Clinical Applications Of fMRI Research Articles

A scoping review of the functional magnetic resonance imaging-based functional connectivity of focal cortical dysplasia-related epilepsy.

Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to noninvasively map whole-brain functional networks. Despite its relatively widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities.

Ground-truth “resting-state” signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics

The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four “best-case” scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6–18% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8–19% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40–70% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR > 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.

Preoperative surgical planning of glioma: limitations and reliability of fMRI and DTI tractography.

Brain mapping techniques (intraoperative neurophysiology and neuropsychology) represent the gold standard in glioma surgery, and particularly in glioma resection. Since the introduction of MRI in the clinical practice, several advanced applications have been developed, like functional MRI (fMRI) and diffusion imaging-based tractography (DTI), which both have an application in glioma surgery. fMRI allows to identify cortical areas related to a specific function, DTI allows to reconstruct a model of the sub-cortical connectivity. This paper describes the clinical application of fMRI and DTI, enlightening sensitivity and specificity in comparison to gold standard and underlining their limitations in surgical decision making.

Thought experiment: Decoding cognitive processes from the fMRI data of one individual.

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

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.

Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms.

Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting-state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting-state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting-state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting-state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, but also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI. Hum Brain Mapp 38:2226-2241, 2017. © 2017 Wiley Periodicals, Inc.

Functional Magnetic Resonance Imaging: From Acquisition to Application

Functional magnetic resonance imaging (fMRI) is a technique that exploits magnetic resonance imaging (MRI) to detect regional brain activity through measurement of the hemodynamic response that is coupled to electrical neuronal activity. The most common fMRI method detects blood oxygen level dependent (BOLD) contrast. The BOLD effect represents alteration in the ratio of deoxygenated to oxygenated hemoglobin within brain tissue following neuronal activity. Alterations in this hemoglobin ratio result from changes in cerebral oxygen extraction, cerebral blood flow, and cerebral blood volume that occur in response to neuronal activity. The small, but detectable, change in magnetics resonance signal intensity is due to the sensitivity of magnetic resonance (MR) images to the paramagnetic deoxygenated state of hemoglobin that is the basis of contrast in fMRI applications. This review describes the physical and physiological bases of the MR signal, the principle of the BOLD effect, technical issues related to fMRI implementation, and fMRI experimental design. Research and clinical applications of fMRI are presented, including the use of fMRI in neurosurgical planning. Since it provides an individualized map of brain function, fMRI enables accurate localization of eloquent brain regions prior to surgery, allowing assessment of surgical risk and prognosis, as well as planning surgical approach.

Applications of blood-oxygen-level-dependent functional magnetic resonance imaging and diffusion tensor imaging in epilepsy.

The lifetime prevalence of epilepsy ranges from 2.7 to 12.4 per 1000 in Western countries. Around 30% of patients with epilepsy remain refractory to antiepileptic drugs and continue to have seizures. Noninvasive imaging techniques such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) have helped to better understand mechanisms of seizure generation and propagation, and to localize epileptic, eloquent, and cognitive networks. In this review, the clinical applications of fMRI and DTI are discussed, for mapping cognitive and epileptic networks and organization of white matter tracts in individuals with epilepsy.

Enhanced phase regression with savitzky‐golay filtering for high‐resolution BOLD fMRI

Phase regression exploits the temporal evolution of phase in individual voxels to suppress blood oxygenation level dependent (BOLD) signal fluctuations caused by larger vessels and draining veins while preserving signal changes from microvascular effects. However, this process does not perform well when phase time series have low signal-to-noise ratios because of high levels of physiological noise. We demonstrate that Savitzky-Golay filters may be used to recover the underlying change in phase and completely restore the efficacy of phase regression. We do not make a priori assumptions regarding phase evolution and perform a data-driven exploration of parameter space to select the Savitzky-Golay filter parameters that minimize temporal variance in each voxel after phase regression. This approach is shown to work well on data acquired with single-shot and multi-shot pulse sequences, and should therefore be useful for both human and animal gradient-echo fMRI at high spatial resolutions at high fields. The ability to improve the spatial specificity of BOLD activation may be especially advantageous for clinical applications of fMRI that rely upon the accuracy of individual subject's activation maps to assist with presurgical planning and clinical decision-making. Enhanced phase regression with Savitzky-Golay filtering may also find other uses in analyses of resting state functional connectivity.

Clinical Applications and Future Directions of Functional MRI

First described for use in mapping the human visual cortex in 1991, functional magnetic resonance imaging (fMRI) is based on blood-oxygen level dependent (BOLD) changes in cortical regions that occur during specific tasks. Typically, an overabundance of oxygenated (arterial) blood is supplied during activation of brain areas. Consequently, the venous outflow from the activated areas contains a higher concentration of oxyhemoglobin, which changes the paramagnetic properties of the tissue that can be detected during a T2-star acquisition. fMRI data can be acquired in response to specific tasks or in the resting state. fMRI has been widely applied to studying physiologic and pathophysiologic diseases of the brain. This review will discuss the most common current clinical applications of fMRI as well as emerging directions.

ICA of fMRI Studies: New Approaches and Cutting Edge Applications

Independent component analysis (ICA) is the most commonly used and most diversely applicable exploratory method for the analysis of functional magnetic resonance imaging (fMRI) data. Over the last 10 years it has offered a wealth of insights into brain function during task execution and in the resting state. Independent component analysis is a blind source separation method that was originally applied to identify technical and physiological artifacts in fMRI, and to allow their removal prior to analysis with model-based approaches. It has matured into a method capable of offering a stand-alone assessment of activation on a sound statistical footing. Recent innovations have taken on the challenges of how components should be combined over subjects to allow group inferences, and how activation identified with ICA might be compared between groups – of patients and controls – for instance. Its reputation having been bolstered by multiple successes in the investigation of resting-state networks, ICA is being applied in other cutting edge uses of fMRI; in multivariate pattern analysis, real-time fMRI, in utero studies, with a wide variety of paradigms and stimulus types and with challenging tasks with patients at ultra-high field. These are testament both to ICA's flexibility and its evolving role both in basic neuroscience and clinical applications of fMRI. This Research Topic has attracted 19 contributions from the most renowned researchers in the field, including the inventor of Fast ICA, Aapo Hyvarinen (Hyvarinen and Ramkumar, 2013), and the authors of the most widely used ICA software for fMRI – Christian Beckmann (FSL's MELODIC) and Vince Calhoun (GIFT). The capacity of ICA to find common patterns of activation in huge cohorts of subjects is demonstrated by the parallel computing approach described by Kalcher et al. (2012) and the use of ICA with cutting edge MR methods are presented by the groups of Stefan Posse [Echo Volume Imaging (Posse et al., 2013)], Markus Barth [EEG-fMRI (Meyer et al., 2013) and Ultra-Fast Generalized Inverse Imaging (Boyacioglu et al., 2013)], and Jorge Jovicich [real-time fMRI (Soldati et al., 2013a,b)]. Two articles in this research topic reflect the continued use of ICA to identify artifacts, using the temporal characteristics of components (Rummel et al., 2013) or both temporal and spatial features (Bhaganagarapu et al., 2013). In addition to using frequency signatures to identify noise, the frequencies of signal fluctuations during rest have been studied using temporal ICA (Boubela et al., 2013) and in ultra-fast generalized imaging (Boyacioglu et al., 2013), while Di et al. (2013) examine the influence of amplitude on resting-state connectivity and Balsters et al. (2013) assess the correlation between BOLD spectral power and working memory performance. The ICA applications featured in this Research Topic range from clinical resting-state studies with patients suffering from schizophrenia (Manoliu et al., 2013; Sui et al., 2013) and neurological patients performing chin and hand motor tasks (Robinson et al., 2013) to the investigation of processing streams using chemosensory stimuli (Frasnelli et al., 2012). Combined methodological approaches are used to study belief decision making with fMRI and EEG (Douglas et al., 2013), to discriminate schizophrenia using data from fMRI, DTI, and sMRI (Sui et al., 2013), to identify amyotrophic lateral sclerosis diseased brains (Welsh et al., 2013) and to examine the microvascular specificity of the BOLD effect at 3 and 7 T using SWI (Geissler et al., 2013). We hope this collection of original research articles illustrates the extent to which ICA is becoming an increasingly flexible and potent analysis method – particularly through innovations such as real-time ICA, temporal ICA, and parallel processing implementations – and that the capacity of ICA to isolate the underlying signal sources in fMRI data is being enhanced by multimodal and ultra-fast imaging. These innovations are leading to an increase in the utility of ICA and the richness of information it can provide in both basic research work and clinical applications.

Functional MRI in pre-surgical planning: Case study and cautionary notes

Background. Since its inception almost 20 years ago, functional magnetic resonance imaging (fMRI) has greatly advanced our knowledge of human brain function. Although the clinical applications of fMRI are still limited, there have recently been encouraging advances for its use in pre-operative functional cortical mapping to identify potentially eloquent areas prior to neurosurgery.
 
 Objectives. We explore the potential use of this emerging technique by presenting a neurosurgical case study, as performed at the Cape Universities Brain Imaging Centre (CUBIC), Tygerberg, Cape Town. We conclude with a brief summary of the potential pitfalls of this technique, as well as cautionary guidelines based on our experience.
 
 Methods and results. A 22-year-old male patient from Tygerberg Hospital underwent the successful resection of an anaplastic astrocytoma after fMRI presurgical planning at our facility. The subject was able to leave the ward unassisted. 
 
 Conclusion. If consideration is given to the many limitations of this emerging technique, fMRI can be useful in aiding the neurosurgeon in pre-operative planning of his surgical approach.

Wissenschaftliche Neurobildgebung in der Medizin

Neuroimaging has in recent years greatly contributed to our understanding of a wide range of aspects of central neurological diseases. These include the classification and localization of disease (e.g., in headache), the understanding of pathology (e.g., in Parkinson's disease), mechanisms of reorganization (e.g., in stroke), and the subclinical progress of disease (e.g., in degenerative diseases). Apart form presurgical mapping, clinical applications of fMRI are limited. However, functional imaging enables the formulation of neurobiological hypotheses that can be tested clinically and is suited to test classical clinical hypotheses about how the brain works. Understanding the mechanisms and the site of pathology, e.g., in cluster headaches, will lead and has led to new therapeutic strategies. New methodological developments for neuroscientific applications are aimed at the integration of functional and morphological connectivity through a combination of magnetic resonance techniques (fMRI, DTI) and electrophysiological (EEG, MEG) recordings. In addition to stimulus-dependent activations, resting state activity has found increasing interest, for example, in sleep research and various psychiatric diseases (e.g., schizophrenia, borderline).

Self-paced frequency of a simple motor task and brain activation: An fMRI study in healthy subjects using an on-line monitor device

Application of fMRI to clinical neurology implies the selection of a simple task and control of the task performance. The capability to objectively monitor variables related to task execution is, therefore, important and could improve accuracy of clinical fMRI studies. We assessed the influence of different self-paced frequencies of a simple motor task on brain activation in healthy subjects. A device was developed to measure the force exerted by a subject in pressing an air-filled rubber bulb with the last four fingers of the dominant hand. The task frequency was determined by analysis of the force signal. Nine healthy subjects performed twice the task with self-paced slow (0.35±0.09 Hz), intermediate (0.58±0.21 Hz) or fast (0.98±0.32 Hz) frequency. The device revealed impaired task execution in 1 subject. The coefficient of variation of frequency was 8.7% for slow, 12.2% for intermediate and 15.8% for fast paced task. No significant differences were found comparing the activation maps obtained at slow, intermediate and fast frequencies in the contralateral sensorimotor cortex and ipsilateral cerebellum. Cluster reproducibility was good for location (standard deviation≤7.3 mm), but poor for signal intensity (coefficient of variation 0–176.8%) and extent (coefficient of variation 1.9–140.6%). In conclusion, self-paced frequency variations of a simple motor task in the 0.2–2 Hz range are not a relevant source of the variability of the fMRI results in healthy subjects. Use of the device for evaluation of the neurologically impaired patients might broaden the clinical applications of fMRI.

Functional Magnetic Resonance Imaging for Cranial Neuronavigation: Methods for Automated and Standardized Data Processing and Management

Preoperative fMRI is one of the best established clinical fMRI applications. Due to the difficulties in recording and coregistration of functional image data, we present methods to standardize and automate these procedures. We used a self-made interactive software package (AFI - Automated Functional Imaging) to automate the time consuming and complex analysis of fMRI data. AFI controls the BrainVoyager program, a postprocessing software package, and furthermore facilitates data management, anonymization of patient data, storage, documentation, data export to neuronavigation systems and the opportunity of spatial transformation of image data for use in group studies. By the end of 2006 we have used this method on 123 patients with brain tumors and 47 patients with trigeminal neuralgia. The fundamental basis of multimodal neuronavigation is precise coregistration. EPI images contain spatial distortions of 5-15 mm. We were able to reduce the misregistration of EPI and FLASH images in a selectable region of interest to 1-2 mm. Furthermore AFI reduces the average evaluation time for a standard clinical fMRI study (four functional measurements, one anatomical data set) by approx. 50% from 140 minutes to about 70 minutes in comparison to manual evaluation by an expert. More importantly, the personal attendance time required for the evaluation decreases by 84% to 23 minutes as the remainder of the program runs automatically. In comparison to currently available online postprocessing software tools which are more limited in use, BrainVoyager can be used for coregistration, data export to neuronavigation systems and spatial transformation.

BOLD response during uncoupling of neuronal activity and CBF

The widely used technique of functional magnetic resonance imaging (fMRI) based on the blood oxygenation level-dependent (BOLD) effect is a tool for the investigation of changes in local brain activity upon stimulation. The principle of measurement is based on the assumption that there is a strong coupling between changes in neural activity, metabolism, vascular response and oxygen extraction in the area under investigation. As fMRI is on the way to become a routine tool in clinical examinations, we wanted to investigate whether, generally and under a variety of conditions, there is a strong link between the BOLD signal and neural activity. For clinical and experimental application of the method, it is crucial, whether the absence of changes in BOLD signal intensity upon stimulation can always be interpreted as an absence of changes in brain activity. We approached this question by inhibiting the nitric oxide mediated ‘neurovascular coupling’ via application of 7 nitroindazole. Before and after inhibition of this neurovascular coupling, we acquired evoked potentials and performed fMRI during somatosensory stimulation in rats. Cerebral blood flow response as well as BOLD signal intensity changes following electrical stimulation were abolished within 10 min after application of 7 nitroindazole, whereas somatosensory-evoked potentials were only slightly affected but still clearly detectable. Even 1 h after injection of 7 nitroindazole, there was still remaining electrical activity. Thus, we observed an uncoupling between electrical, i.e., neural activity and the BOLD signal. According to our results, the absence of BOLD signal changes did not permit the conclusion that there was no neural activity in the area under investigation. Our findings are especially relevant for the clinical application of fMRI in patients suffering from cerebrovascular and other brain diseases.

Diagnostic functional MRI: Illustrated clinical applications and decision‐making

Functional magnetic resonance imaging (fMRI) has become a popular research tool, yet its use for diagnostic purposes and actual treatment planning has remained less widespread. The literature yields rather sparse evidence-based data on clinical fMRI applications and accordant decision-making. Notwithstanding, blood oxygenation level dependent (BOLD)- and arterial spin labeling (ASL)-fMRI can be judiciously combined with perfusion measurements, electroencephalographic (EEG) recordings, diffusion-weighted imaging (DWI), and fiber tractographies to assist clinical decisions. In this article we provide an overview of clinical fMRI applications based on illustrative examples. Assessment of cochlear implant candidates by fMRI is covered in some detail, and distinct reference is made to particular challenges imposed by brain tumors, other space-occupying lesions, cortical dysplasias, seizure disorders, and vascular malformations. Specific strategies, merits, and pitfalls of analyzing and interpreting diagnostic fMRI studies in individual patients are highlighted.

Increasing the validity of functional MRI for presurgical language lateralization: proposal for an algorithm

Introduction: In the presurgical evaluation of epilepsy patients, functional MRI (fMRI) increasingly replaces the intracarotid amobarbital procedure (IAP) for language lateralization. However, recent studies reveal relevant discrepancy between both methods on the single patient level. This questions the uncritical application of fMRI as the only tool for language lateralization. Conditions have to be known, under which fMRI may lateralize language incorrectly. Materials and Methods: 238 patients received fMRI, 65 of them also IAP. By applying the criteria (1) absence of fMRI-interfering lesions, (2) sufficient patient's compliance inside the scanner, and (3) application of lateralization indices high enough to identify unilateral language dominance in accordance with IAP we identified patients feasible for isolated fMRI-based language lateralization. Application of an algorithm comprising these criteria to 238 patients allows to estimate the value of fMRI for presurgical language lateralization. Results: In our series, 22.6% of patients had lesions potentially interfering with fMRI, 4.7% showed compliance questioning sufficient attention to the given task. 60.3% of the patients failed to show the protocol specific lateralization index that guarantees for complete concordance between fMRI and IAP. According the proposed algorithm, fMRI was safe enough to be the only tool for language lateralization in only 32% of patients. Conclusions: This algorithm represent a conservative approach to clinical application of fMRI. However, when fMRI is used for presurgical language lateralization on the single patient level, it's specificity should be maximized even on cost of it's sensitivity. In patients with doubtful fMRI, IAP should be performed.

Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

Patterns of brain activity during deception have recently been characterized with fMRI on the multi-subject average group level. The clinical value of fMRI in lie detection will be determined by the ability to detect deception in individual subjects, rather than group averages. High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with lie and truth. In 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. Predictive accuracy, assessed by cross-validation in participants not included in training, was 88%. The results demonstrate the potential of non-linear machine learning techniques in lie detection and other possible clinical applications of fMRI in individual subjects, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI.

Brain Plasticity and Clinical Applications of fMRI

Brain Plasticity and Clinical Applications of fMRI