Most recent paper
Individual differences in conditioned pain modulation are associated with functional connectivity within the descending antinociceptive pathway
Pain. 2024 Nov 19. doi: 10.1097/j.pain.0000000000003478. Online ahead of print.
ABSTRACT
The perception of pain and ability to cope with it varies widely amongst people, which in part could be due to the presence of inhibitory (antinociceptive) or facilitatory (pronociceptive) effects in conditioned pain modulation (CPM). This study examined whether individual differences in CPM reflect functional connectivity (FC) strengths within nodes of the descending antinociceptive pathway (DAP). A heat-based CPM paradigm and resting-state functional magnetic resonance imaging (rs-fMRI) were used to test the hypothesis that an individual's capacity to exhibit inhibitory CPM (changes in test stimuli [TS] pain due to a conditioning stimulus [CS]) reflects FC of the subgenual anterior cingulate cortex (sgACC), periaqueductal gray (PAG), and rostral ventromedial medulla (RVM). A total of 151 healthy participants (72 men, 79 women) underwent CPM testing and rs-fMRI. Three types of CPM were identified based on the effect of the CS on TS pain: (1) Antinociception: CS reduced TS pain in 45% of participants, (2) No-CPM: CS did not change TS pain in 15% of participants, and (3) Pronociception: CS increased TS pain in 40% of participants. Only the Antinociceptive subgroup exhibited FC between the left sgACC and PAG, right sgACC and PAG, and RVM and PAG. Furthermore, only the Antinociceptive subgroup exhibited a correlation of both left and right sgACC-RVM FC (medium effect sizes) with CPM effect magnitude. Women, compared with men were more likely to be categorized as pronociceptive. These data support the proposition that FC of the DAP reflects or contributes to inhibitory CPM.
PMID:39661368 | DOI:10.1097/j.pain.0000000000003478
Resting-State Functional MRI: Current State, Controversies, Limitations, and Future Directions-AJR Expert Panel Narrative Review
AJR Am J Roentgenol. 2024 Dec 11. doi: 10.2214/AJR.24.32163. Online ahead of print.
ABSTRACT
Resting-state functional MRI (rs-fMRI), a promising method for interrogating different brain functional networks from a single MRI acquisition, is increasingly utilized in clinical presurgical and other pretherapeutic brain mapping. However, challenges in standardization of acquisition, preprocessing, and analysis methods across centers, and variability in results interpretation, complicate its clinical use. Additionally, inherent problems regarding reliability of language lateralization, interpatient variability of cognitive network representation, dynamic aspects of intranetwork and internetwork connectivity, and effects of neurovascular uncoupling on network detection still must be overcome. Although deep-learning solutions and further methodologic standardization will help address these issues, rs-fMRI remains generally considered an adjunct to task-based fMRI (tb-fMRI) for clinical presurgical mapping. Nonetheless, in many clinical instances, rs-fMRI may offer valuable additional information that supplements tb-fMRI, especially if tb-fMRI is inadequate due to patient performance or other limitations. Future growth in clinical applications of rs-fMRI is anticipated as challenges are increasingly addressed. In this AJR Expert Panel Narrative Review, we summarize the current state and emerging clinical utility of rs-fMRI, focusing on its role in presurgical mapping. We present ongoing controversies and limitations in clinical applicability and discuss future directions including the developing role of rs-fMRI in neuromodulation treatment for various neurologic disorders.
PMID:39660823 | DOI:10.2214/AJR.24.32163
Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning
Heliyon. 2024 Nov 2;10(23):e37890. doi: 10.1016/j.heliyon.2024.e37890. eCollection 2024 Dec 15.
ABSTRACT
BACKGROUND: and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients.
METHODS: We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs.
RESULTS: All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937.
CONCLUSION: The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.
PMID:39660184 | PMC:PMC11629196 | DOI:10.1016/j.heliyon.2024.e37890
Abnormalities of cortical and subcortical spontaneous brain activity unveil mechanisms of disorders of consciousness and prognosis in patients with severe traumatic brain injury
Int J Clin Health Psychol. 2024 Oct-Dec;24(4):100528. doi: 10.1016/j.ijchp.2024.100528. Epub 2024 Nov 28.
ABSTRACT
OBJECTIVE: To investigate the spatial distribution characteristics of alterations in spontaneous brain activity in severe traumatic brain injury (sTBI) patients with disorders of consciousness (DOC), based on the mesocircuit theoretical framework, and to establish models for predicting recovery of consciousness.
METHODS: Resting-state functional magnetic resonance imaging was employed to measure the mean fractional amplitude of low-frequency fluctuations (mfALFF) in sTBI patients with DOC and healthy controls, identifying differential brain regions for conducting gene and functional decoding analyses. Patients were classified into wake and DOC groups according to Extended Glasgow Outcome Score at 6 months. Furthermore, predictive models for consciousness recovery were developed using Nomogram and Linear Support Vector Machine (LSVM) based on mfALFF.
RESULTS: In total, 28 sTBI patients with DOC and 30 healthy controls were included, with no significant baseline differences between groups (P > 0.05). The results revealed increased mfALFF of subcortical Ascending Reticular Activating System and decreased cortical mfALFF (default mode network) in DOC patients within the framework of the mesocircuit model (FDR_P < 0.001, Clusters > 100). The study identified 2080 differentially expressed genes associated with reduced brain activity regions, indicating mechanisms involving synaptic function, the oxytocin signaling pathway, and GABAergic processes in DOC formation. In addition, significantly higher mfALFF values were observed in the left angular gyrus, supramarginal gyrus, and inferior parietal lobule of DOC group compared to the wake group (AlphaSim_P < 0.01, Cluster > 19). The Nomogram prediction model highlighted the pivotal role of these regions' activity levels in prognosis (AUC = 0.90). Validation using LSVM demonstrated robust predictive performance with an AUC of 0.90 and positive predictive values of 80% for wake and 83% for DOC.
CONCLUSIONS: This study offered crucial insights underlying DOC in sTBI patients, demonstrating the dissociation between cortical and subcortical brain activities. The findings supported the use of mfALFF as a robust and non-invasive biomarker for evaluating brain function and predicting recovery outcomes.
PMID:39659957 | PMC:PMC11629552 | DOI:10.1016/j.ijchp.2024.100528
Functional connectivity density of postcentral gyrus predicts rumination and major depressive disorders in males
Psychiatry Res Neuroimaging. 2024 Dec 6;347:111939. doi: 10.1016/j.pscychresns.2024.111939. Online ahead of print.
ABSTRACT
Major depressive disorder (MDD) is characterized by persistent sadness and loss of interest. Recent evidence suggested that abnormal functional connectivity (FC) may be linked to the development of MDD, and gender differences existed in FC patterns. In this study, we utilized resting-state functional magnetic resonance imaging (RS-fMRI) data from 41 healthy participants to identify FC patterns that correlate with levels of rumination in both genders. A 2-sample t-test showed no gender differences in rumination levels. A functional connectivity density (FCD) analysis was then conducted using DPABI. It was revealed that in males, the FCD of the postcentral gyrus (PCG) was negatively correlated with the levels of rumination and brooding (Pearson's r > 0.25), while not with reflection. No FCD in females was related to rumination or its subtypes. Further FC analysis revealed that the FC between the PCG and several regions, predominantly from the default mode network (DMN), were negatively correlated with rumination levels (Pearson's r > 0.25). This link was assumed to be a risk factor for rumination and MDD in males. In conclusion, our findings indicate that the PCG-DMN connectivity is a potential risk factor for MDD in males, while no FC risk factors were found in females.
PMID:39657406 | DOI:10.1016/j.pscychresns.2024.111939
APOE ε4 and Insulin Resistance Influence Path-Integration-Based Navigation through Distinct Large-Scale Network Mechanisms
Aging Dis. 2024 Nov 25. doi: 10.14336/AD.2024.0975. Online ahead of print.
ABSTRACT
Path integration (PI), which supports navigation without external spatial cues, is facilitated by grid cells in the entorhinal cortex. These cells are often impaired in individuals at risk for Alzheimer's disease (AD). However, other brain systems can compensate for this impairment, especially when spatial cues are available. From a graph-theoretical perspective, this compensatory mechanism might manifest through changes in network segregation, indicating shifts in distinct functional roles among specialized brain regions. This study explored whether similar compensatory mechanisms are active in APOE ε4 carriers and individuals with elevated insulin resistance, both susceptible to entorhinal cortex dysfunction. We applied a graph-theoretical segregation index to resting-state fMRI data from two cohorts (aged 50-75) to assess PI performance across virtual environments. Although insulin resistance did not directly impair PI performance, individuals with higher insulin resistance demonstrated better PI with less segregated brain networks, regardless of spatial cue availability. In contrast, the APOE effect was cue-dependent: ε4 heterozygotes outperformed ε3 homozygotes in the presence of local landmarks, linked to increased sensorimotor network segregation. When spatial cues were absent, ε4 carriers exhibited reduced PI performance due to lower segregation in the secondary visual network. Controlling cortical thickness and intracortical myelin variability mitigated these APOE effects on PI, with no similar adjustment made for insulin resistance. Our findings suggest that ε4 carriers depend on cortical integrity and spatial landmarks for successful navigation, while insulin-resistant individuals may rely on less efficient neural mechanisms for processing PI. These results highlight the importance of targeting insulin resistance to prevent cognitive decline, particularly in aging navigation and spatial cognition.
PMID:39656485 | DOI:10.14336/AD.2024.0975
The Effect of Modular Degeneracy on Neuroimaging Data
Brain Connect. 2024 Dec 10. doi: 10.1089/brain.2023.0090. Online ahead of print.
ABSTRACT
Introduction: The concept of community structure, based on modularity, is widely used to address many systems-level queries. However, its algorithm, based on the maximization of the modularity index Q, suffers from degeneracy problem, which yields a set of different possible solutions. Methods: In this work, we explored the degeneracy effect of modularity principle on resting-state functional magnetic resonance imaging (rsfMRI) data, when it is used to parcellate the cingulate cortex using data from the Human Connectome Project. We proposed a new iterative approach to address this limitation. Results: Our results show that current modularity approaches furnish a variety of different solutions, when these algorithms are repeated, leading to different number of subdivisions for the cingulate cortex. Our new proposed method, however, overcomes this limitation and generates more stable solution for the final partition. Conclusion: With this new method, we were able to mitigate the degeneracy problem and offer a tool to use modularity in a more reliable manner, when applying it to rsfMRI data.
PMID:39655511 | DOI:10.1089/brain.2023.0090
Changes in Functional Connectivity Relate to Modulation of Cognitive Control by Subthalamic Stimulation
Hum Brain Mapp. 2024 Dec 1;45(17):e70095. doi: 10.1002/hbm.70095.
ABSTRACT
Subthalamic (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) patients not only improves kinematic parameters of movement but also modulates cognitive control in the motor and non-motor domain, especially in situations of high conflict. The objective of this study was to investigate the relationship between DBS-induced changes in functional connectivity at rest and modulation of response- and movement inhibition by STN-DBS in a visuomotor task involving high conflict. During DBS ON and OFF conditions, we conducted a visuomotor task in 14 PD patients who previously underwent resting-state functional MRI (rs-fMRI) acquisitions DBS ON and OFF as part of a different study. In the task, participants had to move a cursor with a pen on a digital tablet either toward (automatic condition) or in the opposite direction (controlled condition) of a target. STN-DBS induced modulation of resting-state functional connectivity (RSFC) as a function of changes in behavior ON versus OFF DBS was estimated using link-wise network-based statistics. Behavioral results showed diminished reaction time adaptation and higher pen-to-target movement velocity under DBS. Reaction time reduction was associated with attenuated functional connectivity between cortical motor areas, basal ganglia, and thalamus. On the other hand, increased movement velocity ON DBS was associated with stronger pallido-thalamic connectivity. These findings suggest that decoupling of a motor cortico-basal ganglia network underlies impaired inhibitory control in PD patients undergoing subthalamic DBS and highlight the concept of functional network modulation through DBS.
PMID:39655402 | DOI:10.1002/hbm.70095
Static and temporal dynamic changes of intrinsic brain activity in early-onset and adult-onset schizophrenia: a fMRI study of interaction effects
Front Neurol. 2024 Nov 25;15:1445599. doi: 10.3389/fneur.2024.1445599. eCollection 2024.
ABSTRACT
BACKGROUND: Schizophrenia is characterized by altered static and dynamic spontaneous brain activity. However, the conclusions regarding this are inconsistent. Evidence has revealed that this inconsistency could be due to mixed effects of age of onset.
METHODS: We enrolled 66/84 drug-naïve first-episode patients with early-onset/adult-onset schizophrenia (EOS/AOS) and matched normal controls (NCs) (46 adolescents, 73 adults), undergoing resting-state functional magnetic resonance imaging. Two-way ANOVA was used to determine the amplitude of low-frequency fluctuation (ALFF) and dynamic ALFF (dALFF) among the four groups.
RESULT: Compared to NCs, EOS had a higher ALFF in inferior frontal gyrus bilateral triangular part (IFG-tri), left opercular part (IFG-oper), left orbital part (IFG-orb), and left middle frontal gyrus (MFG). The AOS had a lower ALFF in left IFG-tri, IFG-oper, and lower dALFF in left IFG-tri. Compared to AOS, EOS had a higher ALFF in the left IFG-orb, and MFG, and higher dALFF in IFG-tri. Adult NCs had higher ALFF and dALFF in the prefrontal cortex (PFC) than adolescent NCs. The main effects of diagnosis were found in the PFC, medial temporal structures, cerebrum, visual and sensorimotor networks, the main effects of age were found in the visual and motor networks of ALFF and PFC of dALFF.
CONCLUSION: Our findings unveil the static and dynamic neural activity mechanisms involved in the interaction between disorder and age in schizophrenia. Our results underscore age-related abnormalities in the neural activity of the PFC, shedding new light on the neurobiological mechanisms underlying the development of schizophrenia. This insight may offer valuable perspectives for the specific treatment of EOS in clinical settings.
PMID:39655163 | PMC:PMC11625647 | DOI:10.3389/fneur.2024.1445599
Differences in cerebral spontaneous neural activity correlate with gene-specific transcriptional signatures in primary angle-closure glaucoma
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
st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks
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
fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review
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
Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer's disease classification
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
Cognitive and Neurobiological Correlates for Switching/Inhibition Moderate the Relations Between Word Reading and Reading Comprehension in Hebrew-Speaking Children: An fMRI Study
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
Utilizing Centromedian Thalamus Connectivity to Personalize Noninvasive Neuromodulation Targets
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
Riemannian manifold-based disentangled representation learning for multi-site functional connectivity analysis
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
The cognitive critical brain: modulation of criticality in perception-related cortical regions
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
Distinct Neural Bases of Visual Art- and Music-Induced Aesthetic Experiences
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
Resting fMRI-guided TMS evokes subgenual anterior cingulate response in depression
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
A multimodal Neuroimaging-Based risk score for mild cognitive impairment
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