Resting-State fMRI: Emerging Concepts for Future Clinical Application

Vulnerable brain regions in adolescent attention deficit hyperactivity disorder: An activation likelihood estimation meta-analysis

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in adolescents characterized by inattention, hyperactivity, and impulsivity, which impact cognitive, behavioral, and emotional functioning. Resting-state functional magnetic resonance imaging (rs-fMRI) provides critical insights into the functional architecture of the brain in ADHD. Despite extensive research, specific brain regions consistently affected in ADHD patients during these formative years have not been comprehensively delineated.

AIM

To identify consistent vulnerable brain regions in adolescent ADHD patients using rs-fMRI and activation likelihood estimation (ALE) meta-analysis.

METHODS

We conducted a comprehensive literature search up to August 31, 2024, to identify studies investigating functional brain alterations in adolescents with ADHD. We utilized regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), dynamic ALFF (dALFF) and fractional ALFF (fALFF) analyses. We compared the regions of aberrant spontaneous neural activity in adolescents with ADHD with those in healthy controls (HCs) using ALE.

RESULTS

Fifteen studies (468 adolescent ADHD patients and 466 HCs) were included. Combining the ReHo and ALFF/fALFF/dALFF data, the results revealed increased activity in the right lingual gyrus [LING, Brodmann Area (BA) 18], left LING (BA 18), and right cuneus (CUN, BA 23) in adolescent ADHD patients compared with HCs (voxel size: 592-32 mm³, P < 0.05). Decreased activity was observed in the left medial frontal gyrus (MFG, BA 9) and left precuneus (PCUN, BA 31) in adolescent ADHD patients compared with HCs (voxel size: 960-456 mm³, P < 0.05). Jackknife sensitivity analyses demonstrated robust reproducibility in 11 of the 13 tests for the right LING, left LING, and right CUN and in 11 of the 14 tests for the left MFG and left PCUN.

CONCLUSION

We identified specific brain regions with both increased and decreased activity in adolescent ADHD patients, enhancing our understanding of the neural alterations that occur during this pivotal stage of development.

Abnormal resting-state neural activities of language and non-language cognitive function impairments in stroke patients with aphasia: A cross-sectional study

OBJECTIVE

Language impairments may mask non-language cognitive deficits in post-stroke aphasia (PSA) patients. Moreover, the underlying neural mechanisms of both language and non-language cognitive impairment remain unclear. This study aimed to investigate the activities and functional abnormalities of local and remote brain regions and their relationship with cognitive function in PSA patients, to provide more effective tips in future clinical therapy.

METHODS

This cross-sectional study included 46 PSA patients and 40 controls, who underwent language and non-language cognitive assessments, and resting-state functional magnetic resonance imaging (rs-fMRI). We then examined the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) based on a modest sample size (46 PSA patients and 40 normal controls (NCs)). Independent two-sample t-tests were used to identify differences in these measures between PSA patients and NCs. Moreover, partial correlation analyses were performed to determine the correlation between FC from the affected brain regions and language, and non-language cognitive performance in PSA patients.

RESULTS

This study revealed that both fALFF and ReHo in PSA patients presented significantly lower in the right superior cerebellum, left thalamus, and left middle frontal gyrus, along with increased values in the right superior frontal gyrus (dorsolateral part) (p < 0.05). Notably, decreased FC between the left middle frontal gyrus and orbital part of the left inferior frontal gyrus was significantly associated with both language and non-language cognitive performance (p < 0.05). In addition, PSA patients were further divided into fluent and non-fluent groups. The results revealed that non-fluent patients demonstrated worse overall cognitive functioning, accompanied by reduced FC between the left thalamus and the left supplementary motor area (p < 0.001).

CONCLUSION

This study provides new evidence that abnormal neural activities and functional connectivities within specific brain regions may play crucial roles in language and non-language cognitive function. The underlying mechanisms of non-language cognitive decline accompanied by impaired language function in PSA patients may be a partial overlap between language and cognitive networks. In the future, combining language and non-language functions and designing a comprehensive treatment plan will be the focus of rehabilitation.

Neural network localization in Parkinson's disease with impulse control disorders

Background

There is a huge heterogeneity of magnetic resonance imaging findings in Parkinson's disease (PD) with impulse control disorders (ICDs) studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of PD with ICDs could be reconciled in a common network.

Methods

In this study, an initial systematic literature review was conducted to collect and evaluate whole-brain functional and structural magnetic resonance imaging studies related to PD with ICDs. We subsequently utilized the Human Connectome Project (HCP) dataset (n = 1,093) and a novel functional connectivity network mapping (FCNM) technique to identify a common brain network affected in PD with ICDs.

Results

A total of 19 studies with 25 contrasts, incorporating 345 individuals with PD and ICDs, and 787 individuals with PD without ICDs were included in the analysis. By using the HCP dataset and a novel FCNM technique, we ultimately identified that the aberrant neural networks predominantly involve the default mode network (middle and inferior temporal gyrus, anterior cingulate cortex, angular gyrus) and subcortical network (caudate nucleus).

Conclusion

This study suggests that the heterogeneous neuroimaging findings in PD with ICDs can be attributed to shared abnormalities in the default mode and subcortical networks. These dysfunctions are associated with impaired self-regulation, decision-making, and heightened impulsivity in PD with ICDs. Our findings integrate diverse neuroimaging results from previous studies, providing a clearer understanding of the neurobiological mechanisms underlying PD with ICDs at a network level.

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.

Non-invasive Assessment of Cerebral Hemodynamics Using Resting-State Functional Magnetic Resonance Imaging in Multiple Sclerosis and Age-Related White Matter Lesions

Perfusion changes in white matter (WM) lesions and normal-appearing brain regions play an important pathophysiological role in multiple sclerosis (MS). However, most perfusion imaging methods require exogenous contrast agents, the repeated use of which is discouraged. Using resting-state functional MRI (rs-fMRI), we aimed to investigate differences in perfusion between white matter lesions and normal-appearing brain regions in MS and healthy participants. A total of 41 MS patients and 41 age- and sex-matched healthy participants received rs-fMRI, from which measures of cerebral hemodynamics and oxygenation were extracted and compared across brain regions and study groups using within- and between-group nonparametric tests, linear mixed models, and robust multiple linear regression. We found longer blood arrival times and lower blood volumes in lesions than in normal-appearing WM. Higher blood volumes were found in MS patients' deep WM lesions compared to healthy participants, and blood arrival time was more delayed in MS patients' deep WM lesions than in healthy participants. Delayed blood arrival time in the cortical grey matter was associated with greater cognitive impairment in MS patients. Perfusion imaging using rs-fMRI is useful for WM lesion characterization. rs-fMRI-based blood arrival times and volumes are associated with cognitive function.

Flow augmentation therapies preserve brain network integrity and hemodynamics in a canine permanent occlusion model

The acute phase of ischemic stroke presents a critical window for therapeutic intervention, where novel approaches such as hyper-acute cerebral flow augmentation offer promising avenues for neuroprotection. In this study, we investigated the effects of two such therapies, NEH (a combination of norepinephrine and hydralazine) and Sanguinate (pegylated bovine carboxyhemoglobin), on resting-state functional connectivity, global mean signal (GMS), and blood oxygen level-dependent (BOLD) time lag in a pre-clinical canine model of stroke via permanent occlusion of the middle cerebral artery (total of n = 40 IACUC-approved mongrel canines randomly split into control/natural history and two treatment groups). Utilizing group independent component analysis (ICA), we identified and examined the integrity of sensorimotor and visual networks both pre- and post-occlusion, across treatment and control groups. Our results demonstrated that while the control group exhibited significant disruptions in these networks following stroke, the treatment groups showed remarkable preservation of network integrity. Voxel-wise functional connectivity analysis revealed less pronounced alterations in the treatment groups, suggesting maintained neural connections. Notably, the treatments stabilized GMS, with only minimal reductions observed post-occlusion compared to significant decreases in the control group. Furthermore, BOLD time-lag unity plots indicated that NEH and Sanguinate maintained consistent hemodynamic response timing, as evidenced by tighter clustering around the line of unity, suggesting a potential neuroprotective effect. These findings were underscored by robust statistical analyses, including paired T-tests and Mann-Whitney U tests, which confirmed the significance of the connectivity changes observed. The correlation of BOLD time-lag variations with neuroimaging functional biomarkers highlighted the impact of stroke and the efficacy of early therapeutic interventions. Our study supports the further study of flow augmentation therapies such as NEH and Sanguinate in stroke treatment protocols and suggests flow augmentation therapies should be further explored in an effort to improve patient outcomes.

Neuroimaging of motor recovery after ischemic stroke - functional reorganization of motor network

The long-term motor outcome of acute stroke patients may be correlated to the reorganization of brain motor network. Abundant neuroimaging studies contribute to understand the pathological changes and recovery of motor networks after stroke. In this review, we summarized how current neuroimaging studies have increased understanding of reorganization and plasticity in post stroke motor recovery. Firstly, we discussed the changes in the motor network over time during the motor-activation and resting states, as well as the overall functional integration trend of the motor network. These studies indicate that the motor network undergoes dynamic bilateral hemispheric functional reorganization, as well as a trend towards network randomization. In the second part, we summarized the current study progress in the application of neuroimaging technology to early predict the post-stroke motor outcome. In the third part, we discuss the neuroimaging techniques commonly used in the post-stroke recovery. These methods provide direct or indirect visualization patterns to understand the neural mechanisms of post-stroke motor recovery, opening up new avenues for studying spontaneous and treatment-induced recovery and plasticity after stroke.

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.

Neuropsychological, plasma marker, and functional connectivity changes in Alzheimer's disease patients infected with COVID-19

Introduction

Patients with COVID-19 may experience various neurological conditions, including cognitive impairment, encephalitis, and stroke. This is particularly significant in individuals who already have Alzheimer's disease (AD), as the cognitive impairments can be more pronounced in these cases. However, the extent and underlying mechanisms of cognitive impairments in COVID-19-infected AD patients have yet to be fully investigated through clinical and neurophysiological approaches.

Methods

This study included a total of 77 AD patients. Cognitive functions were assessed using neuropsychiatric scales for all participants, and plasma biomarkers of amyloid protein and tau protein were measured in a subset of 25 participants. To investigate the changes in functional brain connectivity induced by COVID-19 infection, a cross-sectional neuroimaging design was conducted involving a subset of 37 AD patients, including a control group of 18 AD participants without COVID-19 infection and a COVID-19 group consisting of 19 AD participants.

Results

For the 77 AD patients between the stages of pre and post COVID-19 infection, there were significant differences in cognitive function and psychobehavioral symptoms on the Montreal Scale (MoCA), the neuropsychiatric inventory (NPI), the clinician's global impression of change (CIBIC-Plus), and the activity of daily living scale (ADL). The COVID-19 infection significantly decreased the plasma biomarker level of Aβ42 and increased the plasma p-tau181 level in AD patients. The COVID-19-infected AD patients show decreased local coherence (LCOR) in the anterior middle temporal gyrus and decreased global correlation (GCOR) in the precuneus and the medial prefrontal cortex.

Conclusion

The findings suggest clinical, cognitive, and neural alterations following COVID-19 infection in AD patients and emphasize the need for close monitoring of symptoms in AD patients who have had COVID-19 and further exploration of the underlying mechanisms.

Network-based diagnostic probability estimation from resting-state functional magnetic resonance imaging

Brain functional connectivity is a useful biomarker for diagnosing brain disorders. Connectivity is measured using resting-state functional magnetic resonance imaging (rs-fMRI). Previous studies have used a sequential application of the graphical model for network estimation and machine learning to construct predictive formulas for determining outcomes (e.g., disease or health) from the estimated network. However, the resulting network had limited utility for diagnosis because it was estimated independent of the outcome. In this study, we proposed a regression method with scores from rs-fMRI based on supervised sparse hierarchical components analysis (SSHCA). SSHCA has a hierarchical structure that consists of a network model (block scores at the individual level) and a scoring model (super scores at the population level). A regression model, such as the multiple logistic regression model with super scores as the predictor, was used to estimate diagnostic probabilities. An advantage of the proposed method was that the outcome-related (supervised) network connections and multiple scores corresponding to the sub-network estimation were helpful for interpreting the results. Our results in the simulation study and application to real data show that it is possible to predict diseases with high accuracy using the constructed model.

Resting-state networks representation of the global phenomena

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs' spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events' power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers.