Most recent paper

Hum Brain Mapp. 2025 Jan;46(1):e70107. doi: 10.1002/hbm.70107.

ABSTRACT

Disruptions to brain networks, measured using structural (sMRI), diffusion (dMRI), or functional (fMRI) MRI, have been shown in people with multiple sclerosis (PwMS), highlighting the relevance of regions in the core of the connectome but yielding mixed results depending on the studied connectivity domain. Using a multilayer network approach, we integrated these three modalities to portray an enriched representation of the brain's core-periphery organization and explore its alterations in PwMS. In this retrospective cross-sectional study, we selected PwMS and healthy controls with complete multimodal brain MRI acquisitions from 13 European centers within the MAGNIMS network. Physical disability and cognition were assessed with the Expanded Disability Status Scale (EDSS) and the symbol digit modalities test (SDMT), respectively. SMRI, dMRI, and resting-state fMRI data were parcellated into 100 cortical and 14 subcortical regions to obtain networks of morphological covariance, structural connectivity, and functional connectivity. Connectivity matrices were merged in a multiplex, from which regional coreness-the probability of a node being part of the multiplex core-and coreness disruption index (κ)-the global weakening of the core-periphery structure-were computed. The associations of κ with disease status (PwMS vs. healthy controls), clinical phenotype, level of physical disability (EDSS ≥ 4 vs. EDSS < 4), and cognitive impairment (SDMT z-score < -1.5) were tested within a linear model framework. Using random forest permutation feature importance, we assessed the relative contribution of κ in the multiplex and single-layer domains, in addition to conventional MRI measures (brain and lesion volumes), in predicting disease status, physical disability, and cognitive impairment. We studied 1048 PwMS (695F, mean ± SD age: 43.3 ± 11.4 years) and 436 healthy controls (250F, mean ± SD age: 38.3 ± 11.8 years). PwMS showed significant disruption of the multiplex core-periphery organization (κ = -0.14, Hedges' g = 0.49, p < 0.001), correlating with clinical phenotype (F = 3.90, p = 0.009), EDSS (Hedges' g = 0.18, p = 0.01), and SDMT (Hedges' g = 0.30, p < 0.001). Multiplex κ was the only connectomic measure adding to conventional MRI in predicting disease status and cognitive impairment, while physical disability also depended on single-layer contributions. In conclusion, we show that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core-periphery structure emerging as a potential connectomic biomarker for disease severity and cognitive impairment in PwMS.

PMID:39740239 | DOI:10.1002/hbm.70107

Sci Rep. 2024 Dec 30;14(1):32032. doi: 10.1038/s41598-024-83696-7.

ABSTRACT

Network energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.

PMID:39738735 | DOI:10.1038/s41598-024-83696-7

Brain Struct Funct. 2024 Dec 30;230(1):22. doi: 10.1007/s00429-024-02888-z.

ABSTRACT

The default mode network (DMN) is a singular pattern of synchronization between brain regions, usually observed using resting-state functional magnetic resonance imaging (rs-fMRI) and functional connectivity analyses. In comparison to other brain networks that are primarily involved in attentional-demanding tasks (such as the frontoparietal network), the DMN is linked with self-referential activities, and alterations in its pattern of connectivity have been related to a wide range of disorders. Structural connectivity analyses have highlighted the vital role of the posterior cingulate cortex and the precuneus as integrative hubs, and advanced parcellation methods have further contributed to elucidate the DMN's regions, enriching its explanatory potential across cognitive functions and dysfunctions. Interestingly, the study of its temporal characteristics - the specific frequency spectrum of BOLD signal oscillations -, its developmental trajectory over the course of life, and its interaction with other networks, provides new insight into the DMN's defining features. In this context, this review aims to synthesize the state of the art in the study of the DMN to provide the most updated findings to anyone interested in its research. Finally, some weaknesses in the current state of knowledge and some interesting lines of work for further progress in the study of the DMN are presented.

PMID:39738718 | DOI:10.1007/s00429-024-02888-z

Sci Rep. 2024 Dec 30;14(1):31651. doi: 10.1038/s41598-024-80645-2.

ABSTRACT

This study aimed to investigate alterations in a multilayer network combining structural and functional layers in patients with end-stage kidney disease (ESKD) compared with healthy controls. In all, 38 ESKD patients and 43 healthy participants were prospectively enrolled. They exhibited normal brain magnetic resonance imaging (MRI) without any structural lesions. All participants, both ESRD patients and healthy controls, underwent T1-weighted imaging, diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI) using the same three-tesla MRI scanner. A structural connectivity matrix was generated using the DTI and DSI programs, and a functional connectivity matrix was created using the rs-fMRI and SPM programs in the CONN toolbox. Multilayer network analysis was conducted based on structural and functional connectivity matrices using BRAPH. Significant differences were observed at the global level in the multilayer network between patients with ESKD and healthy controls. The weighted multiplex participation was lower in patients with ESKD than in healthy controls (0.6454 vs. 0.7212, adjusted p = 0.049). However, other multilayer network measures did not differ. The weighted multiplex participation in the right subcentral gyrus, right opercular part of the inferior frontal gyrus, right occipitotemporal medial lingual gyrus, and right postcentral gyrus in patients with ESKD was lower than that in the corresponding regions in healthy controls (0.6704 vs. 0.8562, 0.8593 vs. 0.9388, 0.7778 vs. 0.8849, and 0.6825 vs. 0.8112; adjusted p < 0.05, respectively).This study demonstrated that the multilayer network combining structural and functional layers in patients with ESKD was different from that in healthy controls. The specific differences in weighted multiplex participation suggest potential disruptions in the integrated communication between different brain regions in these patients.

PMID:39738277 | DOI:10.1038/s41598-024-80645-2

Sci Rep. 2024 Dec 30;14(1):31606. doi: 10.1038/s41598-024-76695-1.

ABSTRACT

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining consciousness. Here, we investigated the complexity of selecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, compared with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. We then explored the organization of the obtained low-dimensional dFC latent representations. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. We first applied a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation study was achieved where we virtually inactivated specific brain areas. We demonstrated the model's efficiency in summarizing consciousness-specific information encoded in key inter-areal connections, as described in the global neuronal workspace theory of consciousness. The proposed framework advocates the possibility of developing an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool.

PMID:39738114 | DOI:10.1038/s41598-024-76695-1

Nat Commun. 2024 Dec 30;15(1):10820. doi: 10.1038/s41467-024-55064-6.

ABSTRACT

The correlational structure of brain activity dynamics in the absence of stimuli or behavior is often taken to reveal intrinsic properties of neural function. To test the limits of this assumption, we analyzed peripheral contributions to resting state activity measured by fMRI in unanesthetized, chemically immobilized male rats that emulate human neuroimaging conditions. We find that perturbation of somatosensory input channels modifies correlation strengths that relate somatosensory areas both to one another and to higher-order brain regions, despite the absence of ostensible stimuli or movements. Resting state effects are mediated by the same peripheral and thalamic structures that relay responses to overt sensory stimuli. The impact of basal peripheral input is reduced in a rat model of autism, which displays both lower somatosensory functional connectivity and insensitivity to vibrissa inactivation. These results demonstrate the influence of extrinsic influences on resting state brain phenotypes in health and disease.

PMID:39737991 | DOI:10.1038/s41467-024-55064-6

Nat Commun. 2024 Dec 30;15(1):10791. doi: 10.1038/s41467-024-54947-y.

ABSTRACT

Resting-state functional connectivity (rsFC) has been essential to elucidate the intricacy of brain organization, further revealing clinical biomarkers of neurological disorders. Although functional magnetic resonance imaging (fMRI) remains a cornerstone in the field of rsFC recordings, its interpretation is often hindered by the convoluted physiological origin of the blood-oxygen-level-dependent (BOLD) contrast affected by multiple factors. Here, we capitalize on the unique concurrent multiparametric hemodynamic recordings of a hybrid magnetic resonance optoacoustic tomography platform to comprehensively characterize rsFC in female mice. The unique blood oxygenation readings and high spatio-temporal resolution at depths provided by functional optoacoustic (fOA) imaging offer an effective means for elucidating the connection between BOLD and hemoglobin responses. Seed-based and independent component analyses reveal spatially overlapping bilateral correlations between the fMRI-BOLD readings and the multiple hemodynamic components measured with fOA but also subtle discrepancies, particularly in anti-correlations. Notably, total hemoglobin and oxygenated hemoglobin components are found to exhibit stronger correlation with BOLD than deoxygenated hemoglobin, challenging conventional assumptions on the BOLD signal origin.

PMID:39737925 | DOI:10.1038/s41467-024-54947-y

Transl Psychiatry. 2024 Dec 30;14(1):507. doi: 10.1038/s41398-024-03212-3.

ABSTRACT

Bipolar disorder (BD) is a neuropsychiatric disorder characterized by severe disturbance and fluctuation in mood. Dynamic functional connectivity (dFC) has the potential to more accurately capture the evolving processes of emotion and cognition in BD. Nevertheless, prior investigations of dFC typically centered on larger time scales, limiting the sensitivity to transient changes. This study employed hidden Markov model (HMM) analysis to delve deeper into the moment-to-moment temporal patterns of brain activity in BD. We utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 43 BD patients and 51 controls to evaluate the altered dynamic spatiotemporal architecture of the whole-brain network and identify unique activation patterns in BD. Additionally, we investigated the relationship between altered brain dynamics and structural disruption through the ridge regression (RR) algorithm. The results demonstrated that BD spent less time in a hyperconnected state with higher network efficiency and lower segregation. Conversely, BD spent more time in anticorrelated states featuring overall negative correlations, particularly among pairs of default mode network (DMN) and sensorimotor network (SMN), DMN and insular-opercular ventral attention networks (ION), subcortical network (SCN) and SMN, as well as SCN and ION. Interestingly, the hypoactivation of the cognitive control network in BD may be associated with the structural disruption primarily situated in the frontal and parietal lobes. This study investigated the dynamic mechanisms of brain network dysfunction in BD and offered fresh perspectives for exploring the physiological foundation of altered brain dynamics.

PMID:39737898 | DOI:10.1038/s41398-024-03212-3

J Exerc Sci Fit. 2025 Jan;23(1):14-20. doi: 10.1016/j.jesf.2024.12.001. Epub 2024 Dec 6.

ABSTRACT

BACKGROUND: Basketball is an attractive sport required both cooperative and antagonistic motor skills. However, the neural mechanism of basketball proficiency remains unclear. This study aimed to examine the brain functional and structural substrates underlying varying levels of basketball capacity.

METHODS: Twenty advanced basketball athletes (AB), 20 intermediate basketball athletes (IB) and 20 age-matched non-athlete individuals without basketball experience (NI) participated in this study and underwent T1-weighted MRI and resting-state fMRI scanning. Voxel-mirrored homotopic connectivity (VMHC), amplitude of low frequency fluctuations (ALFF), and gray matter (GM) density were calculated and compared among the three groups.

RESULTS: The VMHC in the bilateral postcentral gyrus, middle temporal gyrus, and superior temporal gyrus, as well as the GM density in the right precentral gyrus, exhibited a hierarchical structure of AB > IB > NI. Compared with NI group, AB and IB groups showed strengthened VMHC in supplementary motor area, paracentral lobule and superior frontal gyrus. Additionally, the ALFF of left middle occipital gyrus and right hippocampal and the GM density of left medial superior frontal gyrus exhibited differences in AB-IB and AB-NI comparisons.

CONCLUSIONS: By conducting the cross-sectional comparison, this study firstly identifies the varying levels of basketball proficiency related brain resting-state functional and structural plasticity. Especially, the regions associated with motor perception and control, including bilateral postcentral gyrus, middle and superior temporal gyrus and right precentral gyrus, are involved in the key neural mechanisms of basketball proficiency. Future longitudinal studies are necessary to further validate these findings.

PMID:39737438 | PMC:PMC11683229 | DOI:10.1016/j.jesf.2024.12.001

Front Immunol. 2024 Dec 16;15:1474988. doi: 10.3389/fimmu.2024.1474988. eCollection 2024.

ABSTRACT

BACKGROUND: This study examined the interhemispheric integration function pattern in patients with iridocyclitis utilizing the voxel-mirrored homotopic connectivity (VMHC) technique. Additionally, we investigated the ability of VMHC results to distinguish patients with iridocyclitis from healthy controls (HCs), which may contribute to the development of objective biomarkers for early diagnosis and intervention in clinical set.

METHODS: Twenty-six patients with iridocyclitis and twenty-six matched HCs, in terms of sex, age, and education level, underwent resting-state functional magnetic resonance imaging (fMRI) examinations. The study employed the voxel-mirrored homotopic connectivity (VMHC) technique to evaluate interhemispheric integration functional connectivity indices at a voxel-wise level. The diagnostic efficacy of VMHC was evaluated using a support vector machine (SVM) classifier, with classifier performance assessed through permutation test analysis. Furthermore, correlation analyses was conducted to investigate the associations between mean VMHC values in various brain regions and clinical features.

RESULTS: Patients with iridocyclitis exhibited significantly reduced VMHC signal values in the bilateral inferior temporal gyrus, calcarine, middle temporal gyrus, and precuneus compared to HCs (voxel-level P < 0.01, Gaussian Random Field correction; cluster-level P < 0.05). Furthermore, the extracted resting-state zVMHC features effectively classified patients with iridocyclitis and HCs, achieving an area under the receiver operating characteristic curve (AUC) of 0.74 and an overall accuracy of 0.673 (P < 0.001, non-parametric permutation test).

CONCLUSION: Our findings reveal disrupted interhemispheric functional organization in patients with iridocyclitis, offering insight into the pathophysiological mechanisms associated with vision loss and cognitive dysfunction in this patient population. This study also highlights the potential of machine learning in ophthalmology and the importance of establishing objective biomarkers to address diagnostic heterogeneity.

PMID:39737192 | PMC:PMC11683089 | DOI:10.3389/fimmu.2024.1474988

Psychoradiology. 2024 Nov 21;4:kkae026. doi: 10.1093/psyrad/kkae026. eCollection 2024.

ABSTRACT

BACKGROUND: The lack of clearly defined neuromodulation targets has contributed to the inconsistent results of real-time fMRI-based neurofeedback (rt-fMRI-NF) for the treatment of chronic pain. Functional neurosurgery (funcSurg) approaches have shown more consistent effects in reducing pain in patients with severe chronic pain.

OBJECTIVE: This study aims to redefine rt-fMRI-NF targets for chronic pain management informed by funcSurg studies.

METHODS: Based on independent systematic reviews, we identified the neuromodulation targets of the rt-fMRI-NF (in acute and chronic pain) and funcSurg (in chronic pain) studies. We then characterized the underlying functional networks using a subsample of the 7 T resting-state fMRI dataset from the Human Connectome Project. Principal component analyses (PCA) were used to identify dominant patterns (accounting for a cumulative explained variance >80%) within the obtained functional maps, and the overlap between these PCA maps and canonical intrinsic brain networks (default, salience, and sensorimotor) was calculated using a null map approach.

RESULTS: The anatomical targets used in rt-fMRI-NF and funcSurg approaches are largely distinct, with the middle cingulate cortex as a common target. Within the investigated canonical rs-fMRI networks, these approaches exhibit both divergent and overlapping functional connectivity patterns. Specifically, rt-fMRI-NF approaches primarily target the default mode network (P value range 0.001-0.002) and the salience network (P = 0.002), whereas funcSurg approaches predominantly target the salience network (P = 0.001) and the sensorimotor network (P value range 0.001-0.023).

CONCLUSION: Key hubs of the salience and sensorimotor networks may represent promising targets for the therapeutic application of rt-fMRI-NF in chronic pain.

PMID:39737084 | PMC:PMC11683833 | DOI:10.1093/psyrad/kkae026

J Integr Neurosci. 2024 Dec 12;23(12):217. doi: 10.31083/j.jin2312217.

ABSTRACT

BACKGROUND: The National Institutes of Health (NIH) Toolbox Cognition Battery is increasingly being used as a standardized test to examine cognitive functioning in multicentric studies. This study examines the associations between the NIH Toolbox Cognition Battery composite scores with neuroimaging metrics using data from the Adolescent Brain Cognitive Development (ABCD) study to elucidate the neurobiological and neuroanatomical correlates of these cognitive scores.

METHODS: Neuroimaging data from 5290 children (mean age 9.9 years) were analyzed, assessing the correlation of the composite scores with Diffusion Tensor Imaging (DTI), structural Magnetic Resonance Imaging (sMRI), and resting-state functional connectivity (rs-fMRI). Results were adjusted for age, sex, race/ethnicity, head size, body mass index (BMI), and parental income and education.

RESULTS: Higher fluid cognition composite scores were linked to greater white matter (WM) microstructural integrity, lower cortical thickness, greater cortical surface area, and mixed associations with rs-fMRI. Conversely, crystallized cognition composite scores showed more complex associations, suggesting that higher scores correlated with lower WM microstructure integrity. Total cognition scores reflected patterns consistent with a combination of both fluid and crystallized cognition, but with diluted specific insights. Our findings highlight the complexity of the neuroimaging correlates of the NIH Toolbox composite scores.

CONCLUSIONS: The results suggest that fluid cognition composite scores may serve as a marker for cognitive functioning, emphasizing neuroimaging's clinical relevance in assessing cognitive performance in children. These insights can guide early interventions and personalized education strategies. Future ABCD follow-ups will further illuminate these associations into adolescence and adulthood.

PMID:39735971 | DOI:10.31083/j.jin2312217

Netw Neurosci. 2024 Dec 10;8(4):1089-1104. doi: 10.1162/netn_a_00390. eCollection 2024.

ABSTRACT

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (>1 Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting state (N = 926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of subsecond connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals' cognitive abilities and traits. Such subsecond connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

PMID:39735509 | PMC:PMC11674572 | DOI:10.1162/netn_a_00390

Netw Neurosci. 2024 Dec 10;8(4):1065-1088. doi: 10.1162/netn_a_00391. eCollection 2024.

ABSTRACT

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infraslow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting state (N = 928, 473 females), we quantified the heritability of multivariate (multistate) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60-500 ms. Temporal features were heritable, particularly Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of the phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for the heritability of dynamic spatial features, specifically states' Modularity and connectivity pattern. We conclude that genetic effects shape individuals' connectome dynamics at rapid timescales, specifically states' overall occurrence and sequencing.

PMID:39735507 | PMC:PMC11674403 | DOI:10.1162/netn_a_00391

Netw Neurosci. 2024 Dec 10;8(4):1212-1242. doi: 10.1162/netn_a_00398. eCollection 2024.

ABSTRACT

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multiobjective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multimodal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared with the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

PMID:39735500 | PMC:PMC11674407 | DOI:10.1162/netn_a_00398

Netw Neurosci. 2024 Dec 10;8(4):1310-1330. doi: 10.1162/netn_a_00401. eCollection 2024.

ABSTRACT

Memory is a complex brain process that requires coordinated activities in a large-scale brain network. However, the relationship between coordinated brain network activities and memory-related behavior is not well understood. In this study, we investigated this issue by suppressing the activity in the dorsal hippocampus (dHP) using chemogenetics and measuring the corresponding changes in brain-wide resting-state functional connectivity (RSFC) and memory behavior in awake rats. We identified an extended brain network contributing to the performance in a spatial memory related task. Our results were cross-validated using two different chemogenetic actuators, clozapine (CLZ) and clozapine-N-oxide (CNO). This study provides a brain network interpretation of memory performance, indicating that memory is associated with coordinated brain-wide neural activities.

PMID:39735497 | PMC:PMC11674488 | DOI:10.1162/netn_a_00401

Netw Neurosci. 2024 Dec 10;8(4):1105-1128. doi: 10.1162/netn_a_00392. eCollection 2024.

ABSTRACT

A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.

PMID:39735496 | PMC:PMC11674492 | DOI:10.1162/netn_a_00392

Netw Neurosci. 2024 Dec 10;8(4):1491-1506. doi: 10.1162/netn_a_00413. eCollection 2024.

ABSTRACT

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity that the other type of network does not.

PMID:39735494 | PMC:PMC11675104 | DOI:10.1162/netn_a_00413

Netw Neurosci. 2024 Dec 10;8(4):1613-1633. doi: 10.1162/netn_a_00412. eCollection 2024.

ABSTRACT

The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone-although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.

PMID:39735493 | PMC:PMC11675094 | DOI:10.1162/netn_a_00412

Netw Neurosci. 2024 Dec 10;8(4):1173-1191. doi: 10.1162/netn_a_00396. eCollection 2024.

ABSTRACT

The atypical static brain functions related to the executive control network (ECN), default mode network (DMN), and salience network (SN) in people with autism spectrum disorder (ASD) has been widely reported. However, their transient functions in ASD are not clear. We aim to identify transient network states (TNSs) using coactivation pattern (CAP) analysis to characterize the age-related atypical transient functions in ASD. CAP analysis was performed on a resting-state fMRI dataset (78 ASD and 78 healthy control (CON) juveniles, 54 ASD and 54 CON adults). Six TNSs were divided into the DMN-TNSs, ECN-TNSs, and SN-TNSs. The DMN-TNSs were major states with the highest stability and proportion, and the ECN-TNSs and SN-TNSs were minor states. Age-related abnormalities on spatial stability and TNS proportion were found in ASD. The spatial stability of DMN-TNSs was found increasing with age in CON, but was not found in ASD. A lower proportion of DMN-TNSs was found in ASD compared with CON of the same age, and ASD juveniles had a higher proportion of SN-TNSs while ASD adults had a higher proportion of ECN-TNSs. The abnormalities on spatial stability and TNS proportion were related to social deficits. Our results provided new evidence for atypical transient brain functions in people with ASD.

PMID:39735491 | PMC:PMC11674577 | DOI:10.1162/netn_a_00396