Most recent paper

Neuroimage. 2025 Jan 30:121055. doi: 10.1016/j.neuroimage.2025.121055. Online ahead of print.

ABSTRACT

It has been widely observed that cognitive training can enhance the working memory capacity (WMC) of participants, yet the underlying mechanisms remain unexplained. Previous research has confirmed that abacus-based mental calculation (AMC) training can enhance the WMC of subjects and suggested its possible association with changes in functional connectivity. With fMRI data, we construct whole brain resting state connectivity of subjects who underwent long-term AMC training and other subjects from a control group. Their working memory capacity is simulated based on their whole brain resting state connectivity and reservoir computing. It is found that the AMC group has higher WMC than the control group, and especially the WMC involved in the frontoparietal network (FPN), visual network (VIS) and sensorimotor network (SMN) associated with the AMC training is even higher in the AMC group. However, the advantage of the AMC group disappears if the connection strengths between brain regions are neglected. The effects on WMC from the connection strength differences between the AMC and control groups are evaluated. The results show that the WMC of the control group is enhanced and achieved consistency with or even better than that the AMC group if the connection strength of the control group are weakened. And the advantage of FPN, VIS and SMN is reproduced too. In conclusion, our work reveals a correlation between reduction in functional connection strength and enhancements in the WMC of subjects undergoing cognitive training.

PMID:39892528 | DOI:10.1016/j.neuroimage.2025.121055

Med Image Anal. 2025 Jan 29;101:103462. doi: 10.1016/j.media.2025.103462. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. In this study we propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix, and provides evidence of goal-specific brain connectivity patterns, which opens up potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand. Our implementation can be found on https://github.com/bishalth01/DSAM.

PMID:39892220 | DOI:10.1016/j.media.2025.103462

Hum Brain Mapp. 2025 Feb 1;46(2):e70145. doi: 10.1002/hbm.70145.

ABSTRACT

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting-state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting-state fMRI signals by evaluating the temporal variations and non-stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non-stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting-state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development.

PMID:39891569 | DOI:10.1002/hbm.70145

J Neurosci. 2025 Jan 31:e1449242024. doi: 10.1523/JNEUROSCI.1449-24.2024. Online ahead of print.

ABSTRACT

Prematurely born infants often experience frequent hypoxic episodes due to immaturity of respiratory control resulting in disturbances of cortical development and long-term cognitive and behavioral abnormalities. We hypothesize that neonatal intermittent hypoxia alters maturation of cortical excitatory and inhibitory circuits that can be detected early with functional MRI. C57BL/6 mouse male and female pups were exposed to an intermittent hypoxia (IH) regimen from P3 to P7, corresponding to pre-term humans. Adult mice after neonatal IH exhibited motor hyperactivity and impaired motor learning in complex wheel tests. Patch clamp and evoked field potential recordings revealed increased glutamatergic synaptic transmission. To investigate the role of GABAergic inhibition on glutamatergic transmission during the developmental, we applied a selective GABAA receptor inhibitor picrotoxin. A decreased synaptic inhibitory drive in the motor cortex was evidenced by miniature IPSC frequency on pyramidal cells, multi-unit activity recording in vivo with picrotoxin injection, and decreased interneuron density. There was also an increased tonic depolarizing effect of picrotoxin after IH on Betz cells' membrane potential on patch clamp and direct current potential in extracellular recordings. The amplitude of low-frequency fluctuation on resting-state fMRI was larger, with a larger increase in regional homogeneity index after picrotoxin injection in the IH group.The increased glutamatergic transmission, decreased numbers, and activity of inhibitory interneurons after neonatal IH may affect the maturation of connectivity in cortical networks, resulting in long-term cognitive and behavioral changes. Functional MRI reveals increased intrinsic connectivity in the sensorimotor cortex, suggesting neuronal dysfunction in cortical maturation after neonatal IH.Significance Statement The study demonstrates that perinatal hypoxic brain injury disrupts the balance between excitatory and inhibitory neurotransmission in developing cortical networks. This disruption, potentially caused by functional deficiencies in GABAergic interneurons alongside increased glutamatergic transmission, may contribute to altered brain connectivity and the observed behavioral deficits, including hyperactivity and cognitive difficulties. This research provides insights into how perinatal brain injury disrupts the balance of neural excitation and inhibition, which can be detected as altered local resting-state fMRI connectivity. These findings contribute to our understanding of possible cellular underpinning of clinical fMRI findings after perinatal brain injury.

PMID:39890465 | DOI:10.1523/JNEUROSCI.1449-24.2024

eNeuro. 2025 Jan 31:ENEURO.0375-24.2024. doi: 10.1523/ENEURO.0375-24.2024. Online ahead of print.

ABSTRACT

The perception of scenes is associated with processing in a network of scene-selective regions in the human brain. Prior research has identified a posterior-anterior bias within this network. Posterior scene regions exhibit preferential connectivity with early visual and posterior parietal regions, indicating a role in representing egocentric visual features. In contrast, anterior scene regions demonstrate stronger connectivity with frontoparietal control and default mode networks, suggesting a role in mnemonic processing of locations. Despite these findings, evidence linking connectivity in these regions to cognitive scene processing remains limited. In this preregistered study, we obtained cognitive behavioural measures alongside resting-state fMRI data from a large-scale public dataset to investigate interindividual variation in scene processing abilities relative to the functional connectivity of the scene network. Our results revealed substantial individual differences in scene recognition, spatial memory, and navigational abilities. Resting-state functional connectivity reproduced the posterior-anterior bias within the scene network. However, contrary to our preregistered hypothesis, we did not observe any consistent associations between interindividual variation in this connectivity and behavioural performance. These findings highlight the need for further research to clarify the role of these connections in scene processing, potentially through assessments of functional connectivity during scene-relevant tasks or in naturalistic conditions.Significance Statement Our ability to process scenes is crucial for interacting with our environment as it allows us to extract spatial, contextual, and navigational information. However, the mechanisms by which the scene network in the human brain supports these abilities remain poorly understood. To investigate this, we compared behavioural measures of scene processing with resting-state functional connectivity within the scene network. Extensive individual variability was evident in scene recognition, spatial memory, and navigational abilities. However, contrary to our preregistered hypothesis, we did not observe any consistent associations between task performance and the resting-state functional connectivity of the scene network. These results suggest that future research employing task-related or naturalistic designs may be necessary for elucidating the neural basis of scene perception.

PMID:39890456 | DOI:10.1523/ENEURO.0375-24.2024

Brain Stimul. 2025 Jan 29:S1935-861X(25)00030-0. doi: 10.1016/j.brs.2025.01.024. Online ahead of print.

NO ABSTRACT

PMID:39889819 | DOI:10.1016/j.brs.2025.01.024

Asian J Psychiatr. 2025 Jan 25;104:104380. doi: 10.1016/j.ajp.2025.104380. Online ahead of print.

ABSTRACT

OBJECTIVE: The purpose of this study was to investigate the alterations of intrinsic brain function in major depressive disorder (MDD) patients with and without anhedonia based on whole-brain level by using two novel measures, four-dimensional spatial-temporal consistency of local neural activity (FOCA) and local functional connectivity density (lFCD).

METHODS: A total of 26 MDD patients with anhedonia (MDD-WA), 29 MDD patients without anhedonia (MDD-WoA), and 30 healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning and intrinsic brain function was explored by FOCA and lFCD. A two-sample t-test was conducted to explore FOCA and lFCD differences between MDD patients and HCs, then analysis of covariance (ANCOVA) and post hoc tests were performed to obtain brain regions with significant differences among three groups. Finally, the diagnostic performance of FOCA and lFCD values with significant inter-group difference was evaluated using receiver operating characteristic (ROC) curves.

RESULTS: Compared to HCs, MDD patients showed decreased FOCA in the right cuneus (CUN) and left postcentral gyrus (PoCG), as well as diminished lFCD in the right CUN and left calcarine fissure and surrounding cortex (CAL). Interestingly, the MDD-WA group was more likely to exhibit decreased FOCA in the left PoCG and reduced lFCD in the left CAL after consideration for the effect of anhedonia in MDD patients. The MDD-WA group further showed increased FOCA in the bilateral caudate (CAU) and right ventral anterior nucleus (VA) when comparing to HCs. Additionally, as compared with MDD-WoA group, the MDD-WA group presented decreased FOCA in the right middle occipital gyrus (MOG), which was also negatively associated with the severity of anhedonia in MDD patients. Finally, FOCA values of the right MOG exhibited excellent discriminant validity in differentiating MDD-WA from MDD-WoA, and the other individual or combined indices of FOCA or lFCD values in the aforementioned distinct brain regions presented significant utility in distinguishing between MDD or MDD-WA and HCs.

CONCLUSIONS: The present findings suggest that aberrant intrinsic brain function in the left CAL, left PoCG, bilateral CAU, right VA, and, especially the right MOG may be associated with anhedonia in patients with MDD. Altered FOCA in the right MOG may have the potential to be a diagnostic neuroimaging biomarker for MDD patients with anhedonia.

PMID:39889674 | DOI:10.1016/j.ajp.2025.104380

Neurology. 2025 Feb 25;104(4):e210271. doi: 10.1212/WNL.0000000000210271. Epub 2025 Jan 31.

ABSTRACT

BACKGROUND AND OBJECTIVES: Alzheimer disease (AD) is defined by cortical β-amyloid (Aβ), tau, and neurodegeneration, which contribute to cognitive decline, in part, by altering large-scale functional brain networks. While cortical Aβ and tau have been associated with changes in functional brain connectivity, it is unknown whether plasma biomarkers relate to such changes. In a healthy community sample of cognitively unimpaired adults free from major CNS disease from the Baltimore Longitudinal Study of Aging, we examined whether plasma biomarkers of AD pathology (Aβ42/40, phosphorylated tau [pTau-181]), astrogliosis (glial fibrillary acidic protein [GFAP]), and neuronal injury (neurofilament light chain [NfL]) were associated with longitudinal changes in functional connectivity and whether changes in functional connectivity were related to longitudinal cognition.

METHODS: Plasma biomarkers were measured using the Quanterix SIMOA assays. Intranetwork connectivity (3T resting-state fMRI) from 7 functional networks was derived using a predefined cortical parcellation mask for each participant visit. Cognitive performance was assessed concurrently with fMRI scan. Covariate-adjusted linear mixed-effect models were used to determine (1) whether plasma biomarkers were associated with longitudinal changes in connectivity, (2) whether the magnitude of the biomarker-connectivity relationships differed by amyloid status, and (3) whether changes in connectivity co-occurred with longitudinal changes in cognition.

RESULTS: Our primary findings (n = 486; age = 65.5 ± 16.2 years; 54% female; mean follow-up time = 4.3 ± 1.7 years) showed that higher baseline GFAP was associated with faster declines in somatomotor (β = -0.04, p = 0.01, 95% CI -0.06 to -0.01), limbic (β = -0.03, p = 0.02, 95% CI -0.06 to -0.005), and frontoparietal (β = -0.04, p = 0.02, 95% CI -0.07 to -0.01) network connectivity. Amyloid status moderated several biomarker-connectivity associations. For instance, higher baseline NfL was related to faster declines in visual and limbic network connectivity, but only among amyloid-positive participants. Among 421 participants with ≥2 fMRI visits (age = 71.7 ± 11.4 years; 55% female; follow-up time = 3.9 ± 1.6 years), longitudinal changes in connectivity were associated with concurrent declines in cognition; however, these results did not survive multiple comparison correction.

DISCUSSION: Among cognitively unimpaired participants, plasma biomarkers of amyloidosis, astrogliosis, and neuronal injury are associated with declines in network connectivity, particularly among amyloid-positive participants. Major limitations include the lack of inclusion of the sensitive pTau-217 and pTau-231 isoforms and comparative PET biomarkers.

PMID:39889254 | DOI:10.1212/WNL.0000000000210271

Eur J Pediatr. 2025 Jan 31;184(2):166. doi: 10.1007/s00431-025-05989-x.

ABSTRACT

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) often co-occur. Developmental stages significantly influence the variations in brain alterations. However, whether ASD comorbid with ADHD (ASD + ADHD) represents a unique neural characteristic from ASD without comorbid ADHD (ASD-alone), or instead manifests a shared neural correlate associated with ASD across diverse age cohorts remain unclear. This study examined topological properties and functional connectivity (FC) patterns through resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange II. Participants were divided into two age cohorts: childhood (under 12 years) and adolescence (12-18 years), consisting of 171 ASD pediatric patients and 111 typically developing (TD) controls. These cohorts were further classified into subgroups of ASD + ADHD, ASD-alone, and TD controls to compare across the age categories. The age, intelligence quotient, and gender of participants across three groups were matched within childhood and adolescence stages. We constructed functional brain networks, conducted graph-theory analysis, and analysed FC for both age cohorts. The findings revealed that both ASD + ADHD and ASD-alone shared some FC dysfunctions in the Default Mode Network (DMN) and atypical global metrics. Additionally, each group demonstrated unique neural FC and topological profiles that evolved with development.

CONCLUSIONS: This study highlights the neural profiles of ASD + ADHD from a developmental perspective and suggests age-considerate approaches in clinical treatments.

WHAT IS KNOWN: • ASD + ADHD shared some neural correlate associated with ASD-alone and also had specific neurobiological mechanisms which were different from ASD-alone. • Developmental stages significantly influence the variations in brain alterations observed in ASD or ADHD.

WHAT IS NEW: • Both ASD + ADHD and ASD-alone shared some FC dysfunctions in the Default Mode Network and atypical global metrics. • ASD + ADHD and ASD-alone demonstrated unique neural FC and topological profiles that evolved with development.

PMID:39888443 | DOI:10.1007/s00431-025-05989-x

Hum Brain Mapp. 2025 Feb 1;46(2):e70125. doi: 10.1002/hbm.70125.

ABSTRACT

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

PMID:39887794 | DOI:10.1002/hbm.70125

Elife. 2025 Jan 31;13:RP95125. doi: 10.7554/eLife.95125.

ABSTRACT

Predicting an individual's cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual's brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual's time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.

PMID:39887179 | DOI:10.7554/eLife.95125

Front Neurol. 2025 Jan 7;15:1511915. doi: 10.3389/fneur.2024.1511915. eCollection 2024.

ABSTRACT

INTRODUCTION: Resting state-fMRI, provides a sensitive method for detecting changes in brain functional integrity, both with respect to regional oxygenated blood flow and whole network connectivity. The primary goal of this report was to examine alterations in functional connectivity in collegiate American football players after a season of repetitive head impact exposure.

METHODS: Collegiate football players completed a rs-fMRI at pre-season and 1 week into post-season. A seed-based functional connectivity method, isolating the posterior cingulate cortex (PCC), was utilized to create individual functional connectivity maps. During group analysis, first, voxel-wise paired sample t-tests identified significant changes in connectivity from pre- to post-season, by player, and previous concussion history. Second, 10 DMN ROIs were constructed by overlaying an anatomical map over regions of positive correlation from one-sample t-tests of pre-season and post-season. These ROIs, plus the LpCun, were included in linear mix-effect modeling, with position or concussion history as covariates.

RESULTS: 66 players were included (mean age 20.6 years; 100% male; 34 (51.5%) non-speed position players). The 10 DMN ROIs showed no alterations from pre-season to post-season. By concussion history, the right temporal ROI demonstrated a significant effect on baseline functional connectivity (p = 0.03). Speed players, but not non-speed players, demonstrated a significant decrease in functional connectivity in the precuneus from pre- to post-season (p < 0.001).

DISCUSSION: There are region-specific differences functional connectivity related to both position and concussion history in American collegiate football players. Player position affected functional connectivity across a season of football. Position-specific differences in head impact exposure rate and magnitude plays a crucial role in functional connectivity alterations.

PMID:39882371 | PMC:PMC11776490 | DOI:10.3389/fneur.2024.1511915

Front Aging Neurosci. 2025 Jan 15;16:1497874. doi: 10.3389/fnagi.2024.1497874. eCollection 2024.

ABSTRACT

BACKGROUND AND PURPOSE: Asymptomatic carotid stenosis (ACS) is an independent risk factor for ischemic stroke and vascular cognitive impairment, affecting cognitive function across multiple domains. This study aimed to explore differences in static and dynamic intrinsic functional connectivity and temporal dynamics between patients with ACS and those without carotid stenosis.

METHODS: We recruited 30 patients with unilateral moderate-to-severe (stenosis ≥ 50%) ACS and 30 demographically-matched healthy controls. All participants underwent neuropsychological testing and 3.0T brain MRI scans. Resting-state functional MRI (rs-fMRI) was used to calculate both static and dynamic functional connectivity. Dynamic independent component analysis (dICA) was employed to extract independent circuits/networks and to detect time-frequency modulation at the circuit level. Further imaging-behavior associations identified static and dynamic functional connectivity patterns that reflect cognitive decline.

RESULTS: ACS patients showed altered functional connectivity in multiple brain regions and networks compared to controls. Increased connectivity was observed in the inferior parietal lobule, frontal lobe, and temporal lobe. dICA further revealed changes in the temporal frequency of connectivity in the salience network. Significant differences in the temporal variability of connectivity were found in the fronto-parietal network, dorsal attention network, sensory-motor network, language network, and visual network. The temporal parameters of these brain networks were also related to overall cognition and memory.

CONCLUSIONS: These results suggest that ACS involves not only changes in the static large-scale brain network connectivity but also dynamic temporal variations, which parallel overall cognition and memory recall.

PMID:39881682 | PMC:PMC11774917 | DOI:10.3389/fnagi.2024.1497874

J Neurosci. 2025 Jan 29:e1570242025. doi: 10.1523/JNEUROSCI.1570-24.2025. Online ahead of print.

ABSTRACT

Time persistence is a fundamental property of many complex physical and biological systems; thus understanding the phenomenon in the brain is of high importance. Time persistence has been explored at the level of stand-alone neural time-series, but since the brain functions as an interconnected network, it is essential to examine time persistence at the network level. Changes in resting-state networks have been previously investigated using both dynamic (i.e., examining connectivity states) and static functional connectivity (i.e., test-retest reliability), but no systematic investigation of the time persistence as a network was conducted, particularly across different time-scales (i.e., seconds, minutes, dozens of seconds, days) and different brain subnetworks. Additionally, individual differences in network time persistence have not been explored. Here, we devised a new framework to estimate network time persistence at both the link (i.e., connection) and node levels. In a comprehensive series analysis of three functional MRI (fMRI) resting-state datasets including both sexes, we established that: a) The resting-state functional brain network becomes gradually less similar to itself for the gaps up to 23 minutes within the run and even less similar for the gap between the days; b) Network time persistence varies across functional networks, while the sensory networks are more persistent than non-sensory networks; c) Participants show stable individual characteristic persistence, which has a genetic component; and d) Individual characteristic persistence could be linked to behavioral performance. Overall, our detailed characterization of network time persistence sheds light on the potential role of time persistence in brain functioning and cognition.Significance statement Time persistence - how long the system stays in a certain configuration - is a fundamental characteristic property of a variety of complex physical and biological systems. To date, the network time persistence of the brain is not sufficiently well understood. Here, we introduce and test a novel framework to quantify brain network time persistence. We found that the functional brain network becomes gradually less similar within the run (up to 23 minutes) and even less similar between days. The participants showed stable individual characteristic persistence, which has a genetic component. In addition, individual characteristic persistence could be linked to behavioral performance. Thus, brain network time persistence may play a key role in brain functioning and human cognition.

PMID:39880677 | DOI:10.1523/JNEUROSCI.1570-24.2025

Brain Res Bull. 2025 Jan 27:111229. doi: 10.1016/j.brainresbull.2025.111229. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the differences in brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis (ALS-L and ALS-B, respectively) patients using resting-state functional MRI (rs-fMRI) with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo).

MATERIALS AND METHODS: The rs-fMRI data were collected from 41 ALS patients (11 ALS-B and 30 ALS-L) and 25 healthy controls (HC). ALFF and ReHo values were calculated, and group differences were assessed using one-way ANCOVA and two-sample t-tests. Correlation analyses with clinical measures were conducted. Support vector machine (SVM) analysis was performed to distinguish ALS subtypes.

RESULTS: Compared with ALS-L, ALS-B showed increased ALFF values in the right gyrus rectus/ orbital part of right middle frontal gyrus, orbital part of left middle frontal gyrus and left dorsolateral superior frontal gyrus/ left medial superior frontal gyrus and decreased ALFF values in the left superior occipital gyrus (FDR-corrected, P < 0.05). Both ALS subtypes demonstrated distinct ALFF alterations compared to HC. Differences in ReHo values were only found between ALS-B and HC. Correlation analyses revealed associations between ALFF in specific brain regions and ALS clinical scores. SVM analysis achieved an accuracy of 90.2%, with an AUC of 0.909 in differentiating ALS-B and ALS-L.

CONCLUSION: ALS-B and ALS-L patients had distinct alterations in brain spontaneous neural activity, which could serve as potential biomarkers for accurately distinguishing these two subtypes. Our findings offer a new insight into the neural mechanism of ALS, underscoring the importance of personalized diagnostic approaches for this complex neurological disorder.

PMID:39880289 | DOI:10.1016/j.brainresbull.2025.111229

Neurobiol Aging. 2025 Jan 26;148:16-26. doi: 10.1016/j.neurobiolaging.2025.01.005. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is pathologically marked by tau tangles and beta-amyloid (Aβ) plaques. It has been hypothesized that Aβ facilitates spread of tau outside of the medial temporal lobe (MTL), but exact mechanism of this facilitation remains unclear. We aimed to test the hypothesis that abnormal Aβ induces an increase in inter-network functional connectivity, which in turn induces early-stage tau elevation in limbic network. Our study used 18F-Florbetaben Aβ positron emission tomography (PET), 18F-MK6240 tau-PET, and resting-state functional magnetic resonance imaging (rs-fMRI) from 489 healthy unimpaired older adults, including 46 with longitudinal data. We found significant correlations between tau in limbic network and Aβ in distinct functional networks. We then demonstrated that Aβ+ /Tau- participants exhibited elevated inter-network functional connectivity of the limbic network. Finally, our longitudinal results showed that annual increases in inter-network functional connectivity between limbic network and default mode and control networks were linked to annual tau elevation in limbic network, primarily modulated by Aβ+ individuals. Understanding this early brain alteration in response to pathologies could guide treatments early in disease course.

PMID:39879839 | DOI:10.1016/j.neurobiolaging.2025.01.005

Int J Clin Health Psychol. 2025 Jan-Mar;25(1):100541. doi: 10.1016/j.ijchp.2024.100541. Epub 2025 Jan 10.

ABSTRACT

OBJECTIVE: The vicious circle model of obesity proposes that the hippocampus plays a crucial role in food reward processing and obesity. However, few studies focused on whether and how pediatric obesity influences the potential direction of information exchange between the hippocampus and key regions, as well as whether these alterations in neural interaction could predict future BMI and eating behaviors.

METHODS: In this longitudinal study, a total of 39 children with excess weight (overweight/obesity) and 51 children with normal weight, aged 8 to 12, underwent resting-state fMRI. One year later, we conducted follow-up assessments of eating behaviors and BMI. Resting-state functional connectivity and spectral dynamic casual modeling (spDCM) technique were used to examine altered functional and effective connectivity (EC) of the hippocampus in children with overweight/obesity. Linear support vector regression, a machine learning method, was employed to further investigate whether these sensitive hippocampal connections at baseline could predict future BMI and eating behaviors.

RESULTS: Compared to controls, children with excess weight displayed abnormal bidirectional inhibitory effects between the right hippocampus and left postcentral gyrus (PoCG), that is, stronger inhibitory hippocampus→PoCG EC but weaker inhibitory PoCG→hippocampus EC, which further predicted BMI and food approach behavior one year later.

CONCLUSION: These findings point to a particularly important role of abnormal information exchange between the hippocampus and somatosensory cortex in pediatric obesity and future food approach behavior, which provide novel insights into the neural hierarchical mechanisms underlying childhood obesity and further expand the spDCM model of adult obesity by identifying the directionality of abnormal influences between crucial circuits associated with appetitive regulation.

PMID:39877891 | PMC:PMC11773239 | DOI:10.1016/j.ijchp.2024.100541

Front Neurosci. 2025 Jan 14;18:1511183. doi: 10.3389/fnins.2024.1511183. eCollection 2024.

ABSTRACT

BACKGROUND: Acupuncture has been demonstrated to have a promising effect on Alzheimer's disease (AD), but the underlying neural mechanisms remain unclear. The retrosplenial cortex (RSC) is one of the earliest brain regions affected in AD, and changes in its functional connectivity (FC) are reported to underlie disease-associated memory impairment. The aim of this study was to examine the effect of acupuncture on FC with the RSC in patients with AD.

METHODS: Demographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging (fMRI) data were collected from 14 AD patients and 14 normal controls (NCs) matched by age, sex, and educational level at baseline. After the baseline MRI scan, acupuncture stimulation on the Taichong (LIV3) and Hegu (LI4) points was performed for 3 min. Then, another 10 min of fMRI data were acquired after the needle was withdrawn. A dataset that included 100 healthy participants was also included to construct a reliable FC map of the RSC. Two sets of regions of interest (ROIs) in the RSC were selected to assess the sustained effect of acupuncture on FC with the RSC in AD patients and NCs.

RESULTS: Two sets of RSC ROI-based analyses demonstrated robust positive connectivity with the hippocampus (HPC). Furthermore, multiple brain regions, including the bilateral thalamus, bilateral posterior cingulate cortex (PCC), bilateral subcallosal cingulate gyrus (SCG), bilateral orbitofrontal cortex (OFC), and right precuneus, showed decreased FC with the RSC in the AD group and increased FC with the RSC in the NC group after acupuncture compared to that at baseline. Acupuncture also specifically elicited increased FC between the RSC and the HPC as well as between the RSC and the parahippocampal gyrus in AD patients and decreased FC between the RSC and the visual cortices in NCs. Additionally, diminished FC with the RSC was correlated with neuropsychological scale scores in the AD group before acupuncture treatment.

CONCLUSION: These findings confirm and extend previous studies suggesting that acupuncture at Taichong (LIV3) and Hegu (LI4) can exert bidirectional and benign regulatory effects on RSC connectivity in AD patients.

PMID:39877657 | PMC:PMC11772287 | DOI:10.3389/fnins.2024.1511183

J Imaging Inform Med. 2025 Jan 28. doi: 10.1007/s10278-025-01399-5. Online ahead of print.

ABSTRACT

Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction. MHNet has two branches: the Euclidean Space Features Extraction (ESFE) module and the Non-Euclidean Space Features Extraction (Non-ESFE) module, followed by a Feature Fusion-based Classification (FFC) module for NDD identification. ESFE includes a Functional Connectivity Generation (FCG) module and a High-order Convolutional Neural Network (HCNN) module to extract local and high-order features from BFNs in Euclidean space. Non-ESFE comprises a Generic Internet-like Brain Hierarchical Network Generation (G-IBHN-G) module and a High-order Graph Neural Network (HGNN) module to capture topological and high-order features in non-Euclidean space. Experiments on three public datasets show that MHNet outperforms state-of-the-art methods using both AAL1 and Brainnetome Atlas templates. Extensive ablation studies confirm the superiority of MHNet and the effectiveness of using multi-view fMRI information and high-order features. Our study also offers atlas options for constructing more sophisticated hierarchical networks and explains the association between key brain regions and NDD. MHNet leverages multi-view feature learning from both Euclidean and non-Euclidean spaces, incorporating high-order information from BFNs to enhance NDD classification performance.

PMID:39875742 | DOI:10.1007/s10278-025-01399-5

Neuroimage. 2025 Jan 26:121052. doi: 10.1016/j.neuroimage.2025.121052. Online ahead of print.

ABSTRACT

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

PMID:39875038 | DOI:10.1016/j.neuroimage.2025.121052