Although the first reports of rs-fMRI clinical applications appeared 10 years ago 11 , rs-fMRI use in clinical neuroradiology practice remains in a nascent stage, limited mainly to pre-surgical planning 4 , 12 and is typically performed in conjunction with task-fMRI. Given the rapid rise and widespread use of rs-fMRI in neuroimaging clinical research, it might be expected that rs-fMRI would already be widely used in clinical practice, particularly in academic centers. Nevertheless, this is not the case and the reasons for the relatively weak penetration of rs-fMRI methods into neuroradiology practice are not entirely clear.
In this article we will discuss the results of this survey, covering opinions about the current state of rs-fMRI acquisition, analysis, and interpretation methods. We then address existing barriers to using rs-fMRI in clinical practice and propose possible solutions, presenting examples of typical group and individual subject rs-fMRI analyses using public domain data.
After obtaining a human subjects research exemption, an invitation to participate in a 20 item electronic survey was sent to ASFNR members to collect information concerning their use of rs-fMRI in clinical research and practice, demographics, and work environment. Responses were collected using 5-point Likert items and deidentified prior to analysis. Because a majority of respondents expressed concerns that substantial analysis and interpretation problems need to be solved before rs-fMRI can be widely used in clinical practice, we next explored examples of typical rs-fMRI analysis variations using the publicly available NYU CSC TRT dataset www.
In one example, we explored the serial influence of time series preprocessing algorithms on language network detection using an inferior frontal gyrus ROI. Effects of applying global signal regression, incorporating head motion estimates, using anatomical CompCorr, and outlier elimination were examined in a group level analysis of 25 healthy participants. Next, we explored the effects of denoising on single participant data. The exercise revealed large effects that processing variations can have on the detection of domain-specific maps at the group or single-subject level.
These results are presented in the discussion of existing barriers related to increasing rs-fMRI use in clinical practice. Of these, the majority were involved in both clinical and research activities. Twenty-one percent were female. Only two of the respondents were exclusively involved in research. The median time since training was 12 years.
Seventy-two percent reported plans to use rs-fMRI for research in the next year. Eighty-two percent of respondents agreed or strongly agreed that task-fMRI and rs-fMRI clinical use are largely confined to pre-surgical planning, mentioning seizure focus detection as other promising application. While respondents expressed strong interest in rs-fMRI clinical applications, they expressed concerns that may explain its lack of penetration into clinical practice.
Twenty-four percent expressed concern about the reliability and reproducibility of rs-fMRI in identifying canonical brain networks. Seventy-seven percent agreed, or strongly agreed, that there are substantial analysis problems to be solved before rs-fMRI can be widely used in clinical practice. In summary, while most respondents had experience with fMRI in both clinical and research contexts, have adequate MRI systems at their institutions and are relatively enthusiastic about incorporating rs-fMRI into clinical protocols, a number of concerns appear to be slowing the translation of rs-fMRI from research to practice.
Some barriers to rs-fMRI implementation in clinical practice, and possible ways to circumvent them, are addressed below. Functional MRI used in research settings typically averages participant data in order to detect differences in regional task effects between clinical and healthy groups. In clinical medicine, however, diagnostic inferences and treatment recommendations are made for single cases. As most publications describe acquisition and analysis methods optimized to detect between-group effects, better methods to characterize rs-fMRI maps in individuals are needed.
Acquisition technology advances, such as higher magnetic field strength, multi-channel coils, and faster image acquisition have led to substantial sensitivity improvements, making the study of individual resting state networks possible One simple way to improve network detection sensitivity is to lengthen scan time.
While some canonical RSNs, such as the default mode or sensorimotor networks, can be reliably detected at the group level using 5—6 min scans, longer sampling times, on the order of 12—30 min, can substantially improve detection of networks exhibiting lower average connectivity 15 , Since rs-fMRI data is dominated by physiological noise, longer sampling times with short TRs allow more effective physiological denoising and more sensitive neural signal detection.jlhghfdg.co.vu/sopa-de-letras-zig-zag-eurocopa-1960-2020.php
While most analysis techniques assume static connectivity effects between pairs of network nodes, dynamic connectivity estimates can benefit even more from longer sampling times. Dynamic connectivity analysis, while relatively new to rs-fMRI, holds promise in providing quantitative estimates of time-varying connection phenomena that may be altered in brain disease Variance in intrinsic connectivity contributed by cognitive state and mood, rather than disease effects, may be responsible for individual network structure variation Nevertheless, moderate-to-high test-retest reliability of rs-fMRI indices challenges these concerns In addition, longer sampling times, as discussed above, can facilitate detection of individual static network structure in the face of moderate dynamic variations in connectivity.
While rs-fMRI is currently being used for preoperative planning in a few centers 20 , other clinical applications are not as common. High within-subject reproducibility of RSNs suggests that they might serve as biomarkers for monitoring disease progression in individual patients Finally, tools comparing individual to group maps are needed. Structural templates based on normative data sets that take into account age, sex, magnetic field strength, and data quality have been developed Standardizing rs-fMRI acquisition protocols, then collecting normative comparative data, would greatly facilitate rs-fMRI clinical use by allowing comparison of individuals to age, sex, and IQ adjusted norms.
For example, a clinically relevant target, the left hemisphere language network, when identified using a left inferior gyrus ROI, exhibits substantial between-subject variability, even when averaging across three collection sessions Supplement Figure 1. Of greater concern is the fact that the majority of patients referred for pre-surgical mapping have space occupying lesions that distort both local and global anatomy, making mapping to standard anatomical spaces difficult or impossible using conventional spatial normalization techniques.
Moreover, slowly growing tumors may dynamically alter inter-regional connectivity, making comparisons to functional group maps derived from healthy participants difficult to interpret. In pre-surgical planning, precisely determining the details of how an individual patient's functional anatomy differs from a typical spatial distribution may be important in determining treatment recommendations.
These different connectivity modeling techniques may measure fundamentally different aspects of inter-regional coupling. It is also unclear which connectivity measures are sensitive to specific pathologies and therefore are most appropriate to particular clinical questions. In addition, techniques for ICA network identification have not been standardized and are quite sensitive to specification of the maximum number of identified components.
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Increasing the maximum number can cause large networks to split into smaller subsets. A major limitation of network analysis methods based on graph theory metrics is that group sizes larger than 40—50 are required to obtain stable estimates of network properties using short acquisition protocols, making them difficult to use in characterizing individual patients Nevertheless, novel indices, like the hub disruption index, may be useful in characterizing an individual's relationship to a group For all of these techniques, compensating for anatomical distortion from space occupying lesions presents a substantial analytical challenge.
Recently, there has been growing concern about the reliability and reproducibility of biomedical research Our survey demonstrates that the neuroradiology community shares this concern with respect to rs-fMRI. Identifying reliable and reproducible canonical brain networks has received great attention in the rs-fMRI literature, with studies showing reproducible networks in both adults and children 27 , Yet, the neuroradiology community remains uncertain about how these findings translate to individual patients. More individual participant test-retest studies may be needed to address this area of uncertainty.
Large test-retest data sets, focusing on rs-fMRI from over 36 laboratories around the world, have been made publicly available by the Consortium for Reliability and Reproducibility CoRR through the International Data-sharing Neuroimaging Initiative The individual scans composing the large aggregate dataset have been collected using different acquisition parameters and experimental designs, allowing investigators to assess rs-fMRI reliability and reproducibility. In addition, the impact of commonly encountered artifacts, such as motion, on inter-individual variation can be explored In addition, there have not yet been any large scale validation studies to determine if the cognitive domains commonly mapped using intraoperative cortical stimulation can be identified using rs-fMRI.
Most rs-fMRI validation studies compare to task-fMRI results, which are expected to have better specificity for specific functions, making simple comparisons difficult.
Comparisons between cortical stimulation and other functional imaging modalities have previously shown good between modality correspondence 31 , suggesting that this strategy may be useful. While a majority of survey respondents indicated that rs-fMRI data are relatively easy to collect, the majority also believed that rs-fMRI data are relatively difficult to process.
Resting state data analysis can be time intensive and, therefore, not always feasible during a typical demanding day on clinical service. Automatic transfer of images to a clinical image archiving system, followed by automated analysis, could facilitate clinical workflows. One popular analysis program, the CONN Toolbox 13 , while well suited for automated analysis of group rs-fMRI data, has limited options for single subject statistical analysis. Nevertheless, a CONN Toolbox script optimized for clinical use and running on a typical laboratory computer requires 10—15 min to process data from a single subject, in addition to the time required to transfer images from PACS.
Other toolboxes designed for clinical practitioners, such as CLINICA 32 , are not yet widely used, but do hold promise for single subject analysis. Hemodynamic signal artifacts resulting from physiological noise, including head motion, cardiac pulsation, and respiratory effects can severely compromise efforts to detect regional modulations in neural activity. Participant head motion is particularly problematic, as it can bias estimated activity correlations between regions.
Visual examination of a participant's scan immediately after completion, using a movie loop, allows a clinician to repeat scans when excessive head motion is detected. For this reason, motion correction using rigid body realignment is an obligate part of the rs-fMRI preprocessing pipeline, followed by inclusion of motion estimates in subsequent single-subject statistical modeling Even images from cooperative patients will have physiologic confounds that need to be addressed. Cardiac pulsation and respiration can cause spurious connectivity patterns Band-pass filtering to remove fluctuations outside the frequency range of interest mitigates cardiac and respiratory effects and does not require external physiological recordings.
Global signal regression 20 is another method sometimes used for physiologic noise reduction GSR uses a denoising covariate that contains information from both physiological noise and neural signal. Its re-centers the mean of the inter-regional correlation distribution, so that some positive correlations appear to be negative.
Its use may therefore confound attempts to distinguish sets of regions whose activity are either positively or negatively associated For this reason, noise reduction techniques like anatomical CompCorr, that exclude the cortical signal from the denoising procedure, may be preferred in most circumstances 13 Figure 2.
Figure 2. Denoising effects on functional connectivity estimates. A Additive effects of denoising sources on detection of seed connectivity in a group of 25 healthy participants studied during three sessions.
B Denoising reduces structured noise in individuals. Left—Connectivity values histograms in a single healthy participant before gray and after yellow denoising including WM signal, CSF signal, estimated head motion, and outlier removal. Middle—Global signal variation before and after denoising. Right—carpet plot of voxel signal variation before top and after bottom denoising.
C Denoising increases sensitivity to, and specificity for, the language network. Effects of including denoising sources on detection of left inferior frontal gyrus seed connectivity are seen in a single participant. Systemic carbon dioxide CO 2 fluctuations alter BOLD-contrast and contribute to respiratory induced signal variation To reduce CO 2 fluctuation effects, end-tidal CO 2 can be measured with a face-mask or nasal cannula and the measurements incorporated into the denoising pipeline Temporal signal-to-noise ratio tSNR , the ratio of the mean signal over its temporal standard deviation SD , reflects the ability to detect BOLD-contrast signal changes 40 , and thus can be used in quality assurance.
More recently, the Physiological Contributions in Spontaneous Oscillations index has been proposed as a more sensitive measure of functional connectivity strength These denoising techniques are not only effective at the group level Figures 2A,B , but also can improve sensitivity and specificity for detecting networks at the individual level Figure 2C.
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In summary, the inter-regional associations estimated with rs-fMRI may be relatively weak compared to the customary task-fMRI effects, often being masked by physiological noise. The reproducibility of the two modalities may also differ. Varying acquisition and processing parameters can profoundly affect detection sensitivity 42 and there is ongoing debate regarding the role of GSR in pre-processing 43 — Further, different data analysis families such as ROI-based correlation analysis, independent component analysis ICA detection of canonical networks, and graph theory metrics used to quantify local and global network properties, are likely to be sensitive to very different aspects of inter-regional functional connectivity 3.
Traditionally, diagnostic radiology has been primarily an anatomical medical specialty. Functional MRI acquisition and interpretation is more physiological and statistical in nature and may therefore may require somewhat different training. While many academic programs briefly expose trainees to the principles of functional MRI, it is presently not part of the standard curriculum in diagnostic radiology residency or neuroradiology fellowship programs in the U.
More training in software systems for rs-fMRI analysis will facilitate clinical practice implementation. Relevant curricular offerings in systems neuroscience and statistical modeling could help trainees gain a deeper understanding of the origins of instrumental and physiological noise in rs-fMRI data and thereby optimize their data acquisition, analysis, and interpretation efforts. The lack of standardization of rs-fMRI acquisition and analysis methods may reflect a lack of consensus regarding the best approach to maximize inter-individual signal variability while concomitantly minimizing intra-subject measure variability As task-fMRI analysis methods are relatively mature compared to their rs-fMRI counterparts, more vigorous engagement of professional societies with the rs-fMRI research community will promote achieving agreement concerning rs-fMRI analysis standards.
Overcoming these hurdles will require a concerted effort from the interested academic and commercial parties. MRI system vendors could have a major role in these activities, working with academic investigators to develop software tools and techniques in accordance with standard medical device development practices, thereby speeding the transition from research to practice. Acquiring the expertise needed for rs-fMRI acquisition, analysis, and interpretation requires a substantial time commitment.
Busy clinicians may be more motivated to obtain such training, and their associated hospitals be more willing to support them, if rs-fMRI had an associated Current Procedural Terminology CPT code. Before this can happen, however, rs-fMRI protocols must be standardized by neuroradiologists.
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Even after standardization and regulatory hurdles are overcome, it will be necessary to identify the clinical applications for which rs-fMRI can provide useful information to referring physicians from neurosurgery, neurology and psychiatry. Physical description 1 online resource xxi, p.
Online Available online. More options. Find it at other libraries via WorldCat Limited preview. Contributor Faro, Scott H. Mohamed, Feroze B. Law, Meng. Ulmer, John L. SpringerLink Online service. Bibliography Includes bibliographical references and index.
Contents Pt. Diffusion and perfusion imaging Pt. Magnetic resonance spectroscopy Pt. Multi modality functional neuroradiology Pt. Diffusion tensor imaging Pt. Beyond proton imaging Pt. Functional spine and CSF imaging Pt.
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