What does fMRI tell us about neuronal activity?

Persistent alterations in gray matter in COVID-19 patients experiencing sleep disturbances: a 3-month longitudinal study

JOURNAL/nrgr/04.03/01300535-202510000-00030/figure1/v/2024-11-26T163120Z/r/image-tiff Sleep disturbances are among the most prevalent neuropsychiatric symptoms in individuals who have recovered from severe acute respiratory syndrome coronavirus 2 infections. Previous studies have demonstrated abnormal brain structures in patients with sleep disturbances who have recovered from coronavirus disease 2019 (COVID-19). However, neuroimaging studies on sleep disturbances caused by COVID-19 are scarce, and existing studies have primarily focused on the long-term effects of the virus, with minimal acute phase data. As a result, little is known about the pathophysiology of sleep disturbances in the acute phase of COVID-19. To address this issue, we designed a longitudinal study to investigate whether alterations in brain structure occur during the acute phase of infection, and verified the results using 3-month follow-up data. A total of 26 COVID-19 patients with sleep disturbances (aged 51.5 ± 13.57 years, 8 women and 18 men), 27 COVID-19 patients without sleep disturbances (aged 47.33 ± 15.98 years, 9 women and 18 men), and 31 age- and gender-matched healthy controls (aged 49.19 ± 17.51 years, 9 women and 22 men) were included in this study. Eleven COVID-19 patients with sleep disturbances were included in a longitudinal analysis. We found that COVID-19 patients with sleep disturbances exhibited brain structural changes in almost all brain lobes. The cortical thicknesses of the left pars opercularis and left precuneus were significantly negatively correlated with Pittsburgh Sleep Quality Index scores. Additionally, we observed changes in the volume of the hippocampus and its subfield regions in COVID-19 patients compared with the healthy controls. The 3-month follow-up data revealed indices of altered cerebral structure (cortical thickness, cortical grey matter volume, and cortical surface area) in the frontal-parietal cortex compared with the baseline in COVID-19 patients with sleep disturbances. Our findings indicate that the sleep disturbances patients had altered morphology in the cortical and hippocampal structures during the acute phase of infection and persistent changes in cortical regions at 3 months post-infection. These data improve our understanding of the pathophysiology of sleep disturbances caused by COVID-19.

Linking brain activation to standing balance performance: A systematic review and meta analysis of functional near-infrared spectroscopy literature

BACKGROUND

Functional Near-Infrared Spectroscopy (fNIRS) holds promise for clinical applications in the field of balance impairment amelioration; however, the relationship between fNIRS metrics and balance performance remains uncertain. We aimed to quantify the correlations between fNIRS-derived brain activation and standing balance performance, and determine variables that influence these associations.

METHODS

We systematically reviewed English-language studies, published across PuBMed, PsycINFO, Embase, CINAHL, Ovid Medline, and Web of Science from inception up until July 1, 2024, that assessed standing balance tasks in adults > 18 years old with or without medical diagnosis measured with fNIRS. Pooled correlation coefficients were synthesized using a random effects restricted maximum likelihood model.

RESULTS

Overall, 17 studies were included with 420 participants. Key factors influencing the identified relationships were brain region and participant diagnosis. We identified moderate correlations between balance performance and cortical activation recorded by fNIRS in the supplementary motor area (SMA) (r = 0.52, 95 % CI = 0.39 0.64), and the prefrontal cortex (PFC) (r = 0.47, 95 % CI=0.32 - 0.60). In the PFC, increased oxygenated haemoglobin (HbO) was negatively associated with balance measures. The reverse relationship was reported in the PFC for individuals with physical and cognitive impairment. In the SMA, HbO was positively associated with balance. Few studies found associations between deoxygenated haemoglobin (HbR) and total hemoglobin (HbT) with balance performance.

SIGNIFICANCE

Current evidence supports a relationship between fNIRS measures, specifically HbO, with standing balance performance. This relationship depends on the brain region measured, age, and the diagnosis of the participants. To better understand this relationship, there is a need to report standardized balance performance metrics alongside other metrics of interest to better synthesize data across publications. Improved understanding the neural basis of standing balance with fNIRS will lead to more informed interventions for balance rehabilitation.

Functional Neural Connectivity of the Mouse Brain using Photoacoustic Ultrasound Imaging

Brain connectomes are insightful models that describe the connectivity of different regions throughout the brain. These connectomes are traditionally generated through temporal correlation of blood oxygen level dependent (BOLD) signals detected by functional magnetic resonance imaging (fMRI). Photoacoustic ultrasound (PAU) can also detect oxygenation levels while being more accessible and cost effective than fMRI. We propose the use of PAU to generate brain connectomes as an alternative to fMRI. In this study we successfully developed a pipeline for processing PAU data from whole brain scans of mice models and found that the connectomes it produced were comparable to those generated by fMRI, particularly, in detecting connections previously documented in the literature. Our findings suggest that PAU is a promising alternative to fMRI for mapping brain connectome, offering advantages in sensitivity and accessibility, making it a valuable tool for future research on brain connectivity.

Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders

Schizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision.

Interfacing with the Brain: How Nanotechnology Can Contribute

Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.

Open-transmit and flexible receiver array for high resolution ultrahigh-field fMRI of the human sensorimotor cortex

In this study, we developed an open-transmit and 24-channel flexible receiver head coil assembly tailored for high-resolution ultrahigh-field functional magnetic resonance imaging (fMRI) of the human somatosensory and motor cortex. Leveraging the increased signal-to-noise ratio (SNR) and spatial resolution of ultrahigh field MRI, we address the technical challenges inherent in fMRI coil design. The open-birdcage transmit coil enhances patient comfort and enables visual task implementation, demonstrating superior performance in transmit efficiency and specific absorption rate distribution compared to conventional coils. Furthermore, the 24-channel flexible receiver head coil offers enhanced SNR and image quality, facilitating sub-millimeter vascular-space-occupancy imaging for precise functional mapping. These advancements provide valuable tools for unraveling the intricacies of somatosensory and motor cortex function. By enriching human brain functional studies, they contribute significantly to our understanding of the mechanisms underlying somatosensory and motor cortex function and may have valuable clinical applications in neurology and neuroscience research.

Improved whole-brain reconfiguration efficiency reveals mechanisms of speech rehabilitation in cleft lip and palate patients: an fMRI study

Introduction

Cleft lip and/or palate (CLP) patients still have severe speech disorder requiring speech rehabilitation after surgical repair. The clarity of language rehabilitation is evaluated clinically by the Language Rehabilitation Scale. However, the pattern and underlying mechanisms of functional changes in the brain are not yet clear. Recent studies suggest that the brain's reconfiguration efficiency appears to be a key feature of its network dynamics and general cognitive abilities. In this study, we compared the association between rehabilitation effects and reconfiguration efficiency.

Methods

We evaluated CLP patients with speech rehabilitation (n = 23) and without speech rehabilitation (n = 23) and normal controls (n = 25). Assessed CLP patients on the Chinese Speech Intelligibility Test Word Lists and collected fMRI data and behavioral data for all participants. We compared behavioral data and task activation levels between participants for between-group differences and calculated reconfiguration efficiencies for each task based on each participant. In patients, we correlated reconfiguration efficiency with task performance and measured the correlation between them.

Results

Behaviorally, CLP patients with rehabilitation scored significantly higher than those without rehabilitation on the Chinese Speech Intelligibility Test Word Lists. Rehabilitation caused local brain activation levels of CLP patients to converge toward those of controls, indicating rehabilitative effects on brain function. Analysis of reconfiguration efficiency across tasks at the local and whole-brain levels identified underlying recovery mechanisms. Whole-brain reconfiguration efficiency was significantly and positively correlated with task performance.

Conclusion

Our results suggest that speech rehabilitation can improve the level of language-related brain activity in CLP patients, and that reconfiguration efficiency can be used as an assessment index of language clarity to evaluate the effectiveness of brain rehabilitation in CLP patients, a finding that can provide a better understanding of the degree of brain function recovery in patients.

The causal association between resting state intrinsic functional networks and neurodegeneration

Alterations of resting state intrinsic functional networks have been associated with neurodegenerative diseases even before the onset of cognitive symptoms. Emerging hypotheses propose a role of resting state intrinsic functional networks alterations in the risk or vulnerability to neurodegeneration. It is unknown whether intrinsic functional network alterations can be causal for neurodegenerative diseases. We sought to answer this question using two-sample Mendelian randomization. Using the largest genome-wide association study of resting state intrinsic functional connectivity (n = 47 276), we generated genetic instruments (at the significance level 2.8 ×10-11) to proxy resting state intrinsic functional network features. Based on the known brain regions implicated in different neurodegenerative diseases, we generated genetically proxied resting state intrinsic functional features and tested their association with their paired neurodegenerative outcomes: features in parieto-temporal regions and Alzheimer dementia (111 326 cases, 677 663 controls); frontal region and frontotemporal dementia (2154 cases, 4308 controls); temporal pole region and semantic dementia (308 cases, 616 controls), and occipital region with Lewy body dementia (LBD) (2591 cases, 4027 controls). Major depressive disorder outcome (170 756 cases, 329 443 controls) was included as a positive control and tested for its association with genetically proxied default mode network (DMN) exposure. Inverse-variance weighted analysis was used to estimate the association between the exposures (standard deviation units) and outcomes. Power and sensitivity analyses were completed to assess the robustness of the results. None of the genetically proxied functional network features were significantly associated with neurodegenerative outcomes (adjusted P value >0.05), despite sufficient calculated power. Two resting state features in the visual cortex showed a nominal level of association with LBD (P = 0.01), a finding that was replicated using a different instrument (P = 0.03). The genetically proxied DMN connectivity was associated with the risk of depression (P = 0.024), supporting the validity of the genetic instruments. Sensitivity analyses were supportive of the main results. This is the first study to comprehensively assess the potential causal effect of resting state intrinsic functional network features on the risk of neurodegeneration. Overall, the results do not support a causal role for the tested associations. However, we report a nominal association between visual network connectivity and Lewy body dementia that requires further evaluation.

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia

The association between the human brain and gut microbiota, known as the "brain-gut-microbiota axis", is involved in the neuropathological mechanisms of schizophrenia (SZ); however, its association patterns and correlations with symptom severity and neurocognition are still largely unknown. In this study, 43 SZ patients and 55 normal controls (NCs) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) and gut microbiota data were acquired for each participant. First, the brain features of brain images and functional brain networks were computed from rs-fMRI data; the gut features of gut microbiota abundance and the gut microbiota network were computed from gut microbiota data. Second, we propose a novel methodology to construct an individual brain-gut microbiota network (BGMN) for each participant by combining the brain and gut features via multiple strategies. Third, discriminative models between SZ patients and NCs were built using the connectivity matrices of the BGMN as input features. Moreover, the correlations between the most discriminative features and the scores of symptom severity and neurocognition were analyzed in SZ patients. The results showed that the best discriminative model between SZ patients and NCs was achieved using the connectivity matrices of the BGMN when all the brain and gut features were integrated, with an accuracy of 0.90 and an area under the curve value of 0.97. The most discriminative features were related primarily to the genera Faecalibacterium and Collinsella, in which the genus Faecalibacterium was linked to the visual system and subcortical cortices and the genus Collinsella was linked to the default network and subcortical cortices. Furthermore, parts of the most discriminative features were significantly correlated with the scores of neurocognition in the SZ patients. The methodology for constructing individual BGMNs proposed in this study can help us reveal the associations between the brain and gut microbiota and understand the neuropathology of SZ.

BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological conditions. Despite these achievements, the aspect of prediction uncertainty has been relatively underexplored in brain fMRI data analysis. Accurate uncertainty estimation is essential for trustworthy learning, given the challenges associated with brain fMRI data acquisition and the potential diagnostic implications for patients. To address this gap, we introduce a novel posterior evidential network, named the Brain Posterior Evidential Network (BPEN), designed to capture both aleatoric and epistemic uncertainty in the analysis of brain fMRI data. We conducted comprehensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and ADNI-depression (ADNI-D) cohorts, focusing on predictions for mild cognitive impairment (MCI) and depression across various diagnostic groups. Our experiments not only unequivocally demonstrate the superior predictive performance of our BPEN model compared to existing state-of-the-art methods but also underscore the importance of uncertainty estimation in predictive models.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Entropy and Complexity Tools Across Scales in Neuroscience: A Review

Understanding the brain's intricate dynamics across multiple scales-from cellular interactions to large-scale brain behavior-remains one of the most significant challenges in modern neuroscience. Two key concepts, entropy and complexity, have been increasingly employed by neuroscientists as powerful tools for characterizing the interplay between structure and function in the brain across scales. The flexibility of these two concepts enables researchers to explore quantitatively how the brain processes information, adapts to changing environments, and maintains a delicate balance between order and disorder. This review illustrates the main tools and ideas to study neural phenomena using these concepts. This review does not delve into the specific methods or analyses of each study. Instead, it aims to offer a broad overview of how these tools are applied within the neuroscientific community and how they are transforming our understanding of the brain. We focus on their applications across scales, discuss the strengths and limitations of different metrics, and examine their practical applications and theoretical significance.

Early Detection of High-Altitude Hypoxic Brain Injury by In Vivo Electrochemistry

High-altitude hypoxic brain injury (HHBI) is a kind of acute mountain sickness and the survival rate of patients with HHBI can be improved only if it is detected and treated at the early stage. However, limited by speediness and accuracy, it is still very difficult for most of current approaches to realize the early detection of HHBI. We propose herein a novel strategy for this goal based on spatiotemporal changes in the brain oxygen level. As revealed by in vivo electrochemistry, the characteristic changes of brain oxygen level under the high-altitude exposure are directly associated with the brain hypoxia status. Given brain hypoxia is the main pathogenesis of HHBI, the degree of HHBI can be diagnosed by the variation of brain oxygen, making the early detection of HHBI feasible. In addition, the risk of HHBI for mouse exposed to high-altitude hypoxia environments can be also prognosed days in advance.

The role of fMRI in the mind decoding process in adults: a systematic review

BACKGROUND

Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.

METHODOLOGY

A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms).

RESULTS

This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others' mental states).

CONCLUSIONS

This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field.

Characterizing astrocyte-mediated neurovascular coupling by combining optogenetics and biophysical modeling

Vasoactive signaling from astrocytes is an important contributor to the neurovascular coupling (NVC), which aims at providing energy to neurons during brain activation by increasing blood perfusion in the surrounding vasculature. Pharmacological manipulations have been previously combined with experimental techniques (e.g., transgenic mice, uncaging, and multiphoton microscopy) and stimulation paradigms to isolate in vivo individual pathways of the astrocyte-mediated NVC. Unfortunately, these pathways are highly nonlinear and non-additive. To separate these pathways in a unified framework, we combine a comprehensive biophysical model of vasoactive signaling from astrocytes with a unique optogenetic stimulation method that selectively induces astrocytic Ca2+ signaling in a large population of astrocytes. We also use a sensitivity analysis and an optimization technique to estimate key model parameters. Optogenetically-induced Ca2+ signals in astrocytes cause a cerebral blood flow (CBF) response with two major components. Component-1 was rapid and smaller (ΔCBF∼13%, 18 seconds), while component-2 was slowest and highest (ΔCBF ∼18%, 45 seconds). The proposed biophysical model was adequate in reproducing component-2, which was validated with a pharmacological manipulation. Model's predictions were not in contradiction with previous studies. Finally, we discussed scenarios accounting for the existence of component-1, which once validated might be included in our model.

Acting with awareness is positively correlated with dorsal anterior cingulate cortex glutamate concentration but both are impaired in Internet gaming disorder

Internet gaming disorder (IGD) is increasingly recognized as a public concern for its adverse impacts on cognition and mental health. In IGD, the transition from goal-directed actions to habitual and eventually compulsive behaviors is accompanied by altered neural response within the dorsal anterior cingulate cortex (dACC), a critical region involved in conscious actions. However, the neurochemical profile of the dACC in IGD and its relationship with behavioral awareness remain poorly understood. In this study, 1H-magnetic resonance spectroscopy was employed to quantify dACC glutamate concentration and examine its association with the capacity for 'acting with awareness' among 21 participants with IGD and 19 recreational game users. Results indicated that dACC glutamate levels and behavioral awareness were significantly lower in the IGD group compared to recreational game users. Moreover, a significant positive correlation between awareness and dACC glutamate concentration emerged in the recreational game users' group, a relationship attenuated in those with IGD. In an independent cohort of 107 participants, the positive association between awareness and dACC glutamate concentration was replicated. These findings suggest that reduced dACC glutamate in IGD may underlie diminished awareness of maladaptive habitual behaviors. Enhancing dACC neural excitability through neuromodulation or mindfulness training could represent a potential intervention to restore behavioral awareness.

Pediatric stroke: We need to look for it

PURPOSE

This review provides a comprehensive overview of the characteristics and diagnosis of pediatric stroke, emphasizing the importance of early recognition and accurate assessment. Pediatric stroke is a complex condition with diverse etiologies, and its timely diagnosis is critical for initiating appropriate interventions and improving clinical outcomes.

RECENT FINDINGS

Recent advances in neuroimaging techniques, including magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), have significantly enhanced the diagnostic capabilities for pediatric stroke. Additionally, a better understanding of its underlying etiologies in specific cases, and of the importance of differential diagnosis have improved the outcome and prevention strategies in this vulnerable population. Despite these improvements, though, research still has a long way to go to optimize the management of this condition.

SUMMARY

Timely and accurate diagnosis of pediatric stroke remains a challenge due to its rarity and variability in clinical presentation, and to the presence of many mimic conditions. The integration of clinical evaluation, neuroimaging, and comorbidities analysis is crucial for achieving a precise diagnosis and guiding tailored treatment strategies for affected children.

Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates

While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Neuroimaging in thyroid eye disease: A systematic review

Thyroid eye disease (TED) is an organ-specific autoimmune disease secondary largely to hyperthyroid Graves' disease, which profoundly affects patients' visual function, appearance, and physical and mental well-being. Emerging neuroimaging studies have reported alterations in the brains of patients with TED, suggesting that the impact of this autoimmune disease may extend beyond the orbit. This systematic review aims to consolidate the neuroimaging evidence that describes the brain alterations of TED. We analyzed information from thirty-one related studies involving 1349 TED patients and 710 healthy controls, employing multimodal neuroimaging techniques such as structural magnetic resonance imaging (MRI), functional MRI, diffusion MRI, and metabolic MRI. These studies define the brain alterations in regions associated with vision, cognition, and emotion regulation, such as gray matter volume changes, altered functional connectivity and activity, and microstructural modifications, revealing the neurological impact of TED beyond the orbit. Notably, there was convergence across these studies indicating predominant abnormalities within the occipital and parietal lobes. This review underscores the critical role of advanced neuroimaging techniques in unraveling the complex neuropathological mechanism of TED, laying a foundation for future research and potential therapeutic targets.

The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research

Beginning with the successful sequencing of the human genome two decades ago, the possibility of developing personalized health interventions based on one's biology has captured the imagination of researchers, medical providers, and individuals seeking health care services. However, the application of a personalized medicine approach to emotional and behavioral health has lagged behind the development of personalized approaches for physical health conditions. There is potential value in developing improved methods for integrating biological science with prevention science to identify risk and protective mechanisms that have biological underpinnings, and then applying that knowledge to inform prevention and intervention services for emotional and behavioral health. This report represents the work of a task force appointed by the Board of the Society for Prevention Research to explore challenges and recommendations for the integration of biological and prevention sciences. We present the state of the science and barriers to progress in integrating the two approaches, followed by recommended strategies that would promote the responsible integration of biological and prevention sciences. Recommendations are grounded in Community-Based Participatory Research approaches, with the goal of centering equity in future research aimed at integrating the two disciplines to ultimately improve the well-being of those who have disproportionately experienced or are at risk for experiencing emotional and behavioral problems.

Psychedelic 5-HT2A receptor agonism: neuronal signatures and altered neurovascular coupling

Psychedelics hold therapeutic promise for mood disorders due to rapid, sustained results. Human neuroimaging studies have reported dramatic serotonin-2A receptor-(5-HT2AR)-dependent changes in functional brain reorganization that presumably reflect neuromodulation. However, the potent vasoactive effects of serotonin have been overlooked. We found psilocybin-mediated alterations to fMRI-HRFs in humans, suggesting potentially altered NVC. To assess the neuronal, hemodynamic, and neurovascular coupling (NVC) effects of the psychedelic 5-HT2AR agonist, 2,5-Dimethoxy-4-iodoamphetamine (DOI), wide-field optical imaging (WFOI) was used in awake Thy1-jRGECO1a mice during stimulus-evoked and resting-state conditions. While DOI partially altered tasked-based NVC, more pronounced NVC alterations occurred under resting-state conditions and were strongest in association regions. Further, calcium and hemodynamic activity reported different accounts of RSFC changes under DOI. Co-administration of DOI and the 5-HT2AR antagonist, MDL100907, reversed many of these effects. Dissociation between neuronal and hemodynamic signals emphasizes a need to consider neurovascular effects of psychedelics when interpreting blood-oxygenation-dependent neuroimaging measures.

Enhancing perceptual, attentional, and working memory demands through variable practice schedules: insights from high-density EEG multi-scale analyses

Contextual interference (CI) enhances learning by practicing motor tasks in a random order rather than a blocked order. One hypothesis suggests that the benefits arise from enhanced early perceptual/attentional processes, while another posits that better learning is due to highly activated mnemonic processes. We used high-density electroencephalography in a multi-scale analysis approach, including topographic analyses, source estimations, and functional connectivity, to examine the intertwined dynamics of attentional and mnemonic processes within short time windows. We recorded scalp activity from 35 participants as they performed an aiming task at three different distances, under both random and blocked conditions using a crossover design. Our results showed that topographies associated with processes related to perception/attention (N1, P3a) and working memory (P3b) were more pronounced in the random condition. Source estimation analyses supported these findings, revealing greater involvement of the perceptual ventral pathway, anterior cingulate and parietal cortices, along with increased functional connectivity in ventral alpha and frontoparietal theta band networks during random practice. Our results suggest that CI is driven, in the random compared to the blocked condition, by enhanced specific processes such as perceptual, attentional, and working memory processes, as well as large-scale functional networks sustaining more general attentional and executive processes.

Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human using chronological age (CA) as label. However, whether the BA of macaque, one of the most important animal models, can also be reliably predicted is largely unknown. To address this question, we propose a novel deep learning model called Multi-Branch Vision Transformer (MB-ViT) to fuse multi-scale (i.e., from coarse-grained to fine-grained) brain functional connectivity (FC) patterns derived from resting state functional magnetic resonance imaging (rs-fMRI) data to predict BA of macaques. The discriminative functional connections and the related brain regions contributing to the prediction are further identified based on Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our proposed model successfully predicts BA of 450 normal rhesus macaques from the publicly available PRIMatE Data Exchange (PRIME-DE) dataset with lower mean absolute error (MAE) and mean square error (MSE) as well as higher Pearson's correlation coefficient (PCC) and coefficient of determination (R2) compared to other baseline models. The correlation between the predicted BA and CA reaches as high as 0.82 of our proposed method. Furthermore, our analysis reveals that the functional connections predominantly contributing to the prediction results are situated in the primary motor cortex (M1), visual cortex, area v23 in the posterior cingulate cortex, and dysgranular temporal pole. In summary, our proposed deep learning model provides an effective tool to accurately predict BA of primates (macaque in this study), and lays a solid foundation for future studies of age-related brain diseases in those animal models.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, 1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal; 2) we propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

Hyperglycemia selectively increases cerebral non-oxidative glucose consumption without affecting blood flow

Multiple studies have shown that hyperglycemia increases the cerebral metabolic rate of glucose (CMRglc) in subcortical white matter. This observation remains unexplained. Using positron emission tomography (PET) and euinsulinaemic glucose clamps, we found, for the first time, that acute hyperglycemia increases non-oxidative CMRglc (i.e., aerobic glycolysis (AG)) in subcortical white mater as well as in medial temporal lobe structures, cerebellum and brainstem, all areas with low euglycemic CMRglc. Surprisingly, hyperglycemia did not change regional cerebral blood flow (CBF), the cerebral metabolic rate of oxygen (CMRO2), or the blood-oxygen-level-dependent (BOLD) response. Regional gene expression data reveal that brain regions where CMRglc increased have greater expression of hexokinase 2 (HK2). Simulations of glucose transport revealed that, unlike hexokinase 1, HK2 is not saturated at euglycemia, thus accommodating increased AG during hyperglycemia.

A randomized placebo controlled trial demonstrates the effect of dl-methylephedrine on brain functions is weaker than that of pseudoephedrine

Intellectual drug doping in athletics by using stimulants that affect central nervous system functions has been diversified. Stimulants are regulated by the World Anti-Doping Agency according to their levels of urinary concentration. Positron emission tomography could evaluate how stimulants affect central nervous system functions. We aimed to evaluate the effect of stimulants on brain function by examining the difference in brain dopamine transporter occupancy by PET after administration of dl-methylephedrine or pseudoephedrine at the clinical maximum daily dose. Four PET scans without and with drug administration (placebo, dl-methylephedrine 150 mg and pseudoephedrine 240 mg) were performed. The concentrations of dl-methylephedrine and pseudoephedrine in plasma and urine were measured. DAT occupancies in the striatum with placebo, dl-methylephedrine and pseudoephedrine were calculated by PET images. The urinary concentration of dl-methylephedrine (12.7 µg/mL) exceeded the prohibited concentration (10 µg/mL), but the DAT occupancy with dl-methylephedrine (6.1%) did not differ (p = 0.92) from that with placebo (6.2%). By contrast, although the urinary concentration of pseudoephedrine (144.8 µg/mL) was below the prohibited concentration (150 μg/mL), DAT occupancy with pseudoephedrine was 18.4%, which was higher than that with placebo (p = 0.009). At the maximum clinical dose, dl-methylephedrine was shown to have weaker effects on brain function than pseudoephedrine.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

The amplitude of low-frequency fluctuations is correlated with birth trauma in patients with postpartum post-traumatic stress disorder

Postpartum post-traumatic stress disorder (PP-PTSD) is a severe mental disorder worldwide. In recent years, some studies have reported that PP-PTSD stems from birth trauma. The present study was dedicated in finding ways to predict the occurrence of emergency caesarean section (ECS), trying to analyze the methods to reduce incidence of PP-PTSD on this basis, further exploring the neuroimaging changes in PP-PTSD. A total of 245 primiparas with intention of vaginal delivery were recruited. The internal tocodynamometry measurement was performed during labor for all mothers, and respectively taken at 3-5 cm, 5-8 cm, and 8-10 cm of cervical dilation. The receiver operating characteristic (ROC) curve and Binary logistic regression analyses were also performed to identify fetal head descending thrust that might help in the prediction of ECS. Resting-state magnetic resonance imaging (MRI) was performed on 26 patients diagnosed with PP-PTSD of 245 mothers, the amplitude of low-frequency fluctuations (ALFF) technology was used to observe the spontaneous neural activity of all PP-PTSD patients and correlation analyses were performed. We found that the natural delivery rate of mothers with fetal head descending thrust <16.29 N (5-8 cm), 26.36 N (8-10 cm) were respectively lower than other mothers with fetal head descending thrust ≥16.29 N (5-8 cm), 26.36 N (8-10 cm) (P < 0.05). The ROC curve analysis showed that the area under the receiver operating characteristic curve (AUC) value of thrust (5-8 cm) was 0.896 (95% CI: 0.854-0.938, p < 0.001), AUC of thrust(8-10 cm) was 0.786 (95% CI: 0.714-0.858, p < 0.001), which showed strong potential for predicting ECS. In addition, the Binary logistic regression analysis showed thrust (5-8 cm) and thrust (8-10 cm) were independent correlates of ECS. The resting-state functional magnetic resonance imaging (rs-fMRI) results indicated that PP-PTSD group showed decreased ALFF in the bilateral insula cortex (IC), right anterior cingulate cortex (ACC), and left midcingulate cortex (MCC) compared with healthy postpartum women (HPW) (false discovery rate (FDR) correction q-value < 0.05). The ALFF value of the right ACC was positively correlated with the Perinatal Post-traumatic stress disorder Questionnaire (PPQ) score (r = 0.4046 p = 0.0403) and Posttraumatic Stress Disorder Checklist-Civilian Version (PCL-C) score (r = 0.3909 p = 0.0483). The internal tocodynamometry measurement can serve as a predictive tool for ECS, on this basis, the implementation of effective emotional support may help to reduce the incidence of PP-PTSD. Besides, this study has verified the presence of altered ALFF in the brain regions of PP-PTSD patients, mainly involving the bilateral IC, right ACC, and left MCC, that might be associated with emotion, cognition, and memory disorders functions in PP-PTSD patients.

Mesenchymal Stem Cells Promote an Increase in Neuronal Oscillation via Glutamate Tonic Release

Mesenchymal stromal cells (MSCs) hold therapeutic potential for neurological disorders, but their impact on neuronal activity remains unclear. We investigated the effects of SB623 cells (Notch-1 intracellular domain-transfected MSCs) and parental MSCs on human induced pluripotent stem cell (iPSC)-derived neurons using multi-electrode arrays. SB623 cells significantly increased neuronal activity and oscillation in a dose-dependent manner, surpassing astrocytes in promoting network bursts. Strikingly, glutamatergic neurons showed a rapid increase in activity and bursts compared to GABAergic neurons, suggesting glutamate release from SB623 cells. We confirmed this by finding high glutamate levels in SB623 cell conditioned medium, which were reduced by glutaminase inhibition. Glutamate release was further implicated by the reduced excitability in co-cultures with astrocytes, known glutamate scavengers. Our findings reveal a novel mechanism for MSCs: promoting neuronal activity and network formation through tonic glutamate release, with potential implications for MSC-based therapies.

Large field-of-view metabolic profiling of murine brain tissue following morphine incubation using label-free multiphoton microscopy

BACKGROUND

Although the effects on neural activation and glucose consumption caused by opiates such as morphine are known, the metabolic machinery underlying opioid use and misuse is not fully explored. Multiphoton microscopy (MPM) techniques have been developed for optical imaging at high spatial resolution. Despite the increased use of MPM for neural imaging, the use of intrinsic optical contrast has seen minimal use in neuroscience.

NEW METHOD

We present a label-free, multimodal microscopy technique for metabolic profiling of murine brain tissue following incubation with morphine sulfate (MSO4). We evaluate two- and three-photon excited autofluorescence, and second and third harmonic generation to determine meaningful intrinsic contrast mechanisms in brain tissue using simultaneous label-free, autofluorescence multi-harmonic (SLAM) microscopy.

RESULTS

Regional differences quantified in the cortex, caudate, and thalamus of the brain demonstrate region-specific changes to metabolic profiles measured from FAD intensity, along with brain-wide quantification. While the overall intensity of FAD signal significantly decreased after morphine incubation, this metabolic molecule accumulated near the nucleus accumbens.

COMPARISON WITH EXISTING METHODS

Histopathology requires tissue fixation and staining to determine cell type and morphology, lacking information about cellular metabolism. Tools such as fMRI or PET imaging have been widely used, but lack cellular resolution. SLAM microscopy obviates the need for tissue preparation, permitting immediate use and imaging of tissue with subcellular resolution in its native environment.

CONCLUSIONS

This study demonstrates the utility of SLAM microscopy for label-free investigations of neural metabolism, especially the intensity changes in FAD autofluorescence and structural morphology from third-harmonic generation.

fMRI-based spatio-temporal parcellations of the human brain

PURPOSE OF REVIEW

Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research.

RECENT FINDINGS

Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth".

SUMMARY

While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.

Language and gesture neural correlates: A meta-analysis of functional magnetic resonance imaging studies

BACKGROUND

Humans often use co-speech gestures to promote effective communication. Attention has been paid to the cortical areas engaged in the processing of co-speech gestures.

AIMS

To investigate the neural network underpinned in the processing of co-speech gestures and to observe whether there is a relationship between areas involved in language and gesture processing.

METHODS & PROCEDURES

We planned to include studies with neurotypical and/or stroke participants who underwent a bimodal task (i.e., processing of co-speech gestures with relative speech) and a unimodal task (i.e., speech or gesture alone) during a functional magnetic resonance imaging (fMRI) session. After a database search, abstract and full-text screening were conducted. Qualitative and quantitative data were extracted, and a meta-analysis was performed with the software GingerALE 3.0.2, performing contrast analyses of uni- and bimodal tasks.

MAIN CONTRIBUTION

The database search produced 1024 records. After the screening process, 27 studies were included in the review. Data from 15 studies were quantitatively analysed through meta-analysis. Meta-analysis found three clusters with a significant activation of the left middle frontal gyrus and inferior frontal gyrus, and bilateral middle occipital gyrus and inferior temporal gyrus.

CONCLUSIONS

There is a close link at the neural level for the semantic processing of auditory and visual information during communication. These findings encourage the integration of the use of co-speech gestures during aphasia treatment as a strategy to foster the possibility to communicate effectively for people with aphasia.

WHAT THIS PAPER ADDS

What is already known on this subject Gestures are an integral part of human communication, and they may have a relationship at neural level with speech processing. What this paper adds to the existing knowledge During processing of bi- and unimodal communication, areas related to semantic processing and multimodal processing are activated, suggesting that there is a close link between co-speech gestures and spoken language at a neural level. What are the potential or actual clinical implications of this work? Knowledge of the functions related to gesture and speech processing neural networks will allow for the adoption of model-based neurorehabilitation programs to foster recovery from aphasia by strengthening the specific functions of these brain networks.

Phytomedicine Fructus Aurantii-derived two absorbed compounds unlock antidepressant and prokinetic multi-functions via modulating 5-HT3/GHSR

ETHNOPHARMACOLOGICAL RELEVANCE

Fructus Aurantii (FA), a well-known phytomedicine, has been employed to evoke antidepressant and prokinetic multi-functions. Therein, systematically identifying bioactive components and the referred mechanism is essential for FA.

AIM OF THE STUDY

This study was planned to answer "2 W" (What and Why), such as which components and pathways contribute to FA's multi-functions. We aimed to identify bioactive compounds as the key for opening the lock of FA's multi-functions, and the molecule mechanisms are their naturally matched lock cylinder.

MATERIALS AND METHODS

The phytochemical content of FA extract was determined, and the compounds were identified in rats pretreated with FA using liquid chromatography with mass spectrometry (LC-MS). The contribution strategy was used to assess bioactive compounds' efficacy (doses = their content in FA) in model rats with the mechanism. The changes in functional brain regions were determined via 7.0 T functional magnetic resonance imaging-blood oxygen level-dependent (fMRI-BOLD).

RESULT

Eight phytochemicals' content was detected, and merely six components were identified in rats in vivo. Meranzin hydrate + hesperidin (MH), as the primary contributor of FA, exerted antidepressant and prokinetic effects (improvement of indexes for immobility time, gastric emptying, intestinal transit, CRH, ghrelin, ACTH, DA, NA, 5-HT, CORT, and 5-HT3) by regulating 5-HT3/Growth hormone secretagogue receptor (GHSR) pathway. These results were validated by 5-HT2A, 5-HT3, and GHSR receptor antagonists combined with molecule docking. MH restored the excessive BOLD activation of the left accumbens nucleus, left corpus callosum and hypothalamus preoptic region.

CONCLUSION

Absorbed MH accounts for FA's anti-depressant and prokinetic efficacy in acutely-stressed rats, primarily via 5-HT3/GHSR shared regulation.

High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response

PURPOSE

Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s.

METHODS

Thirty-five healthy volunteers underwent fPET with [18F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s.

RESULTS

Consistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks.

CONCLUSIONS

In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.

Neuroimaging studies of cannabidiol and potential neurobiological mechanisms relevant for alcohol use disorders: a systematic review

The underlying neurobiological mechanisms of cannabidiol's (CBD) management of alcohol use disorder (AUD) remains elusive.Aim We conducted a systematic review of neuroimaging literature investigating the effects of CBD on the brain in healthy participants. We then theorise the potential neurobiological mechanisms by which CBD may ameliorate various symptoms of AUD.Methods This review was conducted according to the PRISMA guidelines. Terms relating to CBD and neuroimaging were used to search original clinical research published in peer-reviewed journals.Results Of 767 studies identified by our search strategy, 16 studies satisfied our eligibility criteria. The results suggest that CBD modulates γ-Aminobutyric acid and glutamate signaling in the basal ganglia and dorso-medial prefrontal cortex. Furthermore, CBD regulates activity in regions associated with mesocorticolimbic reward pathways; salience, limbic and fronto-striatal networks which are implicated in reward anticipation; emotion regulation; salience processing; and executive functioning.Conclusion CBD appears to modulate neurotransmitter systems and functional connections in brain regions implicated in AUD, suggesting CBD may be used to manage AUD symptomatology.

Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet

Introduction

In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.

Methods

This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.

Results and discussion

To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

Regional GABA levels modulate abnormal resting-state network functional connectivity and cognitive impairment in multiple sclerosis

More evidence shows that changes in functional connectivity with regard to brain networks and neurometabolite levels correlated to cognitive impairment in multiple sclerosis. However, the neurological basis underlying the relationship among neurometabolite levels, functional connectivity, and cognitive impairment remains unclear. For this purpose, we used a combination of magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging to study gamma-aminobutyric acid and glutamate concentrations in the posterior cingulate cortex, medial prefrontal cortex and left hippocampus, and inter-network functional connectivity in 29 relapsing-remitting multiple sclerosis patients and 34 matched healthy controls. Neuropsychological tests were used to evaluate the cognitive function. We found that relapsing-remitting multiple sclerosis patients demonstrated significantly reduced gamma-aminobutyric acid and glutamate concentrations and aberrant functional connectivity involving cognitive-related networks compared to healthy controls, and both alterations were associated with specific cognition decline. Moreover, mediation analyses indicated that decremented hippocampus gamma-aminobutyric acid levels in relapsing-remitting multiple sclerosis patients mediated the association between inter-network functional connectivity in various components of default mode network and verbal memory deficits. In summary, our findings shed new lights on the essential function of GABAergic system abnormalities in regulating network dysconnectivity and functional connectivity in relapsing-remitting multiple sclerosis patients, suggesting potential novel approach to treatment.

Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease

Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE ϵ4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.

Addiction-related brain networks identification via Graph Diffusion Reconstruction Network

Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph Diffusion Reconstruction Network (GDRN), a novel framework designed to capture addiction-related brain connectivity from fMRI data acquired from addicted rats. The proposed GDRN incorporates a diffusion reconstruction module that effectively maintains the unity of data distribution by reconstructing the training samples, thereby enhancing the model's ability to reconstruct nicotine addiction-related brain networks. Experimental evaluations conducted on a nicotine addiction rat dataset demonstrate that the proposed GDRN effectively explores nicotine addiction-related brain connectivity. The findings suggest that the GDRN holds promise for uncovering and understanding the complex neural mechanisms underlying addiction using fMRI data.

Exploring Functional Connectivity in Chronic Spinal Cord Injury Patients With Neuropathic Pain Versus Without Neuropathic Pain

The great majority of spinal cord injury (SCI) patients have debilitating chronic pain. Despite decades of research, these pain pathways of neuropathic pain (NP) are unknown. SCI patients have been shown to have abnormal brain pain pathways. We hypothesize that SCI NP patients' pain matrix is altered compared to SCI patients without NP. This study examines the functional connectivity (FC) in SCI patients with moderate-severe chronic NP compared to SCI patients with mild-no NP. These groups were compared to control subjects. The Neuropathic Pain Questionnaire and neurological evaluation based on the International Standard Neurological Classification of SCI were utilized to define the severity and level of injury. Of the 10 SCI patients, 7 (48.6 ± 17.02 years old, 6 male and 1 female) indicated that they had NP and 3 did not have NP (39.33 ± 8.08 years old, 2 male and 1 female). Ten uninjured neurologically intact participants were used as controls (24.8 ± 4.61 years old, 5 male and 5 female). FC metrics were obtained from the comparisons of resting-state functional magnetic resonance imaging among our various groups (controls, SCI with NP, and SCI without NP). For each comparison, a region-of-interest (ROI)-to-ROI connectivity analysis was pursued, encompassing a total of 175 ROIs based on a customized atlas derived from the AAL3 atlas. The analysis accounted for covariates such as age and sex. To correct for multiple comparisons, a strict Bonferroni correction was applied with a significance level of p < 0.05/NROIs. When comparing SCI patients with moderate-to-severe pain to those with mild-to-no pain, specific thalamic nuclei had altered connections. These nuclei included: medial pulvinar; lateral pulvinar; medial geniculate nucleus; lateral geniculate nucleus; and mediodorsal magnocellular nucleus. There was increased FC between the lateral geniculate nucleus and the anteroventral nucleus in NP post-SCI. Our analysis additionally highlights the relationships between the frontal lobe and temporal lobe with pain. This study successfully identifies thalamic neuroplastic changes that occur in patients with SCI who develop NP. It additionally underscores the pain matrix and involvement of the frontal and temporal lobes as well. Our findings complement that the development of NP post-SCI involves cognitive, emotional, and behavioral influences.

Pain-preferential thalamocortical neural dynamics across species

Searching for pain-preferential neural activity is essential for understanding and managing pain. Here, we investigated the preferential role of thalamocortical neural dynamics in encoding pain using human neuroimaging and rat electrophysiology across three studies. In study 1, we found that painful stimuli preferentially activated the medial-dorsal (MD) thalamic nucleus and its functional connectivity with the dorsal anterior cingulate cortex (dACC) and insula in two human functional magnetic resonance imaging (fMRI) datasets (n = 399 and n = 25). In study 2, human fMRI and electroencephalography fusion analyses (n = 220) revealed that pain-preferential MD responses were identified 89-295 ms after painful stimuli. In study 3, rat electrophysiology further showed that painful stimuli preferentially activated MD neurons and MD-ACC connectivity. These converging cross-species findings provided evidence for pain-preferential thalamocortical neural dynamics, which could guide future pain evaluation and management strategies.

Functional Brain Regions Linked to Tinnitus Pathology and Compensation During Task Performance: A Systematic Review

OBJECTIVE

To systematically search the literature and organize relevant advancements in the connection between tinnitus and the activity of different functional brain regions using functional magnetic resonance imaging (fMRI).

DATA SOURCES

MEDLINE (OVID), EMBASE (OVID), CINAHL (EBSCO), Web of Science, ProQuest Dissertations & Theses Global, Cochrane Database of Systematic Reviews, and PROSPERO from inception to April 2022.

REVIEW METHODS

Studies with adult human subjects who suffer from tinnitus and underwent fMRI to relate specific regions of interest to tinnitus pathology or compensation were included. In addition, fMRI had to be performed with a paradigm of stimuli that would stimulate auditory brain activity. Exclusion criteria included non-English studies, animal studies, and studies that utilized a resting state magnetic resonance imaging or other imaging modalities.

RESULTS

The auditory cortex may work to dampen the effects of central gain. Results from different studies show variable changes in the Heschl's gyrus (HG), with some showing increased activity and others showing inhibition and volume loss. After controlling for hyperacusis and other confounders, tinnitus does not seem to influence the inferior colliculus (IC) activation. However, there is decreased connectivity between the auditory cortex and IC. The cochlear nucleus (CN) generally shows increased activation in tinnitus patients. fMRI evidence indicates significant inhibition of thalamic gating. Activating the thalamus may be of important therapeutic potential.

CONCLUSION

Patients with tinnitus have significantly altered neuronal firing patterns, especially within the auditory network, when compared to individuals without tinnitus. Tinnitus and hyperacusis commonly coexist, making differentiation of the effects of these 2 phenomena frequently difficult.

The Effects of Transcranial Direct Current Stimulation in Patients with Mild Cognitive Impairment

Transcranial direct current stimulation (tDCS) came into consideration in recent years as a promising, non-invasive form of neuromodulation for individuals suffering from mild cognitive impairment (MCI). MCI represents a transitional stage between normal cognitive aging and more severe cognitive decline, which appears in neurodegenerative diseases, such as Alzheimer's disease. Numerous studies have shown that tDCS can have several useful effects in patients with MCI. It is believed to enhance cognitive functions, including memory and attention, potentially slowing down the progression of neurodegeneration and cognitive decline. tDCS is believed to work by modulating neuronal activity and promoting synaptic plasticity in the brain regions associated with cognition. Moreover, tDCS is generally considered safe and well-tolerated, making it an attractive option for long-term therapeutic use in MCI. However, further research is needed to determine the optimal stimulation parameters and long-term effects of tDCS in this population, as well as its potential to serve as a complementary therapy alongside other interventions for MCI. In this review, we included 16 randomized clinical trials containing patients with MCI who were treated with tDCS. We aim to provide important evidence for the cognitive enhancement using tDCS in patients with MCI, summarizing the effects and conclusions found in several clinical trials, and discuss its main mechanisms.

Mechanisms of brain activation following naming therapy in aphasia: A systematic review on task-based fMRI studies

The pattern of brain neuroplasticity after naming therapies in patients with aphasia can be evaluated using task-based fMRI. This article aims to review studies investigating brain reorganization after semantic and phonological-based anomia therapy that used picture-naming fMRI tasks. We searched for those articles that compared the activation of brain areas before and after aphasia therapies in the PubMed and the EMBASE databases from 1993 up to April 2020. All studies (single-cases or group designs) on anomia treatment in individuals with acquired aphasia were reviewed. Data were synthesized descriptively through tables to allow the facilitated comparison of the studies. A total of 14 studies were selected and reviewed. The results of the reviewed studies demonstrated that the naming improvement is associated with changes in the activation of cortical and subcortical brain areas. This review highlights the need for a more systematic investigation of the association between decreased and increased activation of brain areas related to anomia therapy. Also, more detailed information about factors influencing brain reorganization is required to elucidate the neural mechanisms of anomia therapy. Overall, regarding the theoretical and clinical aspects, the number of studies that used intensive protocol is growing, and based on the positive potential of these treatments, they could be suitable for the rehabilitation of people with aphasia.

Distortion correction of functional MRI without reverse phase encoding scans or field maps

Functional magnetic resonance images (fMRI) acquired using echo planar sequences typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may cause geometric mismatch with structural images and affect subsequent quantification and localization of brain function. State-of-the art distortion correction methods (for example, using FSL's topup or AFNI's 3dQwarp algorithms) require the collection of additional scans - either field maps or images with reverse phase encoding directions (i.e., blip-up/blip-down acquisitions) - to estimate and correct distortions. However, not all imaging protocols acquire these additional data and thus cannot take advantage of these post-acquisition corrections. In this study, we aim to enable state-of-the art processing of historical or limited datasets that do not include specific sequences for distortion correction by using only the acquired functional data and a single commonly acquired structural image. To achieve this, we synthesize an undistorted image with contrast similar to the fMRI data and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach, named SynBOLD-DisCo (Synthetic BOLD contrast for Distortion Correction), and show that this distortion correction process yields fMRI data that are geometrically similar to non-distorted structural images, with distortion correction virtually equivalent to acquisitions that do contain both blip-up/blip-down images. Our method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation and integration into existing fMRI preprocessing pipelines.

Rapid neural changes during novel speech-sound learning: An fMRI and DTI study

While the functional and microstructural changes that occur when we learn new language skills are well documented, relatively little is known about the time course of these changes. Here a combined functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) study that tracks neural change over three days of learning Arabic phonetic categorization as a new language (L-training) is presented. Twenty adult native English-speaking (L-native) participants are scanned before and after training to perceive and produce L-training phonetic contrasts for one hour on three consecutive days. A third (Chinese) language is used as a control language (L-control). Behavioral results show significant performance improvement for L-training in both learnt tasks; the perception and production task. Imaging analysis reveals that, training-related hemodynamic fMRI signal and fractional anisotropy (FA) value increasing can be observed, in the left inferior frontal gyrus (LIFG) and positively correlated with behavioral improvement. Moreover, post training functional connectivity findings show a significant increasing between LIFG and left inferior parietal lobule for L-training. These results indicate that three hours of phonetic categorization learning causes functional and microstructural changes that are typically associated with much more long-term learning.

A generic framework for embedding human brain function with temporally correlated autoencoder

Learning an effective and compact representation of human brain function from high-dimensional fMRI data is crucial for studying the brain's functional organization. Traditional representation methods such as independent component analysis (ICA) and sparse dictionary learning (SDL) mainly rely on matrix decomposition which represents the brain function as spatial brain networks and the corresponding temporal patterns. The correspondence of those brain networks across individuals are built by viewing them as one-hot vectors and then performing the matching. However, those one-hot vectors do not encode the regularity and/or variability of different brains very well, and thus are limited in effectively representing the functional brain activities across individuals and among different time points. To address this problem, in this paper, we formulate the human brain functional representation as an embedding problem, and propose a novel embedding framework based on the Transformer model to encode the brain function in a compact, stereotyped and comparable latent space where the brain activities are represented as dense embedding vectors. We evaluate the proposed embedding framework on the publicly available Human Connectome Project (HCP) task fMRI dataset. The experiments on brain state prediction task indicate the effectiveness and generalizability of the learned embedding. We also explore the interpretability of the learned embedding from both spatial and temporal perspective. In general, our approach provides novel insights on representing the regularity and variability of human brain function in a general, comparable, and stereotyped latent space.

Causal inference: relating language to event representations and events in the world

Events are not isolated but rather linked to one another in various dimensions. In language processing, various sources of information-including real-world knowledge, (representations of) current linguistic input and non-linguistic visual context-help establish causal connections between events. In this review, we discuss causal inference in relation to events and event knowledge as one aspect of world knowledge, and their representations in language comprehension. To evaluate the mechanism and time course of causal inference, we gather insights from studies on (1) implicit causality/consequentiality as a specific form of causal inference regarding the protagonists of cause/consequence events, and (2) the processing of causal relations. We highlight the importance of methodology in measuring causal inference, compare the results from different research methods, and emphasize the contribution of the visual-world paradigm to achieve a better understanding of causal inference. We recommend that further investigations of causal inference consider temporally sensitive measures and more detailed contexts.

The role of the insula in chronic pain following spinal cord injury: A resting-state fMRI study

BACKGROUND AND PURPOSE

Spinal cord injury (SCI) results in the loss of motor and sensory function from disconnections between efferent and afferent pathways. Most SCI patients are affected with chronic neuropathic pain, but there is a paucity of data concerning neuroplastic changes following SCI. Chronic pain disrupts default networks and is associated with abnormal insular connectivity. The posterior insula (PI) is associated with the degree of pain and intensity of pain. The anterior insula (AI) is related to signal changes. Comprehension of SCI pain mechanisms is essential to elucidate effective treatment options.

METHODS

This study examines the insular gyri functional connectivity (FC) of seven (five male, two female) SCI participants with moderate-severe chronic pain compared to 10 (five male, five female) healthy controls (HC). All subjects had 3-Tesla MRI performed and resting-state functional MRI (fMRI) was acquired. FC metrics were obtained from the comparisons of resting-state fMRI among our various groups. A seed-to-voxel analysis was pursued, encompassing six gyri of the insula. For multiple comparisons, a correction was applied with a significance level of p < .05.

RESULTS

There were significant differences in FC of the insula between SCI participants with chronic pain compared with HC. In the SCI participants, there was hyperconnectivity of the AI and PI to the frontal pole. In addition, there was increased FC noted between the PI and the anterior cingulate cortex. Hyperconnectivity was also observed between the AI and the occipital cortex.

CONCLUSIONS

These findings illustrate that there is a complex hyperconnectivity and modulation of pain pathways after traumatic SCI.