fMRI Methods Research Articles

Organizing Variables Affecting fMRI Estimates of Language Dominance in Patients with Brain Tumors.

Numerous variables can affect the assessment of language dominance using presurgical functional magnetic resonance (fMRI) in patients with brain tumors. This work organizes the variables into confounding and modulating factors. Confounding factors give the appearance of changed language dominance. Most confounding factors are fMRI-specific and they can substantially disrupt the evaluation of language dominance. Confounding factors can be divided into two categories: tumor-related and fMRI analysis. The tumor-related confounds further subdivide into tumor characteristics (e.g., tumor grade) and tumor-induced conditions (aphasia). The fMRI analysis confounds represent technical aspects of fMRI methods (e.g., a fixed versus an individual threshold). Modulating factors can modify language dominance without confounding it. They are not fMRI-specific, and they can impact language dominance both in healthy individuals and neurosurgical patients. The effect of most modulating factors on fMRI language dominance is smaller than that of confounding factors. Modulating factors include demographics (e.g., age) and linguistic variables (e.g., early bilingualism). Three cases of brain tumors in the left hemisphere are presented to illustrate how modulating confounding and modulating factors can impact fMRI estimates of language dominance. Distinguishing between confounding and modulating factors can help interpret the results of presurgical language mapping with fMRI.

Evidence of hippocampal learning in human infants

The hippocampus is essential for human memory.1 The protracted maturation of memory capacities from infancy through early childhood2-4 is thus often attributed to hippocampal immaturity.5-7 The hippocampus of human infants has been characterized in terms of anatomy,8,9 but its function has never been tested directly because of technical challenges.10,11 Here, we use recently developed methods for task-based fMRI in awake human infants12 to test the hypothesis that the infant hippocampus supports statistical learning.13-15 Hippocampal activity increased with exposure to visual sequences of objects when the temporal order contained regularities to be learned, compared to when the order was random. Despite the hippocampus doubling in anatomical volume across infancy, learning-related functional activity bore no relationship to age. This suggests that the hippocampus is recruited for statistical learning at the youngest ages in our sample, around 3months. Within the hippocampus, statistical learning was clearer in anterior than posterior divisions. This is consistent with the theory that statistical learning occurs in the monosynaptic pathway,16 which is more strongly represented in the anterior hippocampus.17,18 The monosynaptic pathway develops earlier than the trisynaptic pathway, which is linked to episodic memory,19,20 raising the possibility that the infant hippocampus participates in statistical learning before it forms durable memories. Beyond the hippocampus, the medial prefrontal cortex showed statistical learning, consistent with its role in adult memory integration21 and generalization.22 These results suggest that the hippocampus supports the vital ability of infants to extract the structure of their environment through experience.

Spontaneous functional changes in specific cerebral regions in patients with hypertensive retinopathy: a resting-state functional magnetic resonance imaging study.

This study investigated functional alterations in the cerebral network of patients with hypertensive retinopathy (HR) by resting-state functional magnetic resonance imaging (rs-fMRI) and degree centrality (DC) methods. 31 patients with HR along with 31 healthy controls (HC) closely matched in gender and age were enrolled for the research. All participants were examined by rs-fMRI, and the DC method was applied to evaluate alterations in spontaneous cerebral activity between the 2 groups. We used the independent samples t test to evaluate demographic and general information differences between HR patients and HCs. The 2-sample t test was used to compare the DC values of different cerebral regions between the 2 groups. The accuracy of differential diagnostic HR was analyzed by receiver operating characteristic (ROC) curve method for rs-fMRI DC values changes. Pearson’s correlation coefficient was applied to determine the correlation between differences in DC in specific cerebral areas and clinical manifestation. Results showed that DC values were higher in the left cerebellum posterior lobe (LCPL), left medial occipital gyrus (LMOG), and bilateral precuneus (BP) of HR patients compared to HCs. Mean DC values were lower in the right medial frontal gyrus/bilateral anterior cingulate cortex of HR patients. Anxiety and depression scores were positively correlated with DC values of LMOG and LCPL, respectively. Bilateral best-corrected visual acuity in HR patients was negatively correlated with the DC value of BP. Hence, changes in DC in specific cerebral areas of patients with HR reflect functional alterations that provide insight into the pathophysiologic mechanisms of HR.

New analysis method for functional brain imaging: White noise removed T2* variation mapping using multi-echo EPI

BackgroundGenerally, the analysis of functional magnetic resonance imaging (fMRI) using echo-planar imaging (EPI) data is based on independent component analysis (ICA) and the general linear model (GLM). The application of these two approaches is highly independent, like GLM is for task-related activation mapping, and ICA is for resting-state imaging. Herein, we propose white noise-removed T2*-variation mapping as a new analysis method for fMRI that integrates the two conventional mapping approaches. New MethodWe derived the standard deviation to the mean-square ratio of the true T2* signal from the multi-echo EPI (ME-EPI) dataset. For the true T2*-variation-based value, we removed the S0 (initial signal intensity) and white noise component from the variation in the EPI signal using signal-coherence analysis of the echo time (TE) dataset and slope analysis of the TE-variated coefficient of variation of the ME-EPI dataset. ResultsThe activation mapping for a visual task and resting-state imaging by the proposed method showed the reliable activation map in the visual cortex area and area for the typical default mode network, with white noise and the S0 component removed. Comparison with existing methodsConventional analyses for fMRI cannot be applied to both activation mapping and resting-state imaging, with white noise removed, while the proposed method can be applied. ConclusionsWe demonstrated white noise-removed true T2*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.

Neurotransmission in Visual Analyser and Bionic Eye. A Review

The aim of the work is to point out the transmission of electrical voltage changes in the visual analyser and thus the efficiency of the bionic eye. The review deals with the question of the transmission of electrical changes in visual path voltage under physiological and pathological conditions. In particular, it points to feedback autoregulatory damage not only of primarily altered cellular structures, but of all other, both horizontally and vertically localized. Based on the results of functional magnetic resonance imaging and electrophysiological methods, it shows the pathology of the entire visual pathway in three eye diseases: retinitis pigmentosa, age-related macular degeneration and glaucoma. The thesis also provides an overview of possible systems that are used to replace lost vision, from epiretinal, subretinal, suprachoroidal implants, through stimulation of the optic nerve, corpus geniculatum laterale to the visual cortex. Due to the pathology of neurotransmission, bionic eye systems cannot be expected to be restored after stabilization of binocular functions.

Localization of Epileptic Foci Based on Simultaneous EEG–fMRI Data

Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.

Altered Functional Connectivity Within and Between Salience and Sensorimotor Networks in Patients With Functional Constipation.

Functional constipation (FCon) is a common functional gastrointestinal disorder. A considerable portion of patients with FCon is associated with anxiety/depressive status (FCAD). Previous neuroimaging studies mainly focused on patients with FCon without distinguishing FCAD from FCon patients without anxiety/depressive status (FCNAD). Differences in brain functions between these two subtypes remain unclear. Thus, we employed resting-state functional magnetic resonance imaging (RS-fMRI) and graph theory method to investigate differences in brain network connectivity and topology in 41 FCAD, 42 FCNAD, and 43 age- and gender-matched healthy controls (HCs). FCAD/FCNAD showed significantly lower normalized clustering coefficient and small-world-ness. Both groups showed altered nodal degree/efficiency mainly in the rostral anterior cingulate cortex (rACC), precentral gyrus (PreCen), supplementary motor area (SMA), and thalamus. In the FCAD group, nodal degree in the SMA was negatively correlated with difficulty of defecation, and abdominal pain was positively correlated with nodal degree/efficiency in the rACC, which had a lower within-module nodal degree. The salience network (SN) exhibited higher functional connectivity (FC) with the sensorimotor network (SMN) in FCAD/FCNAD, and FC between these two networks was negatively correlated with anxiety ratings in FCAD group. Additionally, FC of anterior insula (aINS)–rACC was only correlated with constipation symptom (i.e., abdominal pain) in the FCNAD group. In the FCAD group, FCs of dorsomedial prefrontal cortex–rACC, PreCen–aINS showed correlations with both constipation symptom (i.e., difficulty of defecation) and depressive status. These findings indicate the differences in FC of the SN–SMN between FCAD and FCNAD and provide neuroimaging evidence based on brain function, which portrays important clues for improving new treatment strategies.

Operation-Specific Lexical Consistency Effect in Fronto-Insular-Parietal Network During Word Problem Solving.

The practice of mathematical word problem is ubiquitous and thought to impact academic achievement. However, the underlying neural mechanisms are still poorly understood. In this study, we investigate how lexical consistency of word problem description is modulated in adults' brain responses during word problem solution. Using functional magnetic resonance imaging methods, we examined compare word problems that included relational statements, such as “A dumpling costs 9 dollars. A wonton is 2 dollars less than a dumpling. How much does a wonton cost?” and manipulated lexical consistency (consistent: the relational term consistent with the operation to be performed, e.g., more—addition/inconsistent: e.g., less—addition) and problem operation (addition/subtraction). We found a consistency by operation interaction in the widespread fronto-insular-parietal activations, including the anterior insula, dorsoanterior cingulate cortex, middle frontal gyrus, and intraparietal sulcus, such that inconsistent problems engaged stronger activations than consistent problems for addition, whereas the consistency effect was inverse for subtraction. Critically, these results were more salient in the less successful problem solvers than their more successful peers. Our study is the first to demonstrate that lexical consistency effects on arithmetic neural networks are modulated during reading word problem that required distinct arithmetic operations. More broadly, our study has strong potentials to add linkage between neuroscience and education by remediating deficits and enhance instruction design in the school curriculum.

Rt-me-fMRI: a task and resting state dataset for real-time, multi-echo fMRI methods development and validation

A multi-echo fMRI dataset (N=28 healthy participants) with four task-based and two resting state runs was collected, curated and made available to the community. Its main purpose is to advance the development of methods for real-time multi-echo functional magnetic resonance imaging (rt-me-fMRI) analysis with applications in neurofeedback, real-time quality control, and adaptive paradigms, although the variety of experimental task paradigms supports a multitude of use cases. Tasks include finger tapping, emotional face and shape matching, imagined finger tapping and imagined emotion processing. This work provides a detailed description of the full dataset; methods to collect, prepare, standardize and preprocess it; quality control measures; and data validation measures. A web-based application is provided as a supplementary tool with which to interactively explore, visualize and understand the data and its derivative measures:https://rt-me-fmri.herokuapp.com/. The dataset itself can be accessed via a data use agreement on DataverseNL athttps://dataverse.nl/dataverse/rt-me-fmri. Supporting information and code for reproducibility can be accessed athttps://github.com/jsheunis/rt-me-fMRI.

A method to adjust a prior distribution in Bayesian second-level fMRI analysis

Previous research has shown the potential value of Bayesian methods in fMRI (functional magnetic resonance imaging) analysis. For instance, the results from Bayes factor-applied second-level fMRI analysis showed a higher hit rate compared with frequentist second-level fMRI analysis, suggesting greater sensitivity. Although the method reported more positives as a result of the higher sensitivity, it was able to maintain a reasonable level of selectivity in term of the false positive rate. Moreover, employment of the multiple comparison correction method to update the default prior distribution significantly improved the performance of Bayesian second-level fMRI analysis. However, previous studies have utilized the default prior distribution and did not consider the nature of each individual study. Thus, in the present study, a method to adjust the Cauchy prior distribution based on a priori information, which can be acquired from the results of relevant previous studies, was proposed and tested. A Cauchy prior distribution was adjusted based on the contrast, noise strength, and proportion of true positives that were estimated from a meta-analysis of relevant previous studies. In the present study, both the simulated images and real contrast images from two previous studies were used to evaluate the performance of the proposed method. The results showed that the employment of the prior adjustment method resulted in improved performance of Bayesian second-level fMRI analysis.

Medullary Blood Oxygen Level-Dependent MRI Index (R2*) is Associated with Annual Loss of Kidney Function in Moderate CKD

Background: The estimated glomerular filtration rate (eGFR) is frequently used to monitor progression of kidney disease. Multiple values have to be obtained, sometimes over years to determine the rate of decline in kidney function. Recent data suggest that functional MRI (fMRI) methods may be able to predict loss of eGFR. In a prior study, baseline data with multi-parametric MRI in individuals with diabetes and moderate CKD was reported. This report extends our prior observations in order to evaluate the temporal variability of the fMRI measurements over 36 months and their association with annual change in eGFR. Methods: Twenty-four subjects with moderate CKD completed 3 sets of MRI scans over a 36-month period. Blood oxygenation level-dependent (BOLD), arterial spin labeling perfusion, and diffusion MRI images were acquired using a 3 T scanner. Coefficients of variation was used to evaluate variability between subjects at each time point and temporal variability within each subject. We have conducted mixed effects models to examine the trajectory change in GFR over time using time and MRI variables as fixed effects and baseline intercept as random effect. Associations of MRI image markers with annual change in eGFR were evaluated. Results: Multi-parametric functional renal MRI techniques in individuals with moderate CKD showed higher temporal variability in R2* of medulla compared to healthy individuals. This was consistent with the significant lower R2* in medulla observed at 36 months compared to baseline values. The results of linear mixed model showing that R2*_Medulla was the only predictor associated with change in eGFR over time. Furthermore, a significant association of medullary R2* with annual loss of eGFR was observed at all the 3 time points. Conclusions: The lower R2* values and the higher temporal variability in the renal medulla over time suggest the ability to monitor progressive CKD. These were confirmed by the fact that reduced medullary R2* was associated with higher annual loss in eGFR. These data collectively emphasize the need for inclusion of medulla in the analysis of renal BOLD MRI studies.

Роль зеркальных нейронов в интерпретации действий и намерений

Роль зеркальных нейронов в интерпретации действий и намерений

Review of fMRI methods in developmental stuttering and it’s treatment

The article gives a summary overview of the most important studies of stuttering by using functional magnetic resonance imaging (fMRI) in the last 20 years. This review also highlights problems in the literature in terms of methodology and research areas. It presented an integrated approach and technique fMRI studies aimed at both the primary diagnosis of stuttering and stammering on the dynamics of the mechanisms in the treatment process.

Evaluating phase synchronization methods in fMRI: A comparison study and new approaches

In recent years there has been growing interest in measuring time-varying functional connectivity between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.

Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain.

The extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model, as well as object representations, are more widely distributed across the brain than previously acknowledged and that functional searchlight can improve model-based similarity and decoding accuracy. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.

Does Chronic Cannabis Use Impact Risky Decision-Making: An Examination of fMRI Activation and Effective Connectivity?

With the increase in use of cannabis and its shifting legal status in the United States, cannabis use has become an important research focus. While studies of other drug populations have shown marked increases in risky decision-making, the literature on cannabis users is not as clear. The current study examined the performance of 17 cannabis users and 14 non-users on the Balloon Analog Risk Task (BART) using behavioral, fMRI and effective connectivity methods. Significant attenuation was found in a functional pathway projecting from the dorsal anterior cingulate cortex (dACC) to the nucleus accumbens (NAc) in cannabis users compared to non-using controls as well as decreases in risk-taking behaviors. These findings suggest that cannabis users may process and evaluate risks and rewards differently than non-users.

Structured versus free block play: the impact on arithmetic processing.

Block play is one type of intervention that improves visuospatial skills. There are multiple forms of block play and it is unclear whether they have differential cognitive effects. Given the importance of visuospatial skills for mathematical performance, we studied the differential impact of two types of block playstructured (copying a block design) and free (building from imagination) on arithmetic processing, using behavioral and fMRI methods. Forty-three children aged 8.3±0.8 years participated (21 free play and 22 structured block play). Results showed that while both groups showed behavioral improvements, only the structured block play group showed significant improvements in both addition and subtraction performance. Additionally, the structured block play group showed increased activation in several regions linked to memory, motor, and arithmetic processing after training. The results inform choices for activities used in the classroom to improve visuospatial skills and suggest structured block play may be beneficial for arithmetic processing.

Detecting Task Functional MRI Activation Using the Multiband Multiecho (MBME) Echo-Planar Imaging (EPI) Sequence.

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) has been widely applied to detect brain activations. Recent advances in multiband (MB) and multiecho (ME) techniques have greatly improved fMRI methods. MB imaging improves temporal and/or spatial resolution, while ME imaging has been shown to improve BOLD sensitivity. This study aimed to evaluate the novel MBME echo planar imaging (EPI) sequence utilizing MB and ME simultaneously to determine if the MBME outperform the MB single echo (MBSE) sequence for task fMRI. To compare the performance of MBME with MBSE in a task fMRI study. Prospective. A total of 29 healthy volunteers aged 20-46 years (9 male, 20 female). MBSE and MBME gradient-echo EPI sequences were applied at 3T. Additional T1 -weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) was collected. A checkerboard visual task was presented during the functional MBSE and MBME scans. The MBME or MBSE signal was evaluated using the temporal signal-to-noise ratio (tSNR). Task activation was evaluated using the z-score, volume, sensitivity, and specificity. Test-retest metrics of task activation were examined with the Dice coefficient (DC) and intraclass correlation coefficient (ICC) on subjects with repeated scans. A linear mixed-effects model was used to compared MBME and MBSE activation at the voxel base. The paired t-test was used to compare tSNR, activation z-score, and volume, along with sensitivity, specificity, and DC between MBSE and MBME. While similar task activation was detected in the visual cortex, MBME showed higher activation volume and higher sensitivity compared with MBSE (P < 0.05). ICC was higher for MBME than MBSE, while there was a trend of differences in DC (P = 0.08). MBME resulted in higher task fMRI activation volume and sensitivity without losing specificity. Reliability was also higher for MBME scans compared with MBSE. 1 TECHNICAL EFFICACY STAGE: 1.

More restriction, more overeating: conflict monitoring ability is impaired by food-thought suppression among restrained eaters.

Numerous studies have shown that restrained eating is not an effective weight loss strategy. Restrained eaters often suppress their desires and thoughts about tasty food, which makes it more difficult to control themselves in subsequent eating behavior. The ego depletion impairs conflict monitoring abilities. Therefore, this study explored the effects of food thoughts suppression on restrained eaters' conflict monitoring. Therefore, this study used functional magnetic resonance imaging (fMRI) methods to explore changes in the activity of brain regions involved in conflict monitoring when restrained eaters choose between high- and low-calorie foods after either suppressing or not suppressing thoughts about food. The results showed that, compared to the control condition, after suppression of such thoughts, restrained eaters chose more high-calorie foods and displayed decreased activity in the dorsal anterior cingulate cortex-an important region in charge of conflict monitoring. At the same time, the functional coupling of the dorsal anterior cingulate cortex and the precuneus increased. Our findings suggest that restrained eaters' suppression of thoughts about tasty food could lead to a decline in their ability to monitor conflicts between current behaviors and goals, which in turn leads to unhealthy eating behavior.

Brain Functional Biomarkers Distinguishing Premature Ejaculation From Anejaculation by ALFF: A Resting-State fMRI Study

IntroductionPremature ejaculation (PE) and anejaculation (AJ) are 2 opposite disorders of male ejaculatory dysfunction. Recent studies have demonstrated that the process of ejaculation is mediated by certain neural circuits in the brain. However, different mechanisms between PE and AJ are still unclear. AimTherefore, we used resting-state functional magnetic resonance imaging (fMRI) to explore the underlying neural mechanisms in patients with PE and AJ by measuring the amplitude of low-frequency fluctuations (ALFF). MethodsResting-state fMRI data were acquired in 17 PE, 20 AJ patients and 23 matched healthy controls (HC). Main Outcome MeasureDifferences of ALFF values among the 3 groups were compared. We also explored the correlations between brain regions showing altered ALFF values and scores of Premature Ejaculation Diagnostic Tool (PEDT) in the PE group. ResultsThere were widespread differences of ALFF values among the 3 groups, which included left anterior cingulate gyrus, precentral and postcentral gyrus, paracentral lobule, superior temporal gyrus, calcarine fissure, putamen; right postcentral gyrus, paracentral lobule, middle temporal gyrus, putamen. Compared with HC, PE patients had greater ALFF in the right inferior frontal gyrus (opercular part), AJ patients had greater ALFF in the left postcentral gyrus. In addition, PE patients exhibited greater ALFF in the left Rolandic operculum, anterior cingulate gyrus, inferior frontal gyrus (orbital part), putamen, and right putamen when compared with AJ patients, as well as decreased ALFF in the right postcentral gyrus. Moreover, positive correlations were found between ALFF of left postcentral gyrus, inferior frontal gyrus (orbital part), right inferior frontal gyrus (opercular part), and PEDT scores. Clinical ImplicationsThe differences in central pathophysiological mechanisms between PE and AJ might be useful for improving the clinical diagnosis of ejaculation dysfunction. Strength & LimitationsOur results showed that the method of fMRI could identify the differences of ALFF between PE and AJ and that these alterations in ALFF were related to clinical function. However, this was a relatively small sample study, and further multimodal neuroimaging studies with large samples were needed. ConclusionThe findings demonstrated that altered ALFF of frontal, parietal cortex, and putamen might help distinguish premature ejaculation from anejaculation. Abnormal function of these brain regions might play a critical role in the physiopathology of ejaculatory dysfunction of patients.Chen J, Yang J, Huang X, et al. Brain Functional Biomarkers Distinguishing Premature Ejaculation From Anejaculation by ALFF: A Resting-State fMRI Study. J Sex Med 2020;17:2331–2340.