Most recent paper

Multilayer network analysis in patients with end-stage kidney disease

Tue, 12/31/2024 - 19:00

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

Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness

Tue, 12/31/2024 - 19:00

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

Peripheral contributions to resting state brain dynamics

Tue, 12/31/2024 - 19:00

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

Concurrent optoacoustic tomography and magnetic resonance imaging of resting-state functional connectivity in the mouse brain

Tue, 12/31/2024 - 19:00

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

Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach

Tue, 12/31/2024 - 19:00

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

Neural correlates of basketball proficiency: An MRI study across skill levels

Tue, 12/31/2024 - 19:00

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

Impaired interhemispheric synchrony in patients with iridocyclitis and classification using machine learning: an fMRI study

Tue, 12/31/2024 - 19:00

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

Identifying brain targets for real-time fMRI neurofeedback in chronic pain: insights from functional neurosurgery

Tue, 12/31/2024 - 19:00

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

Neuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children

Mon, 12/30/2024 - 19:00

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

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Mon, 12/30/2024 - 19:00

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

Rapid dynamics of electrophysiological connectome states are heritable

Mon, 12/30/2024 - 19:00

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

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

Mon, 12/30/2024 - 19:00

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

Exploring memory-related network via dorsal hippocampus suppression

Mon, 12/30/2024 - 19:00

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

Hemodynamic cortical ripples through cyclicity analysis

Mon, 12/30/2024 - 19:00

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

Contrasting topologies of synchronous and asynchronous functional brain networks

Mon, 12/30/2024 - 19:00

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

Generative dynamical models for classification of rsfMRI data

Mon, 12/30/2024 - 19:00

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

Age-related unstable transient states and imbalanced activation proportion of brain networks in people with autism spectrum disorder: A resting-state fMRI study using coactivation pattern analyses

Mon, 12/30/2024 - 19:00

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

Differential brain activity in patients with disorders of consciousness: a 3-month rs-fMRI study using amplitude of low-frequency fluctuation

Mon, 12/30/2024 - 19:00

Front Neurol. 2024 Dec 13;15:1477596. doi: 10.3389/fneur.2024.1477596. eCollection 2024.

ABSTRACT

INTRODUCTION: Disorders of consciousness (DoC) from severe brain injuries have significant impacts. However, further research on nuanced biomarkers is needed to fully understand the condition. This study employed resting-state functional MRI (rs-fMRI) and the amplitude of low-frequency fluctuation (ALFF) to investigate differential brain activity in patients with DoC following spinal cord stimulation (SCS) therapy. It also assessed the predictive value of rs-fMRI and ALFF in determining the consciousness levels at 3 months post-therapy.

METHODS: We analyzed rs-fMRI data from 31 patients with traumatic brain injury (TBI) and 22 with non-traumatic brain injury (non-TBI) diagnosed with DoC. ALFF was measured before SCS therapy, and clinical outcomes were assessed 3 months later using the Coma Recovery Scale-Revised.

RESULTS: Patients with TBI showed increased ALFF in the thalamus and anterior cingulate cortex, whereas the middle occipital lobe showed decreased ALFF. In the non-TBI group, a higher ALFF was noted in the precuneus, with a reduced ALFF in the occipital and temporal lobes. Patients with improved consciousness post-SCS exhibited distinct ALFF patterns compared with those with unchanged consciousness, particularly in the posterior cingulate and occipital regions.

CONCLUSION: The application of ALFF in rs-fMRI may be a predictive tool for post-treatment outcomes in patients with DoC of varying etiologies. Differential ALFF in specific brain regions could indicate the likelihood of improvement in consciousness following SCS therapy.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/, Identifier ChiCTR2300069756.

PMID:39734630 | PMC:PMC11673223 | DOI:10.3389/fneur.2024.1477596

Disrupted brain networks underlying high-fidelity memory retrieval in subjective cognitive decline: A task-based fMRI study

Sat, 12/28/2024 - 19:00

Alzheimers Dement. 2024 Dec 28. doi: 10.1002/alz.14431. Online ahead of print.

ABSTRACT

INTRODUCTION: Subjective cognitive decline (SCD) is linked to memory complaints and disruptions in certain brain regions identified by molecular imaging and resting-state functional magnetic resonance imaging studies. However, it remains unclear how these regions interact to contribute to both subjective and potential objective memory issues in SCD.

METHODS: To address this gap, task-based imaging studies are essential. The Mnemonic Similarity Task assessed high-fidelity retrieval meanwhile MRI data measured group differences in activation and functional connectivity (FC) (calculated by generalized psychophysiological Interaction) between SCD individuals and normal controls.

RESULTS: Worse high-fidelity retrieval in SCD was associated with hypoactivation in the hippocampus, hyperactivation in the control network (CN), and reduced FC between the hippocampus and CN. The angular gyrus (AG) partially drives this disconnection.

DISCUSSION: This study confirms objective cognitive deficits in SCD and highlights the AG's failure to integrate, addressing a gap in the literature that has primarily focused on the hippocampus and CN.

HIGHLIGHTS: Objective high-fidelity retrieval deficits detected in older adults with SCD. Dysfunctional neural activations within retrieval networks impair memory accuracy. Reduced task-based FC drives high-fidelity retrieval deficits. The SCD-related disruption of integrative function partly explains such deficits. Future studies may benefit from inspections on obstruction in cognitive process.

PMID:39732511 | DOI:10.1002/alz.14431

Cognitive Control Network and Language Reorganization in Patients with Brain Tumors

Fri, 12/27/2024 - 19:00

AJNR Am J Neuroradiol. 2024 Dec 27:ajnr.A8638. doi: 10.3174/ajnr.A8638. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: The interaction between language and other cognitive networks in patients harboring brain tumors is poorly understood. We studied the modification of the cognitive control network (CCN) induced by brain tumors and its participation in language reorganization. We hypothesized that patients with brain tumors and reorganized language would show modification of the CCN compared to patients who remain left dominant.

MATERIALS AND METHODS: Patients were selected with the criteria: newly diagnosed, pathologically-confirmed left hemispheric tumor; single lesions; right handedness; task-based and resting-state fMRI; no artifacts. Age-matched healthy controls (HC) were recruited from open sources databases. Language laterality was calculated using task-based fMRI. We obtained the CCN through ad hoc independent component analysis on resting-state fMRI. Difference in CCN between patients and HC were characterized through cosine similarity (CS) and earth mover's distance (EMD). Changes related to language reorganization and patients' speech were assessed with t-test (p<0.05). Results were corrected for multiple comparisons.

RESULTS: 142 right handed patients (35 low grade, 88 high grade glioma; 19 metastases) and 184 HC were included. Two independent components of the CCN were obtained. T-test confirmed significant effects of lateralization on the CCN (p=0.004). Modification of CCN was associated with less speech deficits 1 week after surgery (p=0.005).

CONCLUSIONS: This study provides evidence that modifications of CCN occur in the setting of language reorganization. Patients exhibiting these modifications perform better at speech evaluation after surgery, suggesting a role of cognitive control in compensating for speech deficits when language reorganizes.

ABBREVIATIONS: AD = atypical dominant; CCN = cognitive control network; CS = cosine similarity; EMD = earth mover's distance; fMRI = functional magnetic resonance imaging; GIG-ICA = group-information guided ICA; HGG = high-grade glioma; LD = left dominant; LGG = low-grade glioma; LI= laterality index; ROI = region of interest; RSNs = resting state networks.

PMID:39730156 | DOI:10.3174/ajnr.A8638