fMRI Research Research Articles

Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis

ObjectivesThis study aims to reveal the current knowledge map, research hotspots of functional magnetic resonance imaging (fMRI) studies on depression, as well as identify the brain regions associated with depression.MethodsCiteSpace was conducted to analyze the publication outputs, country, institution, cited journals, author and cited author, references, keyword cocurrence and burst keywords of fMRI studies in depression from 2010 to 2024. And a meta-analysis of fMRI was used to identify brain regions associated with depression using Neurosynth.ResultsA total of 4,049 publications were included, and Gong Qiyong was the most prolific authors. Neuroimage, Biological Psychiatry, and Human Brain Mapping were prominent journals. Default mode network (DMN), prefrontal cortex, amygdala, and anterior cingulate cortex were the popular keywords. The fMRI studies on depression have mainly focused on major depression, especially the DMN. Functional connectivity and regional homogeneity of brain regions were research hotspots. The meta-analysis revealed significant differences in brain regions between patients with depression and healthy controls, including the Amygdala_L, Insula_R, Frontal_Inf_Oper_R, Cingulum_Post_L, Putamen_L, Thalamus_R, Angular_L, Precuneus_R, Frontal_Sup_R, Occipital_Inf_L.ConclusionsThis study sheds light on key issues and future directions in fMRI research on depression, elucidating the brain regions related to depression.Graphical

Heterogenous brain activations across individuals localize to a common network.

Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.

Reproducible brain PET data analysis: easier said than done.

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.

This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.

Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at “very strong evidence” (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area – BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

Modulation of the attentional response to baby schema by testosterone in pedohebephilic men and its relation to the nurturing system

Previous fMRI research found increased brain responses in men with pedophilic interest to non-sexual pictures of child and animal faces. This raised the question of whether an aberrant nurturing system could be linked to pedophilia. To further explore this hypothesis, 20 pedohebephilic and 23 teleiophilic men performed a target detection task with adult versus infant human and animal faces, which measured selective attention towards the baby schema by comparing reaction times to infant versus adult targets that were presented amongst distractors of the other category. Since the response to baby schema can be influenced by steroid hormones, saliva samples were additionally collected to determine endogenous testosterone, progesterone, estradiol and cortisol. Contrary to expectations, all men did not react faster to infant than adult faces. Yet, pedohebephilic men were more distracted by infant’s faces than teleiophilic men. Pedohebephilic men with higher testosterone were faster in orienting attention to infant targets in the context of adult distractors. This association was not observed in teleiophilic men. Our results support the idea of an overactive nurturing system in pedophilia, which may be influenced by the endogenous testosterone level.

Twenty-five years of research on resting-state fMRI of major depressive disorder: A bibliometric analysis of hotspots, nodes, bursts, and trends

Major depressive disorder (MDD) is a debilitating mental health condition that poses significant risks and burdens. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a promising tool in investigating the neural mechanisms underlying MDD. However, a comprehensive bibliometric analysis of resting-state fMRI in MDD is currently lacking. Here, we aimed to thoroughly explore the trends and frontiers of resting-state fMRI in MDD research. The relevant publications were retrieved from the Web of Science database for the period between 1998 and 2022, and the CiteSpace software was employed to identify the influence of authors, institutions, countries/regions, and the latest research trends. A total of 1501 publications met the search criteria, revealing a gradual increase in the number of annual publications over the years. China contributed the largest publication output, accounting for the highest percentage among all countries. Particularly, the University of Electronic Science and Technology of China, Capital Medical University, and Harvard Medical School were identified as key institutions that have made substantial contributions to this growth. Neuroimage, Biological Psychiatry, Journal of Affective Disorders, and Proceedings of the National Academy of Sciences of the United States of America are among the influential journals in the field of resting-state fMRI research in MDD. Burst keywords analysis suggest the emerging research frontiers in this field are characterized by prominent keywords such as dynamic functional connectivity, cognitive control network, transcranial brain stimulation, and childhood trauma. Overall, our study provides a systematic overview into the historical development, current status, and future trends of resting-state fMRI in MDD, thus offering a useful guide for researchers to plan their future research.

Analogical Visuospatial Matrix Reasoning, Verbal Relational Reasoning, Verbal IQ and Memory Relationships With Maths and Science: A Behavioural and fMRI Study

There is no research that combines behavioural and functional magnetic resonance imaging (fMRI) research approaches to investigate the relationships between analogical reasoning and the mediating and moderating effects of working memory, verbal IQ, visuospatial and verbal reasoning on students’ performance of maths and science. This paper investigates all these multiple relationships between working memory (WM), verbal IQ, visuospatial and verbal reasoning with performance in maths and science accuracy. The multimethod research consists of a behavioural survey and a fMRI neuroscience investigating the interactive and multiple relationships of all cognitive independent variables (IVs) with the dependent variables (DVs) of maths and science performance. Using structural Equation Modelling (SEM) and Warp Partial Least Squares (Warp PLS) analytical approach, the research discusses the findings of both, the behavioural and fMRI data. The adolescent sample (N=34) for the behavioural and fMRI study, was recruited from consenting schools in London, UK, and consisted of equal numbers of girls and boys between 11-15 years old. The hypothesised behavioural SEM model predicted positive relationships between all the independent variables (IV) of visuospatial matrix reasoning (VSMatAR), verbal analogical reasoning (VerbAR), verbal working memory (VWM), visuospatial working memory (VSWM) and verbal IQ, with performance in maths and science accuracy. The findings support all hypotheses except two and identifies verbal IQ, Verbal WM and VerbAR as the three strongest predictors of maths and science accuracy. The hypothesised fMRI SEM, produced mixed results, which supported some of the hypothesised relationships between the IVs of verbal AR and VSMatrix AR associations and four brain regions, namely, the bilateral dorso lateral prefrontal cortex (DLPFC), Parietal, Temporal and pre-supplementary motor area (PSMA). The paper makes two original contributions, firstly, by testing the hypothesis that Verbal WM mediates the independent variables’ effects on maths and science outcomes. And secondly, by using Warp PLS to test the SEM (behavioural and fMRI) models’ multiple non-linear interactions simultaneously between the IVs with the DVs of maths and science accuracy. The research produced some interesting new insights relevant for educational practitioners regarding the strong relationships between Verbal analogical reasoning and maths and science performance for adolescent students.

Theta and alpha oscillations in human hippocampus and medial parietal cortex support the formation of location-based representations.

Our ability to navigate in a new environment depends on learning new locations. Mental representations of locations are quickly accessible during navigation and allow us to know where we are regardless of our current viewpoint. Recent functional magnetic resonance imaging (fMRI) research using pattern classification has shown that these location-based representations emerge in the retrosplenial cortex and parahippocampal gyrus, regions theorized to be critically involved in spatial navigation. However, little is currently known about the oscillatory dynamics that support the formation of location-based representations. We used magnetoencephalogram (MEG) recordings to investigate region-specific oscillatory activity in a task where participants could form location-based representations. Participants viewed videos showing that two perceptually distinct scenes (180° apart) belonged to the same location. This "overlap" video allowed participants to bind the two distinct scenes together into a more coherent location-based representation. Participants also viewed control "non-overlap" videos where two distinct scenes from two different locations were shown, where no location-based representation could be formed. In a post-video behavioral task, participants successfully matched the two viewpoints shown in the overlap videos, but not the non-overlap videos, indicating they successfully learned the locations in the overlap condition. Comparing oscillatory activity between the overlap and non-overlap videos, we found greater theta and alpha/beta power during the overlap relative to non-overlap videos, specifically at time-points when we expected scene integration to occur. These oscillations localized to regions in the medial parietal cortex (precuneus and retrosplenial cortex) and the medial temporal lobe, including the hippocampus. Therefore, we find that theta and alpha/beta oscillations in the hippocampus and medial parietal cortex are likely involved in the formation of location-based representations.

Mapping the landscape: a bibliometric analysis of resting-state fMRI research on schizophrenia over the past 25 years

Schizophrenia, a multifaceted mental disorder characterized by disturbances in thought, perception, and emotion, has been extensively investigated through resting-state fMRI, uncovering changes in spontaneous brain activity among those affected. However, a bibliometric examination regarding publication trends in resting-state fMRI studies related to schizophrenia is lacking. This study obtained relevant publications from the Web of Science Core Collection spanning the period from 1998 to 2022. Data extracted from these publications included information on countries/regions, institutions, authors, journals, and keywords. The collected data underwent analysis and visualization using VOSviewer software. The primary analyses included examination of international and institutional collaborations, authorship patterns, co-citation analyses of authors and journals, as well as exploration of keyword co-occurrence and temporal trend networks. A total of 859 publications were retrieved, indicating an overall growth trend from 1998 to 2022. China and the United States emerged as the leading contributors in both publication outputs and citations, with Central South University and the University of New Mexico being identified as the most productive institutions. Vince D. Calhoun had the highest number of publications and citation counts, while Karl J. Friston was recognized as the most influential author based on co-citations. Key journals such as Neuroimage, Schizophrenia Research, Schizophrenia Bulletin, and Biological Psychiatry played pivotal roles in advancing this field. Recent popular keywords included support vector machine, antipsychotic medication, transcranial magnetic stimulation, and related terms. This study systematically synthesizes the historical development, current status, and future trends in resting-state fMRI research in schizophrenia, offering valuable insights for future research directions.

Optimizing the measurement of sample entropy in resting-state fMRI data.

The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field. We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate. When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility. We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.

Symptom effects and central mechanism of acupuncture in patients with functional gastrointestinal disorders: a systematic review based on fMRI studies

BackgroundFunctional gastrointestinal disorders (FGIDs) are closely related to disorders of brain-gut interaction. FGIDs are the dominant disease of acupuncture treatment, which can improve the symptoms and emotional state.AimTo evaluate the results and quality of the available clinical evidence and to summarize the central mechanism and effect of acupuncture on FGIDs.MethodsPubMed, EMBASE, Web of science, Cochrane Library, China National Knowledge Infrastructure (CNKI) were searched by computer to collect the randomized controlled trials (RCTs), which contained central mechanisms via fMRI research of acupuncture in the treatment of FGIDs patients. The search time limit was from the establishment of the database to June 22, 2022. Two researchers independently screened the literature, extracted data, and evaluated the quality.ResultsTen RCTs involving fMRI data were included in this study, including 4 Functional dyspepsia (FD) studies, 3 irritable bowel syndrome (IBS) studies, and 3 functional constipation (FC) studies. The score of improvements in both gastrointestinal symptoms and psychological symptoms showed that acupuncture could significantly improve the clinical symptoms of FGIDs patients, including abdominal pain, abdominal distension, frequency of defecation, and stool characteristics, and could relieve anxiety and depression symptoms of patients. Acupuncture could regulate brain functional connections and functional activity in FGIDs patients, mainly including insula, anterior cingulate cortex, prefrontal cortex, thalamus, hippocampus, amygdala and other brain regions.ConclusionAcupuncture can improve gastrointestinal symptoms and psychological status in FGIDs patients, and regulate functional connectivity and activity of brain regions such as insula, ACC, PFC, thalamus, HIPP, amygdala, etc. These changes in brain activity may related to visceral sensation, pain regulation, emotion, but further studies of high quality are still necessary.

Introduction to the Meta-analysis method of functional magnetic resonance imaging research and the exploration of its application in the field of acupuncture.

In recent years, the number of functional magnetic resonance imaging (fMRI) research in acupuncture grows increasingly. However, due to the differences in acupoint selection, acupuncture technique and sample size, the problems get more prominent in terms of the diverse results and the lack of common rules of acupuncture among researches. By taking the fMRI research for post-stroke motor dysfunction (PSMD) treated with acupuncture as the example, this paper introduces the fMRI Meta-analysis technology for integrating the relevant research results and extracting the common rules, namely image-based Meta-analysis (IBMA) and coordinate-based Meta-analysis (CBMA). Considering the higher feasibility of CBMA, three available CBMA methods are explained specially, including activation likelihood estimation (ALE), kernel density analysis (KDA), and seed-based d mapping (SDM). Focusing on the precautions and operation procedure of CBMA, the review is conducted systematically on the type of fMRI research, task design, analytical method, and the thinking integrity of fMRI Meta-analysis, and the review findings are collated in charts. It aims to assist readers to understand the abstract and complex theories and practical information of this technology efficiently, conveniently and systematically, and hopes to provide the references for the future learning and the application.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions.

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Amygdala fMRI-A Critical Appraisal of the Extant Literature.

Even before the advent of fMRI, the amygdala occupied a central space in the affective neurosciences. Yet this amygdala-centred view on emotion processing gained even wider acceptance after the inception of fMRI in the early 1990s, a landmark that triggered a goldrush of fMRI studies targeting the amygdala in vivo. Initially, this amygdala fMRI research was mostly confined to task-activation studies measuring the magnitude of the amygdala's response to emotional stimuli. Later, interest began to shift more towards the study of the amygdala's resting-state functional connectivity and task-based psychophysiological interactions. Later still, the test-retest reliability of amygdala fMRI came under closer scrutiny, while at the same time, amygdala-based real-time fMRI neurofeedback gained widespread popularity. Each of these major subdomains of amygdala fMRI research has left its marks on the field of affective neuroscience at large. The purpose of this review is to provide a critical assessment of this literature. By integrating the insights garnered by these research branches, we aim to answer the question: What part (if any) can amygdala fMRI still play within the current landscape of affective neuroscience? Our findings show that serious questions can be raised with regard to both the reliability and validity of amygdala fMRI. These conclusions force us to cast doubt on the continued viability of amygdala fMRI as a core pilar of the affective neurosciences.

Brain Imaging in Mathematics Education: An Analysis of KCI Studies

This study reviews the application of EEG, fMRI, and fNIRS in mathematics education research in South Korea, analyzing 16 significant papers from the last 25 years. The results demonstrate a significant relationship between brainwave patterns and various aspects of mathematical learning. EEG studies highlighted the role of different brainwaves in math anxiety, computation, and problem-solving. fMRI research revealed the importance of whole brain activation in spatial and numerical processing. Studies using fNIRS emphasized the prefrontal cortex's involvement in mathematical tasks, particularly in spatial and computational tasks. Conclusions highlight the need for math learning programs that enhance brain connectivity and consider problem difficulty and learner preferences. The study advocates for more research on the prefrontal cortex, particularly in relation to math anxiety, and emphasizes the importance of brain imaging techniques in understanding cognitive processes in math education, suggesting an integrated approach in future research.

Effects of clamping end-tidal CO2 on neurofluidic low-frequency oscillations.

In recent years, low-frequency oscillations (LFOs) (0.01-0.1 Hz) have been a subject of interest in resting-state functional magnetic resonance imaging research. They are believed to have many possible driving mechanisms, from both regional and global sources. Internal fluctuations in the partial pressure of CO2 (PCO2) has long been thought of as one of these major driving forces, but its exact contributions compared with other mechanisms have yet to be fully understood. This study examined the effects of end-tidal PCO2 (PetCO2) oscillations on LF cerebral hemodynamics and cerebrospinal fluid (CSF) dynamics under "clamped PetCO2" and "free-breathing" conditions. Under clamped PetCO2, a participant's PetCO2 levels were fixed to their baseline average, whereas PetCO2 was not controlled in free breathing. Under clamped PetCO2, the fractional amplitude of hemodynamic LFOs in the occipital and sensorimotor cortex and temporal lobes were found to be significantly reduced. Additionally, the fractional amplitude of CSF LFOs, measured at the fourth ventricle, was found to be reduced by almost one-half. However, the spatiotemporal distributions of blood and CSF delay times, as measured by cross-correlation in the LF domain, were not significantly altered between conditions. This study demonstrates that, while PCO2 oscillations significantly mediate LFOs, especially those observed in the CSF, other mechanisms are able to maintain LFOs, with high correlation, even in their absence.

From Past to the Future of fMRI in Pediatrics: A Bibliometric Analysis

Context: The use of functional magnetic resonance imaging (fMRI) in the evaluation of children's brains is becoming increasingly popular. However, due to the high number of publications, it's nearly impossible to read all the related articles and become aware of the crucial elements or the best approach to invest time and effort in this field. Objectives: Our goal was to gain a better overall understanding of fMRI publications and to use this information in policy and management contexts to make more efficient decisions. Methods: Data were downloaded from Scopus from the 1990s to the end of 2021 based on the theme of fMRI in pediatrics. The retrieved research articles were analyzed with the Bibliometrix package in R software. Results: From 8,544 documents written by 25,697 authors, the USA is by far the most productive country with a total of 3,609 publications. However, regarding the average article citations, Sweden is at the top of the list, with 77.79 average citations. Important themes include emotion, autism spectrum disorders, attention-deficit/hyperactivity disorders, and childhood maltreatment. Conclusions: The results of the present study on fMRI research in the field of pediatrics will be helpful for professionals to visually understand the pediatric modes and trends in fMRI.

Underlying mechanisms, clinical manifestation, and methodological considerations of functional connectivity in Dementia with Lewy Bodies

AbstractBackgroundFunctional connectivity disturbances are potential disease mechanisms related to observable symptoms in Dementia with Lewy Bodies (DLB). Despite several existing theories, the exact relationship between functional connectivity, underlying mechanisms of the DLB, and symptomatology is yet not clear. The present review addresses this gap by summarising research findings of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) research while considering the impact of different methodological choices causing mixed findings.MethodWe performed a systematic literature review of resting‐state functional connectivity in DLB following PRISMA. We searched multiple databases from January 2000 to November 2022. Keywords included “DLB”, “connectivity”, “EEG”, and “fMRI”, their synonyms and acronyms, and related terms of network analysis. The final sample contained 6 EEG and 14 fMRI studies.ResultThe main methodological choices of fMRI articles included a data‐driven approach (i.e., Independent Component Analysis or Graph Theory; 43%), seed‐based or regions of interest (50%), or their combination (7%) with most studies focusing on large‐scale resting‐state networks (71%). The overall trend of connectivity findings showed a decrease, yet more subtle changes in the directionality of connectivity were observed when using a hypothesis‐driven approach (Fig 1A). The Default Mode Network (DMN) was the most studied network with 75% of relevant articles reporting decreased connectivity. Other networks with a consistent decrease in connectivity included Executive and Sensorimotor Networks (80% and 100% of relevant studies respectively). The inter‐network connectivity revealed the mixed relationship between the DMN, executive, attentional and visual networks (VN; Fig 1B). EEG studies showed a consistent decrease in connectivity on alpha and beta bands (100% and 66% of relevant studies respectively) and disorganisation in graph properties in regions overlapping with findings from large‐scale networks.ConclusionDysfunctions in large‐scale networks likely contribute to progressive degenerative mechanisms and are consistent with visual attention and perception processing theories of psychotic symptoms in the DLB. Hence, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering underlying relationships between the networks’ disorganisation and disease mechanisms resulting in psychotic, motor, and cognitive symptoms in DLB. The choice of analytical approach might contribute to the directionality and mixed findings in the literature.

Exploring static and dynamic functional brain networks in adolescent depression using a co-produced novel irritability paradigm

Background Irritability is a core symptom of depression in adolescence and a risk factor for emotion regulation problems. However, its neural correlates are not well understood. Existing functional magnetic resonance imaging (fMRI) research on irritability typically overlooks its social context. Methods Here, we pilot a novel naturalistic fMRI paradigm targeting the social nature of irritability that was co-produced with young people (N = 88) and apply it in an independent sample of youth (N = 29, mean age 18.9 years, 77% female) with self-reported low mood that were aged 16 to 20 years. Participants were also fluent English speakers, free from MRI contraindications, and did not report a diagnosis of a neurological or neurodevelopmental condition. Our aim was to investigate whether graph theoretic and dynamical properties of functional brain networks differed between a resting state scan and our irritability paradigm. We also examined whether these brain features were associated with depressive symptoms and trait irritability. Results Using Leading Eigenvector Dynamics Analysis (LEiDA), we found that the dynamic properties of brain networks comprising default-mode and fronto-parietal regions differed significantly during the irritability paradigm compared to the rest condition. While no gross static topological differences were found between these two conditions, we found that some dynamic and topological features of emotion-related brain networks were related to trait irritability and depressive symptoms in our sample. Conclusions Although the current findings are preliminary due to the pilot nature of this study, this work showcases the feasibility of co-produced research in neuroimaging and lays a strong foundation for further study.