Most recent paper

Neuroscience. 2024 Dec 7:S0306-4522(24)00713-9. doi: 10.1016/j.neuroscience.2024.12.012. Online ahead of print.

ABSTRACT

AIMS: This study was aimed to investigate frequency-specific LFO changes and their correlation with gene pathways in PACG using transcriptome-neuroimaging analysis.

METHODS: Resting-state fMRI (Rs-fMRI) data were acquired from individuals with PACG and healthy controls for evaluating the amplitude of low-frequency oscillations (ALFF) across different frequency bands such as the full band, slow-4 band, and slow-5 band. Transcriptome analysis integrated information from the Allen Human Brain Atlas (AHBA) through gene set enrichment analysis, protein-protein interaction network construction, and specific expression analysis, aiming to clarify the link between ALFF patterns and gene expression profiles in PACG. Statistical analyses, including one-sample t-tests and two-sample t-tests, were used to assess ALFF differences between groups, while partial least squares (PLS) regression was applied to explore the associations between ALFF and transcriptome data.

RESULTS: This study identifies significant variations in ALFF values in PACG patients, observed consistently across multiple frequency bands, including slow-4 and slow-5. Enrichment analysis indicates that these genes are primarily involved in cellular components such as the cytosol and cytoplasm, molecular functions like protein binding, and key pathways, including metabolic and circadian rhythms, epithelial signaling in Helicobacter pylori infection, and glutathione metabolism. Protein-protein interaction (PPI) analysis further underscores the role of PACG-related genes in forming a functional network, highlighting hub genes critical for various biological processes.

CONCLUSION: This study establishes a connection between the molecular mechanisms of PACG and alterations in brain function and gene expression, providing valuable perspectives on the fundamental processes impacting low-frequency oscillations in PACG.

PMID:39653245 | DOI:10.1016/j.neuroscience.2024.12.012

bioRxiv [Preprint]. 2024 Nov 28:2024.11.28.625914. doi: 10.1101/2024.11.28.625914.

ABSTRACT

OBJECTIVE: Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time.

METHODS: We developed a model that leverages the vision transformer (ViT) as its backbone, jointly encoding spatial and temporal information from fMRI inputs using two different configurations: space-time and sequential encoders. The model generates 4D brain network maps that evolve over time, capturing dynamic changes in both spatial and temporal dimensions. In the absence of ground-truth data, we used spatially constrained windowed independent component analysis (ICA) components derived from fMRI data as weak supervision to guide the training process. The model was evaluated using large-scale resting-state fMRI datasets, and statistical analyses were conducted to assess the effectiveness of the generated dynamic maps using various metrics.

RESULTS: Our model effectively produced 4D brain maps that captured both inter-subject and temporal variations, offering a dynamic representation of evolving brain networks. Notably, the model demonstrated the ability to produce smooth maps from noisy priors, effectively denoising the resulting brain dynamics. Additionally, statistically significant differences were observed in the temporally averaged brain maps, as well as in the summation of absolute temporal gradient maps, between patients with schizophrenia and healthy controls. For example, within the Default Mode Network (DMN), significant differences emerged in the temporally averaged space-time configurations, particularly in the thalamus, where healthy controls exhibited higher activity levels compared to subjects with schizophrenia. These findings highlight the model's potential for differentiating between clinical populations.

CONCLUSION: The proposed spatiotemporal dense prediction model offers an effective approach for generating dynamic brain maps by capturing significant spatiotemporal variations in brain activity. Leveraging weak supervision through ICA components enables the model to learn dynamic patterns without direct ground-truth data, making it a robust and efficient tool for brain mapping.

SIGNIFICANCE: This work presents an important new approach for dynamic brain mapping, potentially opening up new opportunities for studying brain dynamics within specific networks. By framing the problem as a spatiotemporal dense prediction task in computer vision, we leverage the spatiotemporal ViT architecture combined with weakly supervised learning techniques to efficiently and effectively estimate these maps.

PMID:39651175 | PMC:PMC11623695 | DOI:10.1101/2024.11.28.625914

PeerJ Comput Sci. 2024 Oct 16;10:e2302. doi: 10.7717/peerj-cs.2302. eCollection 2024.

ABSTRACT

Alzheimer's disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer's disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer's in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer's disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer's disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.

PMID:39650470 | PMC:PMC11622848 | DOI:10.7717/peerj-cs.2302

PeerJ Comput Sci. 2024 Nov 28;10:e2502. doi: 10.7717/peerj-cs.2502. eCollection 2024.

ABSTRACT

Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimer's disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis. In this study, a unique feature extraction technique based on normalized group activations that can be applied to both structural MRI and resting-state-fMRI (rs-fMRI) is proposed. This method is split into two phases: multi-trait condensed feature extraction networks and regional association networks. The initial phase involves extracting features from various brain regions using different multi-layered convolutional networks. Then, multiple regional association networks with normalized group activations for all the regional pairs are trained and the output of these networks is given as input to a classifier. To provide an unbiased estimate, an automated diagnosis system equipped with the proposed feature extraction is designed and analyzed on multi-cohort Alzheimer's Disease Neuroimaging Initiative (ADNI) data to predict multi-stages of AD. This system is also trained/tested on heterogeneous features such as non-transformed features, curvelets, wavelets, shearlets, textures, and scattering operators. Baseline scans of 185 rs-fMRIs and 1442 MRIs from ADNI-1, ADNI-2, and ADNI-GO datasets are used for validation. For MCI (mild cognitive impairment) classifications, there is an increase of 1-4% in performance. The outcome demonstrates the good discriminatory behaviour of the proposed features and its efficiency on rs-fMRI time-series and MRI data to classify multiple stages of AD.

PMID:39650458 | PMC:PMC11622987 | DOI:10.7717/peerj-cs.2502

Dyslexia. 2025 Feb;31(1):e1798. doi: 10.1002/dys.1798.

ABSTRACT

The expanded Simple View of Reading model suggests language processing and word reading as contributors to reading comprehension and points at the participation of executive functions as supporting these abilities. Switching and inhibition are both executive functions (EF) contributing to reading, especially in languages with two writing systems-shallow and deep, such as Hebrew. Here, we aimed to determine the specific role of switching/inhibition both cognitively and neurobiologically in the Simple View of Reading model among 49 eight- to 12-year-old Hebrew-speaking children. Children underwent reading and cognitive behavioural testing as well as a five-min resting-state fMRI scan. Functional connectivity of the fronto-parietal network related to switching/inhibition was determined and included in a moderation model. Results suggest that both switching/inhibition abilities and functional connectivity within the fronto-parietal network moderate the relations between word reading and reading comprehension. This strengthens the contribution of switching/inhibition to facilitating reading comprehension and supports the need to include it as part of the expanded SVR model.

PMID:39648984 | DOI:10.1002/dys.1798

CNS Neurosci Ther. 2024 Dec;30(12):e70120. doi: 10.1111/cns.70120.

ABSTRACT

INTRODUCTION: The centromedian nucleus (CM) of the thalamus is essential for arousal, attention, sensory processing, and motor control. Neuromodulation targeting CM dysfunction has shown efficacy in various neurological disorders. However, its individualized precise transcranial magnetic stimulation (TMS) remains unreported. Using resting-state functional MRI, we mapped CM-based functional connectivity (CM-FC) to develop a personalized TMS scheme for neurological conditions.

METHODS: We first analyzed the CM-FC patterns of healthy subjects via 10 scanning sessions in three MRI scanners spanning two subject groups: one from the Human Connectome Project (n = 20, four sessions) dataset and the other from Hangzhou Normal University (n = 20, three sessions of 3 T MRI and three sessions of 1.5 T MRI). Pearson's correlation was used for CM-FC evaluation. Then, we proposed an overlapping index ranging from 1 to 10, and group-level clusters with the highest overlapping index located 4 cm beneath the scalp were identified. In the individual CM-FC map, watershed image segmentation was used to obtain an individual cluster. The peak voxel with the highest FC value within the individual cluster was defined as a potential individualized target for future TMS.

RESULTS: The spatial FC patterns were remarkably similar between the left and right CMs. CMs have widespread positive connectivity with cortical areas, including the sensorimotor cortex, supplementary motor area, middle frontal cortex, medial temporal cortex, and middle cingulate. Among the group-level FC patterns of the left and right CMs, only the left CM had a group cluster in the left primary sensorimotor cortex (PSMC, cluster size = 51) with an overlapping index of 10, that is, 10 sessions showed significant CM-FC.

CONCLUSIONS: The left PSMC exhibited reproducible FC with the left CM. The individual peak FC location in the left PSMC could be used as a TMS target for indirect modulation of CM activity and aid in the treatment of CM-related neurological disorders.

PMID:39648650 | DOI:10.1111/cns.70120

Neural Netw. 2024 Nov 29;183:106945. doi: 10.1016/j.neunet.2024.106945. Online ahead of print.

ABSTRACT

Functional connectivity (FC), derived from resting-state functional magnetic resonance imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC is usually defined as a correlation matrix that is a symmetric positive definite (SPD) matrix lying on the Riemannian manifold. Recently, a number of learning-based methods have been proposed for FC analysis, while the geometric properties of Riemannian manifold have not yet been fully explored in previous studies. Also, most existing methods are designed to target one imaging site of fMRI data, which may result in limited training data for learning reliable and robust models. In this paper, we propose a novel Riemannian Manifold-based Disentangled Representation Learning (RM-DRL) framework which is capable of learning invariant representations from fMRI data across multiple sites for brain disorder diagnosis. In RM-DRL, we first employ an SPD-based encoder module to learn a latent unified representation of FC from different sites, which can preserve the Riemannian geometry of the SPD matrices. In latent space, a disentangled representation module is then designed to split the learned features into domain-specific and domain-invariant parts, respectively. Finally, a decoder module is introduced to ensure that sufficient information can be preserved during disentanglement learning. These designs allow us to introduce four types of training objectives to improve the disentanglement learning. Our RM-DRL method is evaluated on the public multi-site ABIDE dataset, showing superior performance compared with several state-of-the-art methods.

PMID:39642641 | DOI:10.1016/j.neunet.2024.106945

Neuroimage. 2024 Dec 4:120964. doi: 10.1016/j.neuroimage.2024.120964. Online ahead of print.

ABSTRACT

The constantly evolving world necessitates a brain that can swiftly adapt and respond to rapid changes. The brain, conceptualized as a system performing cognitive functions through collective neural activity, has been shown to maintain a resting state characterized by near-critical neural dynamics, positioning it to effectively respond to external stimuli. However, how near-criticality is dynamically modulated during task performance remains insufficiently understood. In this study, we utilized the prototypical Ising Hamiltonian model to investigate the modulation of near-criticality in neural activity at the cortical subsystem level during perceptual tasks. Specifically, we simulated 2D-Ising models in silico using structural MRI data and empirically estimated the system's state in vivo using functional MRI data. We first replicated previous findings that the resting state is typically near-critical as captured by the Ising model. Importantly, we observed heterogeneous changes in criticality across cortical subsystems during a naturalistic movie-watching task, with visual and auditory regions fine-tuned closer to criticality. A more fine-grained analysis of the ventral temporal cortex during an object recognition task further revealed that only regions selectively responsive to a specific object category were tuned closer to criticality when processing that object category. In conclusion, our study provides empirical evidence from the domain of perception supporting the cognitive critical brain hypothesis that modulating the criticality of subsystems within the brain's hierarchical and modular organization may be a fundamental mechanism for achieving diverse cognitive functions.

PMID:39643023 | DOI:10.1016/j.neuroimage.2024.120964

Neuroimage. 2024 Dec 3:120962. doi: 10.1016/j.neuroimage.2024.120962. Online ahead of print.

ABSTRACT

Aesthetic experiences are characterized by a conscious, emotionally and hedonically rewarding perceptions of a stimulus's aesthetic qualities and are thought to arise from a unique combination of cognitive and affective processes. To pinpoint neural correlates of aesthetic experiences, in the present study, we performed a series of meta-analyses based on the existing functional Magnetic Resonance Imaging (fMRI) studies of art appreciation in visual art (34 experiments, 692 participants) and music (34 experiments, 718 participants). The Activation Likelihood Estimation (ALE) analyses showed that the frontal pole (FP), ventromedial prefrontal cortex (vmPFC), and inferior frontal gyrus (IFG) were commonly activated in visual-art-induced aesthetic experiences, whilst bilateral superior temporal gyrus (STG) and striatal areas were commonly activated in music appreciation. Additionally, task-independent Resting-state Functional Connectivity (RSFC), task-dependent Meta-analytical Connectivity Modelling (MACM) analyses, as well as Activation Network Modeling (ANM) further showed that visual art and music engaged quite distinct brain networks. Our findings support the domain-specific view of aesthetic appreciation and challenge the notion that there is a general "common neural currency" for aesthetic experiences across domains.

PMID:39638082 | DOI:10.1016/j.neuroimage.2024.120962

Neuroimage. 2024 Dec 3:120963. doi: 10.1016/j.neuroimage.2024.120963. Online ahead of print.

ABSTRACT

BACKGROUND: Depression alleviation following treatment with repetitive transcranial magnetic stimulation (rTMS) tends to be more effective when TMS is targeted to cortical areas with high (negative) resting state functional connectivity (rsFC) with the subgenual anterior cingulate cortex (sgACC). However, the relationship between sgACC-cortex rsFC and the TMS-evoked response in the sgACC is still being explored and has not yet been established in depressed patients.

OBJECTIVES: In this study, we investigated the relationship between sgACC-cortical (site of stimulation) rsFC and induced evoked responses in the sgACC in healthy controls and depressed patients.

METHODS: For each participant (N=115, 34 depressed patients), a peak rsFC cortical 'hotspot' for the sgACC and control targets were identified at baseline. Single pulses of TMS interleaved with fMRI readouts were administered to these targets to evoke downstream fMRI blood-oxygen-level-dependent (BOLD) responses in the sgACC. Generalized estimating equations were used to investigate the association between TMS-evoked BOLD responses in the sgACC and rsFC between the stimulation site and the sgACC.

RESULTS: Stimulations over cortical sites with high rsFC to the sgACC were effective in modulating activity in the sgACC in both healthy controls and depressed patients. Moreover, we found that in depressed patients, sgACC rsFC at the site of stimulation was associated with the induced evoked response amplitude in the sgACC: stronger positive rsFC values leading to stronger evoked responses in the sgACC.

CONCLUSIONS: rsFC-based targeting is a viable strategy to causally modulate the sgACC. Assuming an anti-depressive mechanism working through modulation of the sgACC, the field's exclusive focus on sites anticorrelated with the sgACC for treating depression should be broadened to explore positively-connected sites.

PMID:39638081 | DOI:10.1016/j.neuroimage.2024.120963

Neuroimage Clin. 2024 Nov 30;45:103719. doi: 10.1016/j.nicl.2024.103719. Online ahead of print.

ABSTRACT

INTRODUCTION: Alzheimer's disease (AD), the most prevalent age-related dementia, leads to significant cognitive decline. While genetic risk factors and neuroimaging biomarkers have been extensively studied, establishing a neuroimaging-based metric to assess AD risk has received less attention. This study introduces the Brain-wide Risk Score (BRS), a novel approach using multimodal neuroimaging data to assess the risk of mild cognitive impairment (MCI), a precursor to AD.

METHODS: Participants from the OASIS-3 cohort (N = 1,389) were categorized into control (CN) and MCI groups. Structural MRI (sMRI) data provided gray matter (GM) segmentation maps, while resting-state functional MRI (fMRI) data yielded functional network connectivity (FNC) matrices via spatially constrained independent component analysis. Similar imaging features were computed from the UK Biobank (N = 37,780). The BRS was calculated by comparing each participant's neuroimaging features to the difference between average features of CN and MCI groups. Both GM and FNC features were used. The BRS effectively differentiated CN from MCI individuals within OASIS-3 and in an independent dataset from the ADNI cohort (N = 729), demonstrating its ability to identify MCI risk.

RESULTS: Unimodal analysis revealed that sMRI provided greater differentiation than fMRI, consistent with prior research. Using the multimodal BRS, we identified two distinct groups: one with high MCI risk (negative GM and FNC BRS) and another with low MCI risk (positive GM and FNC BRS). Additionally, 46 UK Biobank participants diagnosed with AD showed FNC and GM patterns similar to the high-risk groups.

CONCLUSION: Validation using the ADNI dataset confirmed our results, highlighting the potential of FNC and sMRI-based BRS in early Alzheimer's detection.

PMID:39637673 | DOI:10.1016/j.nicl.2024.103719

Med Image Anal. 2024 Nov 29;101:103403. doi: 10.1016/j.media.2024.103403. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning. However, data augmentation strategies employed in these approaches may damage the original blood-oxygen-level-dependent (BOLD) signals, thus hindering subsequent fMRI feature extraction. In this paper, we propose a self-supervised graph contrastive learning framework with diffusion augmentation (GCDA) for functional MRI analysis. The GCDA consists of a pretext model and a task-specific model. In the pretext model, we first augment each brain functional connectivity network derived from fMRI through a graph diffusion augmentation (GDA) module, and then use two graph isomorphism networks with shared parameters to extract features in a self-supervised contrastive learning manner. The pretext model can be optimized without the need for labeled training data, while the GDA focuses on perturbing graph edges and nodes, thus preserving the integrity of original BOLD signals. The task-specific model involves fine-tuning the trained pretext model to adapt to downstream tasks. Experimental results on two rs-fMRI cohorts with a total of 1230 subjects demonstrate the effectiveness of our method compared with several state-of-the-arts.

PMID:39637557 | DOI:10.1016/j.media.2024.103403

Neurobiol Aging. 2024 Nov 27;147:12-21. doi: 10.1016/j.neurobiolaging.2024.11.008. Online ahead of print.

ABSTRACT

The locus coeruleus (LC) is among the first brain structures impacted by Alzheimer's disease (AD), and noradrenergic denervation may contribute to early neurovascular dysfunction in AD. Mechanistic links between the LC and cerebral perfusion have been demonstrated in rodents, but there have been no similar studies in aging humans. Community-dwelling older adults with no history of stroke or dementia (N=66) underwent structural (T1-MPRAGE; T1-FSE) and perfusion (resting pCASL) MRI. Plasma AD biomarkers levels were evaluated for Aβ42/40 ratio (n=56) and pTau181 (n=60). Higher rostral LC structural MRI contrast was associated with lower perfusion in entorhinal and limbic regions but higher perfusion in lateral and medial orbitofrontal cortices. Relationships between LC structure and regional cerebral perfusion were attenuated in older adults with higher plasma pTau levels and lower plasma Aβ42/40 ratios. Previously unstudied links between LC structure and cerebral perfusion are detectible in older adults using MRI and are attenuated in those showing greater AD pathophysiologic change, suggesting an uncoupling of LC-cerebral perfusion relationships in older adults with aggregating AD-related pathophysiology.

PMID:39637519 | DOI:10.1016/j.neurobiolaging.2024.11.008

J Neuroimaging. 2025 Jan-Feb;35(1):e13252. doi: 10.1111/jon.13252.

ABSTRACT

Multiple sclerosis-related cognitive impairment (MSrCI) affects most patients with multiple sclerosis (MS), significantly contributing to disability and socioeconomic challenges. MSrCI manifests across all disease stages, mainly impacting working memory, information processing, and attention. To date, the underlying mechanisms of MSrCI remain unclear, with its pathogenesis considered multifactorial. While conventional MRI findings correlate with MSrCI, there is no consensus on reliable imaging metrics to detect or diagnose cognitive impairment (CI). Functional MRI (fMRI) has provided unique insights into the brain's neuroplasticity mechanisms, revealing evidence of compensatory mechanisms in response to tissue damage, both beneficial and maladaptive. This review summarizes the current literature on the application of resting-state fMRI (rs-fMRI) and task-based fMRI (tb-fMRI) in understanding neuroplasticity and its relationship with cognitive changes in people with MS (pwMS). Searches of databases, including PubMed/Medline, Embase, Scopus, and the Web of Science, were conducted for the most recent fMRI cognitive studies in pwMS. Key findings ifrom rs-fMRI studies reveal disruptions in brain connectivity and hub integration, leading to CI due to decreased network efficiency. tb-fMRI studies highlight abnormal brain activation patterns in pwMS, with evidence of increased fMRI activity in earlier disease stages as a beneficial compensatory response, followed by reduced activation correlating with increased lesion burden and cognitive decline as the disease progresses. This suggests a gradual exhaustion of compensatory mechanisms over time. These findings support fMRI not only as a diagnostic tool for MSrCI but also as a potential imaging biomarker to improve our understanding of disease progression.

PMID:39636088 | DOI:10.1111/jon.13252

Neurosci Appl. 2024;3:104090. doi: 10.1016/j.nsa.2024.104090. Epub 2024 Sep 24.

ABSTRACT

Longitudinal studies using resting-state functional magnetic resonance imaging (rs-fMRI) focused on adolescent internalizing psychopathology are scarce and have mostly investigated standardized treatment effects on functional connectivity (FC) of the full amygdala. The role of amygdala subregions and large resting-state networks had yet to be elucidated, and treatment is in practice often personalized. Here, longitudinal FC development of amygdala subregions and whole-brain networks are investigated in a clinically representative sample. Treatment-naïve adolescents with clinical depression and comorbid anxiety who started care-as-usual (n = 23; INT) and healthy controls (n = 24; HC) participated in rs-fMRI scans and questionnaires at baseline (before treatment) and after three months. Changes between and within groups over time in FC of the laterobasal amygdala (LBA), centromedial amygdala (CMA) and whole-brain networks derived from independent component analysis (ICA) were investigated. Groups differed significantly in FC development of the right LBA to the postcentral gyrus and the left LBA to the frontal pole. Within INT, FC to the frontal pole and postcentral gyrus changed over time while changes in FC of the right LBA were also linked to symptom change. No significant interactions were observed when considering FC from CMA bilateral seeds or within ICA-derived networks. Results in this cohort suggest divergent longitudinal development of FC from bilateral LBA subregions in adolescents with internalizing disorders compared to healthy peers, possibly reflecting nonspecific treatment effects. Moreover, associations were found with symptom change. These results highlight the importance of differentiation of amygdala subregions in neuroimaging research in adolescents.

PMID:39634556 | PMC:PMC11615185 | DOI:10.1016/j.nsa.2024.104090

Sci Rep. 2024 Dec 3;14(1):30025. doi: 10.1038/s41598-024-81102-w.

ABSTRACT

Non-invasive neuromodulation represents a major opportunity for brain interventions, and transcranial focused ultrasound (FUS) is one of the most promising approaches. However, some challenges prevent the community from fully understanding its outcomes. We aimed to address one of them and unravel the temporal dynamics of FUS effects in humans. Twenty-two healthy volunteers participated in the study. Eleven received FUS in the right inferior frontal cortex while the other 11 were stimulated in the right thalamus. Using a temporal dynamic approach, we compared resting-state fMRI seed-based functional connectivity obtained before and after FUS. We also assessed behavioural changes as measured with a task of reactive motor inhibition. Our findings reveal that the effects of FUS are predominantly time-constrained and spatially distributed in brain regions functionally connected with the directly stimulated area. In addition, mediation analysis highlighted that FUS applied in the right inferior cortex was associated with behavioural alterations which was directly explained by the applied acoustic pressure and the brain functional connectivity change we observed. Our study underscored that the biological effects of FUS are indicative of behavioural changes observed more than an hour following stimulation and are directly related to the applied acoustic pressure.

PMID:39627315 | DOI:10.1038/s41598-024-81102-w

Brain Topogr. 2024 Dec 3;38(1):18. doi: 10.1007/s10548-024-01094-8.

ABSTRACT

Human beings exhibit varying risk-taking behaviors in response to different risk levels. Despite numerous studies on risk-taking in decision-making, the neural mechanisms of decision-making regarding risk levels remains unclear. To investigate the neural correlates of individual differences in risk-taking under different risk-levels, we analyzed behavioral data of the Balloon Analogue Risk Task (BART) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of healthy participants (22-39 years, N = 93) from the University of California, Los Angeles Consortium for Neuropsychiatric Phenomics dataset. In the BART, the participants decided to pump for more points or stop pumping to avoid explosion of the balloons, where the risk level was manipulated by the explosion likelihood which was distinguished by the balloon color (blue for low-, red for high- risk condition). Compared with low-risk condition, the participants pumped less number, exploded more balloons, and showed more variability in pump numbers in high-risk condition, demonstrating the effective manipulation of the risky level. Next, resting state features and functional connectivity (rsFC) strength were associated with behavioral measures in low- and high-risk conditions. We found that the explosion number of balloons were correlated with the low frequency fluctuations (ALFF) in the left dorsolateral prefrontal cortex (L. DLPFC), the rsFC strength between L. DLPFC and the left anterior orbital gyrus in the low-risk condition. In the high-risk condition, we found variability in pump numbers was correlated with the ALFF in the left middle/superior frontal gyrus, the fractional ALFF (fALFF) in the medial segment of precentral gyrus (M. PrG), and the rsFC strength between the M. PrG and bilateral precentral gyrus. Our results highlighted significance of the L. DLPFC in lower risky decision making and the precentral gyrus in higher risky decision making, suggesting that distinctive neural correlates underlie the individual differences of decision-making under different risk level.

PMID:39625684 | DOI:10.1007/s10548-024-01094-8

Brain Imaging Behav. 2024 Dec 3. doi: 10.1007/s11682-024-00959-7. Online ahead of print.

ABSTRACT

The study aimed to examine alterations in surface-based amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) in primary open-angle glaucoma (POAG) patients using resting-state functional magnetic resonance imaging (rs-fMRI), and to investigate their relationships with visual function and molecular profiling. A total of 70 POAG patients and 45 age- and sex-matched healthy controls (HCs) underwent rs-fMRI scans. The differences between POAG and HCs groups were compared by two-sample t-test. Spearman's correlation analyses assessed the relationship between ALFF/fALFF values and ophthalmic parameters. Spatial correlation analysis of the patients-control difference map with brain imaging data further explores underlying neurobiological mechanisms. POAG patients displayed altered brain activity compared to HCs, including decreased ALFF/fALFF in the visual network and increased in the frontoparietal and default mode networks. They exhibited reduced fALFF in the somatomotor network and increased ALFF in the dorsal and ventral attention networks. These changes are linked to neurotransmitter systems, with fALFF particularly associated with the dopamine system. Moreover, the altered ALFF/fALFF in brain regions related to vision and attention - the occipital lobe, temporal lobe, parietal lobe, paracentral lobule, and frontal lobe correlated with ophthalmic examination parameters. Surface-based ALFF/fALFF in POAG decreased in visual processing regions and increased in brain regions related to cognitive control, working memory, and attention. These changes were linked to neurotransmitter distributions important for emotional stability and mental health, potentially informing treatment approaches for POAG patients.

PMID:39625606 | DOI:10.1007/s11682-024-00959-7

Stress Health. 2024 Dec 3:e3515. doi: 10.1002/smi.3515. Online ahead of print.

ABSTRACT

Depression is one of the most common mental disorders. Perceived stress is a significant trigger and has adverse effects on depression. The complex longitudinal relationship between perceived stress and depression at the symptom level has significant implications for clinical intervention but is understudied. In our study, 823 students (67% female, median age 20.38, IQR 19.42-21.43) from a university in Tianjin were randomly sampled and completed measures of PHQ-9 and PSS-10, while 393 (65% female, median age 20.42, IQR 19.46-21.45) were followed up at three points, six months apart. The longitudinal relationships were estimated using cross-lagged modelling and cross-lagged panel network modelling. Among them, 49 students (59% female, median age 19.48, IQR 18.76-20.12) participated in resting-state functional magnetic resonance imaging (fMRI) scans. Cross-lagged analyses showed that depression and perceived stress predicted each other at the global level. At the dimensional level, depression and perceived helplessness were mutually predictive, while depression and perceived coping did not. In the cross-lagged panel network analyses, we identified symptoms in the top 20% of Bridge Expected Influence as bridging symptoms, specifically 'Guilt' (PHQ6) and 'Felt nervous and stressed' (PSS3). Notably, 'guilt' consistently demonstrated the highest Bridge Expected Influence across all time points and showed the strongest predictive power for perceived stress. We found that fALFF in the left superior frontal gyrus (SFG) mediated the association between "guilt" and perceived stress. Our findings elucidate the bidirectional relationship between symptoms of depression and perceived stress, identifying guilt is the most critical symptom of depression for the followed perceived stress, with SFG activity mediating this association.

PMID:39624971 | DOI:10.1002/smi.3515

Front Aging Neurosci. 2024 Nov 18;16:1467054. doi: 10.3389/fnagi.2024.1467054. eCollection 2024.

ABSTRACT

AIM: Mild cognitive impairment (MCI) includes two distinct subtypes, namely progressive MCI (pMCI) and stable MCI (sMCI). The objective of this study was to identify the topological reorganization of brain functional networks in patients with pMCI and sMCI.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was applied to patients with pMCI, sMCI and healthy controls. Graph theory was applied to study the topological characteristics of the brain's functional networks, examining global and nodal metrics, modularity, and rich-club organization. Analysis of covariance and two sample t-tests were applied to assess differences in topological attributes between patient groups, alongside correlation analysis, which examined the value of changing topological attributes in predicting various clinical outcomes.

RESULTS: Significant differences between each group with regard to network metrics were observed. These included clustering coefficients and small-worldness. At the nodal level, several nodes with an abnormal degree centrality and nodal efficiency were detected. In rich club, pMCI and sMCI patients showed declined connectivity compared with HC. Significant differences were observed in the intra- and inter-module connections among the three groups. Particularly noteworthy was the irreplaceable role of the cerebellar module in network interactions.

CONCLUSION: Our study revealed significant differences in network topological properties among sMCI, pMCI and HC patients, which were significantly correlated with cognitive function. Most notably, the cerebellar module played a crucial role in the overall network interactions. In conclusion, these findings could aid in the development of imaging markers used to expedite diagnosis and intervention prior to Alzheimer's disease onset.

PMID:39624168 | PMC:PMC11609165 | DOI:10.3389/fnagi.2024.1467054