Applications of fMRI in translational medicine and clinical practice

Neuromodulation of Cerebral Blood Flow: A Physiological Mechanism and Methodological Review of Neurovascular Coupling

Neurovascular coupling (NVC) refers to the dynamic regulation of cerebral blood flow via neuronal activity, a mechanism crucial for maintaining normal brain function. This review elucidates the intricate physiological mechanisms underlying NVC, emphasizing the coordinated roles of neurons, glial cells, and vascular cells in mediating activity-induced changes in blood flow. We examine how NVC is impaired in neurological disorders such as Alzheimer's disease and stroke, where the dysfunction of this coupling contributes to neurodegeneration and neurological deficits. A broad range of techniques for assessing NVC is discussed-encompassing the established modalities like transcranial Doppler, near-infrared spectroscopy, and functional magnetic resonance imaging (fMRI), as well as emerging technologies such as functional ultrasound imaging and miniaturized endoscopy that enable high-resolution monitoring in deep brain regions. We also highlight the computational modeling approaches for simulating NVC dynamics and identify the novel biomarkers of NVC dysfunction with potential utility in early diagnosis. Finally, emerging translational applications-including neuromodulation techniques and targeted pharmacological interventions-are explored as means to restore normal neurovascular function. These advancements underscore the clinical significance of NVC research, paving the way for improved diagnostic tools and therapeutic strategies in neurological disorders.

Enhancing fMRI quality control

BACKGROUND

fMRI in clinical settings faces challenges affecting activity maps. Template matching can screen for abnormal results by providing an objective metric of activity map quality. This research tests how sample size, age, or gender-specific templates, and unilateral templates affect template matching results.

NEW METHOD

We used an fMRI database of 76 healthy subjects performing 7 tasks assessing motor, language, and working memory functions. Templates were created with varying numbers of subjects, genders, and ages. Individual subjects were compared to templates using leave-one-out cross validation. We also compared unilateral and bilateral templates.

RESULTS

Increasing sample size improved template matches, with diminishing returns for larger sample sizes. Gender and age-specific templates increased correlations for some tasks, with age having a larger effect than gender. Generally, templates including all subjects provided the highest correlations, indicating that age and gender effects did not outweigh the benefits of larger sample sizes. Unilateral templates of the task-dominant hemisphere increased template correlations.

CONCLUSIONS

Age and gender affect templates, but the benefits depend on the database size. When the database is large enough, age and gender effects are beneficial. Unilateral templates enhance template matching, but practical benefits depend on the severity of neurological abnormalities in patients.

Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia

BACKGROUND

Intermediate phenotypes, such as characteristic neuroimaging patterns, offer unique insights into the genetic and stress-related underpinnings of neuropsychiatric disorders like depression. This study aimed to identify neuroimaging intermediate phenotypes associated with depression, bridging etiological factors to behavioral manifestations and connecting insights from animal models to diverse clinical populations.

METHODS

We analyzed datasets from both rodents and humans. The rodent studies included a genetic model (P11 knockout) and an environmental stress model (chronic unpredictable mild stress), while the human data comprised 748 participants from three cohorts. Using the amplitude of low-frequency fluctuations, we identified neuroimaging patterns in rodent models. We then applied a machine-learning approach to cluster neuroimaging subtypes of depression. To assess the genetic predispositions and stress-related changes associated with these subtypes, we analyzed genotype and metabolite data. Linear regression was employed to determine which neuroimaging features predicted core depression symptoms across species.

RESULTS

The genetic and environmental stress models exhibited distinct neuroimaging patterns in subcortical and sensorimotor regions. Consistent patterns emerged in two neuroimaging subtypes identified across three independent depressed cohorts. The subtype resembling P11 knockout demonstrated higher genetic susceptibility, with enriched expression of risk genes in brain tissues and abnormal metabolites linked to tryptophan metabolism. In contrast, the stress animal-like subtype did not show changes in genetic risk scores but exhibited enriched risk gene expression in somatic and endocrine tissues, along with mitochondrial dysfunction in the antioxidant stress system. Notably, these distinct subcortical-sensorimotor neuroimaging patterns predicted anhedonia, a core symptom of depression, in both rodent models and depressed subtypes.

CONCLUSIONS

This cross-species validation suggests that these neuroimaging patterns may serve as robust intermediate phenotypes, linking etiology to anhedonia and facilitating the translation of findings from animal models to humans with depression and other psychiatric disorders.

Probabilistic Presurgical Language fMRI Atlas of Patients with Brain Tumors

BACKGROUND AND PURPOSE

Patients with brain tumors have high intersubject variation in putative language regions, which may limit the utility of straightforward application of healthy subject brain atlases in clinical scenarios. The purpose of this study was to develop a probabilistic functional brain atlas that consolidates language functional activations of sentence completion and Silent Word Generation language paradigms using a large sample of patients with brain tumors.

MATERIALS AND METHODS

The atlas was developed using retrospectively collected fMRI data from patients with brain tumors who underwent their first standard-of-care presurgical language fMRI scan at our institution between July 18, 2015, and May 13, 2022. Three hundred seventeen patients (861 fMRI scans) were used to develop the language functional atlas. An independent presurgical language fMRI data set of 39 patients with brain tumors from a previous study was used to evaluate our atlas. Family-wise error-corrected binary functional activation maps from sentence completion, letter fluency, and category fluency presurgical fMRI were used to create probability overlap maps and pooled probabilistic overlap maps in Montreal Neurological Institute standard space. The Wilcoxon signed-rank test was used to determine a significant difference in the maximum Dice coefficient for our atlas compared with a meta-analysis-based template with respect to expert-delineated primary language area activations.

RESULTS

Probabilities of activating the left anterior primary language area and left posterior primary language area in the temporal lobe were 87.9% and 91.5%, respectively, for sentence completion, 88.5% and 74.2%, respectively, for letter fluency, and 83.6% and 67.6%, respectively, for category fluency. Maximum Dice coefficients for templates derived from our language atlas were significantly higher than the meta-analysis-based template in the left anterior primary language area (0.351 and 0.326, respectively, P < .05) and the left posterior primary language area in the temporal lobe (0.274 and 0.244, respectively, P < .005).

CONCLUSIONS

Brain tumor patient- and paradigm-specific probabilistic language atlases were developed. These atlases had superior spatial agreement with fMRI activations in individual patients compared with the meta-analysis-based template.

Synthetic data in generalizable, learning-based neuroimaging

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Comparative analysis of brain language templates with primary language areas detected from presurgical fMRI of brain tumor patients

INTRODUCTION

Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients.

METHODS

Four language templates (A-D) based on anatomy, task-based fMRI, resting-state fMRI, and meta-analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated.

RESULTS

For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D.

CONCLUSION

This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies.

First implementation of dynamic oxygen-17 (17O) magnetic resonance imaging at 7 Tesla during neuronal stimulation in the human brain

OBJECTIVE

First implementation of dynamic oxygen-17 (17O) MRI at 7 Tesla (T) during neuronal stimulation in the human brain.

METHODS

Five healthy volunteers underwent a three-phase 17O gas (17O2) inhalation experiment. Combined right-side visual stimulus and right-hand finger tapping were used to achieve neuronal stimulation in the left cerebral hemisphere. Data analysis included the evaluation of the relative partial volume (PV)-corrected time evolution of absolute 17O water (H217O) concentration and of the relative signal evolution without PV correction. Statistical analysis was performed using a one-tailed paired t test. Blood oxygen level-dependent (BOLD) experiments were performed to validate the stimulation paradigm.

RESULTS

The BOLD maps showed significant activity in the stimulated left visual and sensorimotor cortex compared to the non-stimulated right side. PV correction of 17O MR data resulted in high signal fluctuations with a noise level of 10% due to small regions of interest (ROI), impeding further quantitative analysis. Statistical evaluation of the relative H217O signal with PV correction (p = 0.168) and without (p = 0.382) did not show significant difference between the stimulated left and non-stimulated right sensorimotor ROI.

DISCUSSION

The change of cerebral oxygen metabolism induced by sensorimotor and visual stimulation is not large enough to be reliably detected with the current setup and methodology of dynamic 17O MRI at 7 T.

The role of research design in the reproducibility of L1 and L2 language networks: A review of bilingual neuroimaging meta-analyses

Meta-analyses are a method by which to increase the statistical power and generalizability of neuroimaging findings. In the neurolinguistics literature, meta-analyses have the potential to substantiate hypotheses about L1 and L2 processing networks and to reveal differences between the two that may escape detection in individual studies. Why then is there so little consensus between the reported findings of even the most recently published and most highly powered meta-analyses? Limitations in the literature, such as the absence of a common method to define and measure descriptive categories (e.g., proficiency level, degree of language exposure, age of acquisition, etc.) are often cited. An equally plausible explanation lies in the technical details of how individual meta-analyses are conducted. This paper provides a review of recent meta-analyses, with a discussion of their methodological choices and the possible effect those choices may have on the reported findings.

Ratiometric Fluorescent Probe Promotes Trans-differentiation of Human Mesenchymal Stem Cells to Neurons

Development of multifunctional theranostics is challenging and crucial for deciphering complex biological phenomena and subsequently treating critical disease. In particular, development of theranostics for traumatic brain injury (TBI) and understanding its repair mechanism are challenging and highly complex areas of research. Recently, there have been interesting pieces of research work demonstrated that a small molecule-based neuroregenerative approach using stem cells has potential for future therapeutic lead development for TBI. However, these works demonstrated the application of a mixture of multiple molecules as a "chemical cocktail", which may have serious toxic effects in the differentiated cells. Therefore, development of a single-molecule-based potential differentiating agent for human mesenchymal stem cells (hMSCs) into functional neurons is vital for the upcoming neuro-regenerative therapeutics. This lead could be further extraploted for the design of theranostics for TBI. In this study, we have developed a multifunctional single-molecule-based fluorescent probe, which can image the transdifferentiated neurons as well as promote the differentiation process. We demonstrated a promising class of fluorescent probes (CP-4) that can be employed to convert hMSCs into neurons in the presence of fibroblast growth factor (FGF). This fluorescent probe was used in cellular imaging as its fluorescence intensity remained unaltered for up to 7 days of trans-differentiation. We envision that this imaging probe can have an important application in the study of neuropathological and neurodegenerative studies.

Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference

Metabolic, hormonal, autonomic and physiological rhythms may have a significant impact on cerebral hemodynamics and intrinsic brain synchronization measured with fMRI (the resting-state connectome). The impact of their characteristic time scales (hourly, circadian, seasonal), and consequently scan timing effects, on brain topology in inherently heterogeneous developing connectomes remains elusive. In a cohort of 4102 early adolescents with resting-state fMRI (median age = 120.0 months; 53.1 % females) from the Adolescent Brain Cognitive Development Study, this study investigated associations between scan time-of-day, time-of-week (school day vs weekend) and time-of-year (school year vs summer vacation) and topological properties of resting-state connectomes at multiple spatial scales. On average, participants were scanned around 2 pm, primarily during school days (60.9 %), and during the school year (74.6 %). Scan time-of-day was negatively correlated with multiple whole-brain, network-specific and regional topological properties (with the exception of a positive correlation with modularity), primarily of visual, dorsal attention, salience, frontoparietal control networks, and the basal ganglia. Being scanned during the weekend (vs a school day) was correlated with topological differences in the hippocampus and temporoparietal networks. Being scanned during the summer vacation (vs the school year) was consistently positively associated with multiple topological properties of bilateral visual, and to a lesser extent somatomotor, dorsal attention and temporoparietal networks. Time parameter interactions suggested that being scanned during the weekend and summer vacation enhanced the positive effects of being scanned in the morning. Time-of-day effects were overall small but spatially extensive, and time-of-week and time-of-year effects varied from small to large (Cohen's f ≤ 0.1, Cohen's d<0.82, p < 0.05). Together, these parameters were also positively correlated with temporal fMRI signal variability but only in the left hemisphere. Finally, confounding effects of scan time parameters on relationships between connectome properties and cognitive task performance were assessed using the ABCD neurocognitive battery. Although most relationships were unaffected by scan time parameters, their combined inclusion eliminated associations between properties of visual and somatomotor networks and performance in the Matrix Reasoning and Pattern Comparison Processing Speed tasks. Thus, scan time of day, week and year may impact measurements of adolescent brain's functional circuits, and should be accounted for in studies on their associations with cognitive performance, in order to reduce the probability of incorrect inference.

Arterial occlusion duration affects the cuff-induced hyperemic response in skeletal muscle BOLD perfusion imaging as shown in young healthy subjects

OBJECTIVE

Dynamic BOLD MRI with cuff compression, inducing ischemia and post-occlusive hyperemia in skeletal muscle, has been pointed out as a potential diagnostic tool to assess peripheral limb perfusion. The objective was to explore the robustness of this technique and its sensitivity to the occlusion duration.

MATERIALS AND METHODS

BOLD images were acquired at 3 T in 14 healthy volunteers. [Formula: see text]-imaging with 5- and 1.5-min occlusions were acquired and several semi-quantitative BOLD parameters were derived from ROI-based [Formula: see text]-time curves. Differences in parameters from the two different occlusion durations were evaluated in the gastrocnemius and soleus muscles using non-parametrical tests. Intra- and inter-scan repeatability were evaluated with coefficient of variation.

RESULTS

Longer occlusion duration resulted in an increased hyperemic signal effect yielding significantly different values (p < 0.05) in gastrocnemius for all parameters describing the hyperemic response, and in soleus for two of these parameters. Specifically, 5-min occlusion yielded steeper hyperemic upslope in gastrocnemius (41.0%; p < 0.05) and soleus (59.7%; p = 0.03), shorter time to half peak in gastrocnemius (46.9%; p = 0.00008) and soleus (33.5%; p = 0.0003), and shorter time to peak in gastrocnemius (13.5%; p = 0.02). Coefficients of variation were lower than percentage differences that were found significant.

DISCUSSION

Findings show that the occlusion duration indeed influences the hyperemic response and thus should play a part in future methodological developments.

A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.

Synergistic structural and functional alterations in the medial prefrontal cortex of patients with high-grade gliomas infiltrating the thalamus and the basal ganglia

Background

High-grade gliomas (HGGs) are characterized by a high degree of tissue invasion and uncontrolled cell proliferation, inevitably damaging the thalamus and the basal ganglia. The thalamus exhibits a high level of structural and functional connectivity with the default mode network (DMN). The present study investigated the structural and functional compensation within the DMN in HGGs invading the thalamus along with the basal ganglia (HITBG).

Methods

A total of 32 and 22 healthy controls were enrolled, and their demographics and neurocognition (digit span test, DST) were assessed. Of the 32 patients, 18 patients were involved only on the left side, while 15 of them were involved on the right side. This study assessed the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), gray matter (GM) volume, and functional connectivity (FC) within the DMN and compared these measures between patients with left and right HITBG and healthy controls (HCs).

Result

The medial prefrontal cortex (mPFC) region existed in synchrony with the significant increase in ALFF and GM volume in patients with left and right HITBG compared with HCs. In addition, patients with left HITBG exhibited elevated ReHo and GM precuneus volumes, which did not overlap with the findings in patients with right HITBG. The patients with left and right HITBG showed decreased GM volume in the contralateral hippocampus without any functional variation. However, no significant difference in FC values was observed in the regions within the DMN. Additionally, the DST scores were significantly lower in patients with HITBG, but there was no significant correlation with functional or GM volume measurements.

Conclusion

The observed pattern of synchrony between structure and function was present in the neuroplasticity of the mPFC and the precuneus. However, patients with HITBG may have a limited capacity to affect the connectivity within the regions of the DMN. Furthermore, the contralateral hippocampus in patients with HITBG exhibited atrophy. Thus, preventing damage to these regions may potentially delay the progression of neurological function impairment in patients with HGG.

Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type

To improve 'bench-to-bedside' translation, it is integral that knowledge flows bidirectionally-from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data-an emerging mode applicable in animal models-as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.

A Cumulants-Based Human Brain Decoding

Human cognition is influenced by the way the nervous system processes information and is linked to this mechanical explanation of the human body’s cognitive function. Accuracy is the key emphasis in neuroscience which may be enhanced by utilising new hardware, mathematical, statistical, and computational methodologies. Feature extraction and feature selection also play a crucial function in gaining improved accuracy since the proper characteristics can identify brain states efficiently. However, both feature extraction and selection procedures are dependent on mathematical and statistical techniques which implies that mathematical and statistical techniques have a direct or indirect influence on prediction accuracy. The forthcoming challenges of the brain-computer interface necessitate a thorough critical understanding of the complicated structure and uncertain behavior of the brain. It is impossible to upgrade hardware periodically, and thus, an option is necessary to collect maximum information from the brain against varied actions. The mathematical and statistical combination could be the ideal answer for neuroscientists which can be utilised for feature extraction, feature selection, and classification. That is why in this research a statistical technique is offered together with specialised feature extraction and selection methods to increase the accuracy. A score fusion function is changed utilising an enhanced cumulants-driven likelihood ratio test employing multivariate pattern analysis. Functional MRI data were acquired from 12 patients versus a visual test that comprises of pictures from five distinct categories. After cleaning the data, feature extraction and selection were done using mathematical approaches, and lastly, the best match of the projected class was established using the likelihood ratio test. To validate the suggested approach, it is compared with the current methods reported in recent research.

Photoacoustic imaging of squirrel monkey cortical and subcortical brain regions during peripheral electrical stimulation

The investigation of neuronal activity in non-human primate models is of critical importance due to their genetic similarity to human brains. In this study, we tested the feasibility of using photoacoustic imaging for the detection of cortical and subcortical responses due to peripheral electrical stimulation in a squirrel monkey model. Photoacoustic computed tomography and photoacoustic microscopy were applied on squirrel monkeys for real-time deep subcortical imaging and optical-resolution cortical imaging, respectively. The electrically evoked hemodynamic changes in primary somatosensory cortex, premotor cortices, primary motor cortex, and underlying subcortical areas were measured. Hemodynamic responses were observed in both cortical and subcortical brain areas at the cortices during external stimulation, demonstrating the feasibility of photoacoustic technique for functional imaging of non-human primate brain.

Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

Functional MRI evidence for reorganization of language networks after stroke

In this chapter, we review fMRI evidence for language reorganization in individuals with poststroke aphasia. Several studies in the current literature have utilized fMRI as a tool to understand patterns of functional reorganization in poststroke aphasia. Consistent with previous models that have been proposed to explain the trajectory of language recovery, differential patterns of language processing and language recovery have been identified across individuals with poststroke aphasia in different stages of recovery. Overall, a global network breakdown typically occurs in the early stages of aphasia recovery, followed by normalization in "traditional" left hemisphere language networks. Depending on individual characteristics, right hemisphere regions and bilateral domain-general regions may be further recruited. The main takeaway of this chapter is that poststroke aphasia recovery does not depend on individual neural regions, but rather involves a complex interaction among regions in larger networks. Many of the unresolved issues and contrastive findings in the literature warrant further research with larger groups of participants and standard protocols of fMRI implementation.

Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network

Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.

Arterial blood stealing as a mechanism of negative BOLD response: From the steady-flow with nonlinear phase separation to a windkessel-based model

Blood Oxygen Level Dependent (BOLD) signal indirectly characterizes neuronal activity by measuring hemodynamic and metabolic changes in the nearby microvasculature. A deeper understanding of how localized changes in electrical, metabolic and hemodynamic factors translate into a BOLD signal is crucial for the interpretation of functional brain imaging techniques. While positive BOLD responses (PBR) are widely considered to be linked with neuronal activation, the origins of negative BOLD responses (NBR) have remained largely unknown. As NBRs are sometimes observed in close proximity of regions with PBR, a blood "stealing" effect, i.e., redirection of blood from a passive periphery to the area with high neuronal activity, has been postulated. In this study, we used the Hagen-Poiseuille equation to model hemodynamics in an idealized microvascular network that account for the particulate nature of blood and nonlinearities arising from the red blood cell (RBC) distribution (i.e., the Fåhraeus, Fåhraeus-Lindqvist and the phase separation effects). Using this detailed model, we evaluate determinants driving this "stealing" effect in a microvascular network with geometric parameters within physiological ranges. Model simulations predict that during localized cerebral blood flow (CBF) increases due to neuronal activation-hyperemic response, blood from surrounding vessels is reallocated towards the activated region. This stealing effect depended on the resistance of the microvasculature and the uneven distribution of RBCs at vessel bifurcations. A parsimonious model consisting of two-connected windkessel regions sharing a supplying artery was proposed to simulate the stealing effect with a minimum number of parameters. Comparison with the detailed model showed that the parsimonious model can reproduce the observed response for hematocrit values within the physiological range for different species. Our novel parsimonious model promise to be of use for statistical inference (top-down analysis) from direct blood flow measurements (e.g., arterial spin labeling and laser Doppler/Speckle flowmetry), and when combined with theoretical models for oxygen extraction/diffusion will help account for some types of NBRs.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

PRATEEK: Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research

BACKGROUND

In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiles, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological diseases.

OBJECTIVE

To develop a processing scheme for multimodal data integration in a seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer's disease).

METHODS

A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- "PRATEEK".

RESULTS

The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants. The data-integration scheme was tested for bilateral occipital cortices as the regions of interest and can also be extended to other anatomical regions. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities.

CONCLUSION

This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role for advance brain research.

Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling

Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

Correcting Susceptibility Artifacts of MRI Sensors in Brain Scanning: A 3D Anatomy-Guided Deep Learning Approach

Echo planar imaging (EPI), a fast magnetic resonance imaging technique, is a powerful tool in functional neuroimaging studies. However, susceptibility artifacts, which cause misinterpretations of brain functions, are unavoidable distortions in EPI. This paper proposes an end-to-end deep learning framework, named TS-Net, for susceptibility artifact correction (SAC) in a pair of 3D EPI images with reversed phase-encoding directions. The proposed TS-Net comprises a deep convolutional network to predict a displacement field in three dimensions to overcome the limitation of existing methods, which only estimate the displacement field along the dominant-distortion direction. In the training phase, anatomical T1-weighted images are leveraged to regularize the correction, but they are not required during the inference phase to make TS-Net more flexible for general use. The experimental results show that TS-Net achieves favorable accuracy and speed trade-off when compared with the state-of-the-art SAC methods, i.e., TOPUP, TISAC, and S-Net. The fast inference speed (less than a second) of TS-Net makes real-time SAC during EPI image acquisition feasible and accelerates the medical image-processing pipelines.

Individualizing Representational Similarity Analysis

Representational similarity analysis (RSA) is a popular multivariate analysis technique in cognitive neuroscience that uses functional neuroimaging to investigate the informational content encoded in brain activity. As RSA is increasingly being used to investigate more clinically-geared questions, the focus of such translational studies turns toward the importance of individual differences and their optimization within the experimental design. In this perspective, we focus on two design aspects: applying individual vs. averaged behavioral dissimilarity matrices to multiple participants' neuroimaging data and ensuring the congruency between tasks when measuring behavioral and neural representational spaces. Incorporating these methods permits the detection of individual differences in representational spaces and yields a better-defined transfer of information from representational spaces onto multivoxel patterns. Such design adaptations are prerequisites for optimal translation of RSA to the field of precision psychiatry.

Neuroinflammation and Precision Medicine in Pediatric Neurocritical Care: Multi-Modal Monitoring of Immunometabolic Dysfunction

The understanding of molecular biology in neurocritical care (NCC) is expanding rapidly and recognizing the important contribution of neuroinflammation, specifically changes in immunometabolism, towards pathological disease processes encountered across all illnesses in the NCC. Additionally, the importance of individualized inflammatory responses has been emphasized, acknowledging that not all individuals have the same mechanisms contributing towards their presentation. By understanding cellular processes that drive disease, we can make better personalized therapy decisions to improve patient outcomes. While the understanding of these cellular processes is evolving, the ability to measure such cellular responses at bedside to make acute care decisions is lacking. In this overview, we review cellular mechanisms involved in pathological neuroinflammation with a focus on immunometabolic dysfunction and review non-invasive bedside tools that have the potential to measure indirect and direct markers of shifts in cellular metabolism related to neuroinflammation. These tools include near-infrared spectroscopy, transcranial doppler, elastography, electroencephalography, magnetic resonance imaging and spectroscopy, and cytokine analysis. Additionally, we review the importance of genetic testing in providing information about unique metabolic profiles to guide individualized interpretation of bedside data. Together in tandem, these modalities have the potential to provide real time information and guide more informed treatment decisions.

Perioperative Management of Patients with Glioblastoma

The clinical presentation of glioblastomas is varied, and definitive diagnosis requires pathologic examination and study of the tissue. Management of glioblastomas includes surgery and adjuvant chemotherapy and radiotherapy, with surgery playing an important role in the prognosis of these patients. Awake craniotomy plays a crucial role in tumors in or adjacent to eloquent areas, allowing surgeons to maximize resection, while minimizing iatrogenic deficits. However, the prognosis remains dismal. This article presents the perioperative management of patients with glioblastoma including tools and surgical adjuncts to maximize extent of resection and minimize poor outcomes.

Evolving Use of fMRI in Medicare Beneficiaries

Using the Medicare Physician-Supplier Procedure Summary Master File, we evaluated the evolving use of fMRI in Medicare fee-for-service beneficiaries from 2007 through 2017. Annual use rates (per 1,000,000 enrollees) increased from 17.7 to 32.8 through 2014 and have remained static since. Radiologists have remained the dominant specialty group from 2007 to 2017 (86.4% and 88.6% of all services, respectively), and the outpatient setting has remained the dominant place of service (65.4% and 65.4%, respectively).

Towards a Unified Framework for De-noising Neural Signals

Neural signals provide key information for decision-making processes in multiple disciplines including medicine, engineering, and neuroscience. The correct interpretation of these signals, however, requires substantial processing, especially when the signals exhibit low Signal to Noise Ratio (SNR). Electroencephalographic (EEG) signals are considered among this group and require effective handling of multiple types of artifactual components. Unfortunately, most available de-noising tools are suitable only for offline signal processing. For some artifacts (e.g., EEG motion artifacts), no established method of effective denoising exists for offline or real-time applications. Thus, there is a critical need for methods that can handle artifacts in neural signals with high performance, reliability and real-time capability. Here, we propose novel methods for handling some of the most challenging artifacts that exhibit highly complex dynamics, including motion artifacts. Having the same core sample-adaptive processing tool used for handling different types of artifacts, we present our efforts towards a unified framework for neural data artifact denoising with real-time compatibility.

Current Challenges in Translational and Clinical fMRI and Future Directions

Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as resting-state fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within- and between-subject variability, which affects the reliability of both task-related, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.

Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity

PURPOSE

Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping.

METHODS

Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI.

RESULTS

With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P < .05) than that of canonical and task-based approaches within putative language regions.

CONCLUSION

The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.

Assessing inter-individual differences with task-related functional neuroimaging

Explaining and predicting individual behavioural differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. In this Perspective, we discuss the theoretical and statistical foundations of the analyses of inter-individual differences in task-related functional neuroimaging. Leveraging a five-year literature review (July 2013-2018), we show that researchers often assess how activations elicited by a variable of interest differ between individuals. We argue that the rationale for such analyses, typically grounded in resource theory, offers an over-large analytical and interpretational flexibility that undermines their validity. We also recall how, in the established framework of the general linear model, inter-individual differences in behaviour can act as hidden moderators and spuriously induce differences in activations. We conclude with a set of recommendations and directions, which we hope will contribute to improving the statistical validity and the neurobiological interpretability of inter-individual difference analyses in task-related functional neuroimaging.

Functional Neuroimaging in the New Era of Big Data

The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.

Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts

Diffusion MRI and tractography hold great potential for surgery planning, especially to preserve eloquent white matter during resections. However, fiber tract reconstruction requires an expert with detailed understanding of neuroanatomy. Several automated approaches have been proposed, using different strategies to reconstruct the white matter tracts in a supervised fashion. However, validation is often limited to comparison with manual delineation by overlap-based measures, which is limited in characterizing morphological and topological differences. In this work, we set up a fully automated pipeline based on anatomical criteria that does not require manual intervention, taking advantage of atlas-based criteria and advanced acquisition protocols available on clinical-grade MRI scanners. Then, we extensively validated it on epilepsy patients with specific focus on language-related bundles. The validation procedure encompasses different approaches, including simple overlap with manual segmentations from two experts, feasibility ratings from external multiple clinical raters and relation with task-based functional MRI. Overall, our results demonstrate good quantitative agreement between automated and manual segmentation, in most cases better performances of the proposed method in qualitative terms, and meaningful relationships with task-based fMRI. In addition, we observed significant differences between experts in terms of both manual segmentation and external ratings. These results offer important insights on how different levels of validation complement each other, supporting the idea that overlap-based measures, although quantitative, do not offer a full perspective on the similarities and differences between automated and manual methods.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

Why is Clinical fMRI in a Resting State?

While resting state fMRI (rs-fMRI) has gained widespread application in neuroimaging clinical research, its penetration into clinical medicine has been more limited. We surveyed a neuroradiology professional group to ascertain their experience with rs-fMRI, identify perceived barriers to using rs-fMRI clinically and elicit suggestions about ways to facilitate its use in clinical practice. The electronic survey also collected information about demographics and work environment using Likert scales. We found that 90% of the respondents had adequate equipment to conduct rs-fMRI and 82% found rs-fMRI data easy to collect. Fifty-nine percent have used rs-fMRI in their past research and 72% reported plans to use rs-fMRI for research in the next year. Nevertheless, only 40% plan to use rs-fMRI in clinical practice in the next year and 82% agreed that their clinical fMRI use is largely confined to pre-surgical planning applications. To explore the reasons for the persistent low utilization of rs-fMRI in clinical applications, we identified barriers to clinical rs-fMRI use related to the availability of robust denoising procedures, single-subject analysis techniques, demonstration of functional connectivity map reliability, regulatory clearance, reimbursement, and neuroradiologist training opportunities. In conclusion, while rs-fMRI use in clinical neuroradiology practice is limited, enthusiasm appears to be quite high and there are several possible avenues in which further research and development may facilitate its penetration into clinical practice.

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

Estimation of brain functional connectivity from hypercapnia BOLD MRI data: Validation in a lifespan cohort of 170 subjects

Functional connectivity MRI, based on Blood-Oxygenation-Level-Dependent (BOLD) signals, is typically performed while the subject is at rest. On the other hand, BOLD is also widely used in physiological imaging such as cerebrovascular reactivity (CVR) mapping using hypercapnia (HC) as a modulator. We therefore hypothesize that hypercapnia BOLD data can be used to extract FC metrics after factoring out the effects of the physiological modulation, which will allow simultaneous assessment of neural and vascular function and may be particularly important in populations such as aging and cerebrovascular diseases. The present work aims to systematically examine the feasibility of hypercapnia BOLD-based FC mapping using three commonly applied analysis methods, specifically dual-regression Independent Component Analysis (ICA), region-based FC matrix analysis, and graph-theory based network analysis, in a large cohort of 170 healthy subjects ranging from 20 to 88 years old. To validate the hypercapnia BOLD results, we also compared these FC metrics with those obtained from conventional resting-state data. ICA analysis of the hypercapnia BOLD data revealed FC maps that strongly resembled those reported in the literature. FC matrix using region-based analysis showed a correlation of 0.97 on the group-level and 0.54 ± 0.10 on the individual-level, when comparing between hypercapnia and resting-state results. Although the correspondence on the individual-level was moderate, this was primarily attributed to variations intrinsic to FC mapping, because a corresponding resting-vs-resting comparison in a sub-cohort (N = 39) revealed a similar correlation of 0.57 ± 0.09. Graph-theory computations were also feasible in hypercapnia BOLD data and indices of global efficiency, clustering coefficient, modularity, and segregation were successfully derived. Hypercapnia FC results revealed age-dependent differences in which within-network connections generally exhibited an age-dependent decrease while between-network connections showed an age-dependent increase.

Test-retest reliability of task-based and resting-state blood oxygen level dependence and cerebral blood flow measures

Despite their wide-spread use, only limited information is available on the comparative test-retest reliability of task-based functional and resting state magnetic resonance imaging measures of blood oxygen level dependence (tb-fMRI and rs-fMRI) and cerebral blood flow (CBF) using arterial spin labeling. This information is critical to designing properly powered longitudinal studies. Here we comprehensively quantified and compared the test-retest reliability and reproducibility performance of 8 commonly applied fMRI tasks, 6 rs-fMRI metrics and CBF in 30 healthy volunteers. We find large variability in test-retest reliability performance across the different tb-fMRI paradigms and rs-fMRI metrics, ranging from poor to excellent. A larger extent of activation in tb-fMRI is linked to higher between-subject reliability of the respective task suggesting that differences in the amount of activation may be used as a first reliability estimate of novel tb-fMRI paradigms. For rs-fMRI, a good reliability of local activity estimates is paralleled by poor performance of global connectivity metrics. Evaluated CBF measures provide in general a good to excellent test-reliability matching or surpassing the best performing tb-fMRI and rs-fMRI metrics. This comprehensive effort allows for direct comparisons of test-retest reliability between the evaluated MRI domains and measures to aid the design of future tb-fMRI, rs-fMRI and CBF studies.

Mapping stress networks using functional magnetic resonance imaging in awake animals

The neurobiology of stress is studied through behavioral neuroscience, endocrinology, neuronal morphology and neurophysiology. There is a shift in focus toward progressive changes throughout stress paradigms and individual susceptibility to stress that requires methods that allow for longitudinal study design and study of individual differences in stress response. Functional magnetic resonance imaging (fMRI), with the advantages of noninvasiveness and a large field of view, can be used for functionally mapping brain-wide regions and circuits critical to the stress response, making it suitable for longitudinal studies and understanding individual variability of short-term and long-term consequences of stress exposure. In addition, fMRI can be applied to both animals and humans, which is highly valuable in translating findings across species and examining whether the physiology and neural circuits involved in the stress response are conserved in mammals. However, compared to human fMRI studies, there are a number of factors that are essential for the success of fMRI studies in animals. This review discussed the use of fMRI in animal studies of stress. It reviewed advantages, challenges and technical considerations of the animal fMRI methodology as well as recent literature of stress studies using fMRI in animals. It also highlighted the development of combining fMRI with other methods and the future potential of fMRI in animal studies of stress. We conclude that animal fMRI studies, with their flexibility, low cost and short time frame compared to human studies, are crucial to advancing our understanding of the neurobiology of stress.

Relation between functional connectivity and disability in multiple sclerosis: a non-linear model

OBJECTIVE

To characterize the relation between brain functional connectivity and disability in patients with multiple sclerosis; to investigate the existence of critical values of both disability and functional connectivity corresponding to exhaustion of functional adaptive mechanisms.

METHODS

Hundred-and-nineteen patients with no-to-severe disability and 42 healthy subjects were studied via 3T resting state functional MRI. Out of 116 regions extracted from Automated Anatomical Labeling atlas, pairs of regions whose functional connectivity correlated with Expanded Disability Status Score were identified. In patients, mathematical modeling was applied to find the best models describing Expanded-Disability-Status-Score vs structural or functional measures. Functional vs structural models intersecting points were identified.

RESULTS

Disability had direct linear relation with lesion load (r = 0.40, p < 5E-6), inverse of thalamic volume (r = 0.31 p < 1E-3) and functional connectivity in bi-frontal pairs of regions (r > 0.40, p < 0.04), while being non-linearly associated with functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions (F > 1.73, p < 0.02). Structural vs functional models intersecting points corresponded to Expanded Disability Status Score of 3.0. 85% of patients scoring more than 3.0 showed functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions below confidence intervals (z = [2.28-2.88] 95% CI) measured in healthy subjects.

CONCLUSIONS

Functional brain connectivity changes may represent mechanisms of adaptation to structural damage and inflammation and may be not always clinically beneficial. Functional connectivity decreases in comparison with structural measure at Expanded Disability Status Score greater than 3.0, which may be critical and indicate exhaustion of compensatory mechanisms.

Preoperative Mapping of the Supplementary Motor Area in Patients with Brain Tumor Using Resting-State fMRI with Seed-Based Analysis

BACKGROUND AND PURPOSE

The supplementary motor area can be a critical region in the preoperative planning of patients undergoing brain tumor resection because it plays a role in both language and motor function. While primary motor regions have been successfully identified using resting-state fMRI, there is variability in the literature regarding the identification of the supplementary motor area for preoperative planning. The purpose of our study was to compare resting-state fMRI to task-based fMRI for localization of the supplementary motor area in a large cohort of patients with brain tumors presenting for preoperative brain mapping.

MATERIALS AND METHODS

Sixty-six patients with brain tumors were evaluated with resting-state fMRI using seed-based analysis of hand and orofacial motor regions. Rates of supplementary motor area localization were compared with those in healthy controls and with localization results by task-based fMRI.

RESULTS

Localization of the supplementary motor area using hand motor seed regions was more effective than seeding using orofacial motor regions for both patients with brain tumor (95.5% versus 34.8%, P < .001) and controls (95.2% versus 45.2%, P < .001). Bilateral hand motor seeding was superior to unilateral hand motor seeding in patients with brain tumor for either side (95.5% versus 75.8%/75.8% for right/left, P < .001). No difference was found in the ability to identify the supplementary motor area between patients with brain tumors and controls.

CONCLUSIONS

In addition to task-based fMRI, seed-based analysis of resting-state fMRI represents an equally effective method for supplementary motor area localization in patients with brain tumors, with the best results obtained with bilateral hand motor region seeding.