters. Furthermore, we present a novel algorithm for estimating [Formula: see text] from the temporal decay of an MR signal. The proposed algorithm incorporates prior information about the functional dependence between [Formula: see text] and relaxation rates. We compare our algorithm to an existing method in the literature and analyze the estimation performance. We show that our proposed algorithm is more efficient, achieving gains in performance as high as 92 %. We also show how our estimation algorithm takes advantage of signal features that are specific to the underlying physiology and geometry. We argue that optimal acquisition sequences, and corresponding estimation methods, should take into account such features in order to obtain robust estimates of blood oxygenation.

Quantifying hemodynamic refractory bold effects in normal subjects at the single-subject level using an inverse logit fitting procedure

PURPOSE

To evaluate whether hemodynamic refractory effects provoked by repeated visual stimulation can be detected and quantified at the single-subject level using a recently described hemodynamic response function (HRF) fitting algorithm.

MATERIALS AND METHODS

Hemodynamic refractory effects were induced with an easily applicable functional MRI (fMRI) paradigm. A fitting method with inverse logit (IL) functions was applied to quantify net HRFs at the single-subject level with three interstimulus intervals (ISI; 1, 2, and 6 s). The model yielded amplitude, latencies, and width for each HRF.

RESULTS

HRF fitting was possible in 44 of 51 healthy volunteers, with excellent goodness-of-fit (R(2) = 0.9745 ± 0.0241). Refractory effects were most pronounced for the 1-s ISI (P < 0.001) and had nearly disappeared for the 6-s ISI.

CONCLUSION

Quantifying refractory effects in individuals was possible in 86.3% of normal subjects using the IL fitting algorithm. This setup may be suitable to explore such effects in individual patients.

A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #1: processes and requirements

There is increasing interest in the application of quantitative magnetic resonance imaging (MRI) methods to drug development, but as yet little standardization or best practice guidelines for its use in this context. Pharmaceutical trials are subject to regulatory constraints and sponsor company processes, including site qualification and expectations around study oversight, blinding, quality assurance and quality control (QA/QC), analysis and reporting of results. In this article, we review the processes on the sponsor side and also the procedures involved in data acquisition at the imaging site. We then propose summary recommendations to help guide appropriate imaging site qualification, as part of a framework of 'good imaging practice' for functional (f)MRI studies applied to drug development.

A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #2: protocol optimization and best practices

Functional magnetic resonance imaging (fMRI) experiments are more complex compared with standard radiological imaging, involving additional data streams and hardware along with complex analysis methods. Here, we propose guidelines based around mitigating risks associated with the complexities of the technique at the level of the individual imaging protocol, including workable and effective quality assurance/quality control procedures and rigorous, predefined, analysis pipelines. Our aim is to provide a framework for 'good imaging practice' (GIP), enabling these requirements to be addressed at an appropriate level of detail. The development of a procedural framework for GIP in pharmaceutical fMRI studies could lead to greater acceptance of the method within industry and facilitate validation and, eventually, qualification of the technique as an imaging biomarker.

Dynamic monitoring of cytosolic glucose in single astrocytes

It is becoming increasingly clear that astrocytes are no longer playing a subservient role to neurons in the central nervous system (CNS), and that these cells are being considered as active communication integrators. They respond to neurotransmitters by the regulated release of gliotransmitters. The delay between neurotransmitter activation and the release of gliotransmitters from astrocytes is in the time-domain of subseconds, much slower than the submillisecond synaptic delay. Astrocytes also control microcirculation and provide metabolic support for neurons. However, the dynamics of their energy metabolic response to neurotransmitter application is not known. We here used a FRET glucose nanosensor to dynamically measure the cytosolic glucose concentration in single astrocytes. We show that following the adrenaline or noradrenaline stimulation the availability of cytosolic glucose is increased promptly after stimulation with a time-constant of 116.7 s and 115.9 s, respectively. A decline in cytosolic glucose concentration with a time-constant of 50.7 s was observed during glutamate and 16.7 s during lactate addition to astrocytes, when these were bathed in the presence of extracellular glucose-containing solution, likely reflecting predominant glucose engagement in glycogen synthesis. In contrast, in the glucose-free extracellular solution, glutamate application to astrocytes resulted in a slow increase in cytosolic glucose concentration, consistent with the view that glutamate may be an alternative energy source in hypoglycemic conditions. We conclude that astrocytic cytosolic glucose metabolism responds in the time-domain of tens of seconds, which is slower compared to the whole brain functional magnetic resonance imaging measurements of the local intravascular hemodynamic response.

The BOLD effect

The purpose of this chapter is to introduce the novice NMR imager to blood oxygen level dependent (BOLD) contrast as well as remind the seasoned veteran of its beauty. Introduction to many of the factors that influence the BOLD signal is given higher priority than pursuing any subset in exquisite detail. Instead, references are given for readers seeking intense investigations into a given aspect. The hope is that this overview inspires the reader with the elegant simplicity of BOLD contrast while not, at first, intimidating too much with the underlying complexity. As one's knowledge of NMR matures so too will one's understanding, appreciation, and application of BOLD MRI. BOLD contrast derives from variations in the magnetic susceptibility of blood due to variations in the concentration of deoxyhemoglobin. These magnetic susceptibility effects produce local magnetic fields around blood vessels that can result in phase dispersion of nearby spins and, therefore, changes in signal intensity in NMR images. After providing brief historical context for BOLD, this chapter will follow the trail of magnetic susceptibility through definition, its source and location in vivo, and how the source and location in vivo interact with anatomical (e.g., blood vessel size) and imaging considerations (e.g., pulse sequence) to influence the BOLD signal. We will conclude by briefly highlighting clinical and preclinical applications using BOLD contrast.

Potential impact of a 32-channel receiving head coil technology on the results of a functional MRI paradigm

PURPOSE

The authors investigated the potential of a 32-channel (32ch) receiving head coil for functional magnetic resonance imaging (fMRI) compared to a standard eight-channel (8ch) coil using a motor task.

MATERIAL AND METHODS

Brain activation was analyzed in 14 healthy right-handed subjects performing finger tapping with the right index finger (block design) during two experimental sessions, one with the 8ch and one with the 32ch coil (applied in a pseudorandomized order). Additionally, a phantom study was performed to compare signal-to-noise ratios (SNRs) of both coils.

RESULTS

During both fMRI sessions, analysis of motor conditions resulted in an activation of the left "hand knob" (precentral gyrus). Application of the 32ch coil obtained additional activation clusters in the right cerebellum, left superior frontal gyrus (SMA), left supramarginal gyrus, and left postcentral gyrus. The phantom study revealed a significantly higher SNR for the 32ch coil compared to the 8ch coil in superficial cortical areas located near the surface of the brain.

CONCLUSION

The 32ch technology has a potential impact on fMRI studies, especially in paradigms that result in activation of cortical areas located near the surface of the brain.

Altered resting state cortico-striatal connectivity in mild to moderate stage Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder that is characterized by dopamine depletion in the striatum. One consistent pathophysiological hallmark of PD is an increase in spontaneous oscillatory activity in the basal ganglia thalamocortical networks. We evaluated these effects using resting state functional connectivity MRI in mild to moderate stage Parkinson's patients on and off l-DOPA and age-matched controls using six different striatal seed regions. We observed an overall increase in the strength of cortico-striatal functional connectivity in PD patients off l-DOPA compared to controls. This enhanced connectivity was down-regulated by l-DOPA as shown by an overall decrease in connectivity strength, particularly within motor cortical regions. We also performed a frequency content analysis of the BOLD signal time course extracted from the six striatal seed regions. PD off l-DOPA exhibited increased power in the frequency band 0.02–0.05 Hz compared to controls and to PD on l-DOPA. The l-DOPA associated decrease in the power of this frequency range modulated the l-DOPA associated decrease in connectivity strength between striatal seeds and the thalamus. In addition, the l-DOPA associated decrease in power in this frequency band correlated with the l-DOPA associated improvement in cognitive performance. Our results demonstrate that PD and l-DOPA modulate striatal resting state BOLD signal oscillations and cortico-striatal network coherence.

[Clinical application of functional MRI for chronic epilepsy]

Functional magnetic resonance imaging (fMRI) is frequently used in the presurgical diagnostic procedure of epilepsy patients, in particular for lateralization of speech and memory and for localization of the primary motor cortex to delineate the epileptogenic lesion from eloquent brain areas. fMRI is one of the non-invasive procedures in the presurgical diagnostic process, together with medical history, seizure semiology, neurological examination, interictal and ictal EEG, structural MRI, video EEG monitoring and neuropsychology. This diagnostic sequence leads either to the decision for or against elective epilepsy surgery or to the decision to proceed with invasive diagnostic techniques (Wada test, intra-operative or extra-operative cortical stimulation). It is difficult to evaluate the contribution of the fMRI test in isolation to the validity of the entire diagnostic sequence. Complications such as memory loss and aphasia in temporal lobe resections or paresis after frontal lobe resections are rare and rarely of disastrous extent. This further complicates the evaluation of the clinical relevance of fMRI as a predictive tool. In this article studies which investigated the concordance between fMRI and other diagnostic gold standards will be presented as well as the association between presurgical fMRI and postsurgical morbidity.

[Methodological problems with clinical functional MRI investigations]

During presurgical diagnostics clinical functional magnetic resonance imaging is increasingly being performed to improve the management of epilepsy and tumor patients. Rapid technical developments in fMRI technology continuously further new diagnostic applications. Safe clinical application requires a profound and critical handling of the various methodological problems inherent with this complex technique. This article reviews relevant problems and solutions for patient investigations up to the preparation of an individual clinical fMRI report.

Supplementary motor area and anterior intraparietal area integrate fine-graded timing and force control during precision grip

We investigated the neuronal processing of the physiologically particularly important precision grip (opposition of index finger and thumb) by the combination of functional magnetic resonance imaging (fMRI) and an MR-compatible haptic interface. Ten healthy subjects performed isometric precision grip force generation with visual task instruction and real-time visual feedback in a block design. In a 2 x 2 two-factorial design, both the timing and force could be either constant or varying (identical average timing and force). As we expected only small changes in the fMRI response for the different fine-graded motor control conditions, we maximized the sensitivity of the data analysis and implemented a volumes of interest (VOI) restricted general linear model analysis including non-explanatory force regressors to eliminate directly force-related low-level activations. The VOIs were defined based on previous studies. We found significant associations: timing variation (variable vs. constant) and primary motor area (M1) and dorsal premotor area (PMd); force variation (variable vs. constant) and primary somatosensory area (S1), anterior intraparietal area (AIP) and PMd; interaction of timing and force and supplementary motor area (SMA) and AIP. We conclude that SMA and AIP integrate fine-graded higher-level timing and force control during precision grip. M1, S1 and PMd process lower-level timing and force control, yet not their integration. These results are the basis for a detailed assessment of manual motor control in a variety of motor diseases. The detailed behavioural assessment by our MR-compatible haptic interface is particularly valuable in patients due to expected larger inter-individual variation in motor performance.

Magnetic resonance imaging in multiple sclerosis

OBJECTIVES

multiple sclerosis (MS) is an inflammatory disease of unknown origin affecting the central nervous system. Magnetic resonance imaging (MRI) plays an increasingly important role in its diagnosis and further monitoring of disease progress.

METHODS

the typical MRI appearance of MS on conventional MRI sequences and current diagnostic criteria for MS are discussed. Advanced imaging techniques are reviewed with respect to application in MS. Finally, the atypical variants of MS are briefly reviewed.

CONCLUSIONS

although MRI is not intended and will not replace clinical assessment in MS, the recognized MRI criteria may aid in establishing an earlier and more accurate diagnosis of MS in the context of a clinical suspicion or clinically isolated syndrome. In addition, MRI might contribute to rule out differential diagnoses for MS. Moreover, MRI may be used to monitor the evolution of MS and in pharmaceutical trials. Advanced imaging techniques might, in the future, further characterize MS lesion subtypes and potentially guide tailored therapy.