Most recent paper

Front Hum Neurosci. 2025 Jan 6;18:1482353. doi: 10.3389/fnhum.2024.1482353. eCollection 2024.

ABSTRACT

Interest has been growing in the use of mindfulness meditation (MM) as a therapeutic practice, as accumulating evidence highlights its potential to effectively address a range of mental conditions. While many fMRI studies focused on neural activation and functional connectivity during meditation, the impact of long-term MM practice on spontaneous brain activity, and on the expression of resting state networks over time, remains unclear. Here, intrinsic functional network dynamics were compared between experienced meditators and meditation-naïve participants during rest. Our analysis revealed that meditators tend to spend more time in two brain states that involve synchrony among cortical regions associated with sensory perception. Conversely, a brain state involving frontal areas associated with higher cognitive functions was detected less frequently in experienced meditators. These findings suggest that, by shifting attention toward enhanced sensory and embodied processing, MM effectively modulates the expression of functional network states at rest. These results support the suggested lasting effect of long-term MM on the modulation of resting-state networks, reinforcing its therapeutic potential for disorders characterized by imbalanced network dynamics. Moreover, this study reinforces the utility of analytic approaches from dynamical systems theory to extend current knowledge regarding brain activity and evaluate its response to interventions.

PMID:39834400 | PMC:PMC11743700 | DOI:10.3389/fnhum.2024.1482353

BMC Psychiatry. 2025 Jan 21;25(1):59. doi: 10.1186/s12888-025-06497-w.

ABSTRACT

BACKGROUND: Depersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.

METHODS: A total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within-and between-network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large-scale brain networks and scores of clinical assessments in DPRD patients.

RESULTS: Compared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.

CONCLUSION: Overall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.

PMID:39833729 | DOI:10.1186/s12888-025-06497-w

bioRxiv [Preprint]. 2025 Jan 7:2025.01.06.631503. doi: 10.1101/2025.01.06.631503.

ABSTRACT

The development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising diffusion probabilistic models (DDPMs). This approach builds upon and extends traditional linear methods, such as independent component analysis (ICA), which are commonly used in neuroimaging studies. A central contribution of our work is the integration of attention mechanisms into conditional DDPMs, enabling the generation of subject-specific 3D ICNs. Conditioning the resting-state fMRI (rs-fMRI) data on the corresponding ICNs enables the extraction of individualized brain connectivity patterns, effectively capturing within-subject and between-subject variability. Unlike prior models limited to 2D visualization, this framework generates 3D representations, providing a more comprehensive depiction of ICNs. The model's performance is validated on an external dataset to prevent over-fitting and for overall generalizability. Furthermore, comparative evaluations also demonstrate that the proposed DDPM-based approach outperforms state-of-the-art generative models in producing more detailed and accurate ICNs, as validated through qualitative assessments.

PMID:39829795 | PMC:PMC11741255 | DOI:10.1101/2025.01.06.631503

Acad Radiol. 2025 Jan 18:S1076-6332(24)00666-4. doi: 10.1016/j.acra.2024.09.017. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Cognitive disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD), significantly impact the quality of life in older adults. Mild cognitive impairment (MCI) is a critical stage for intervention and can predict the development of dementia. The causes of these two diseases are not fully understood, but there is an overlap in their neuropathology. There is a lack of direct comparison regarding the changes in functional connectivity within and between different brain networks during cognitive impairment in these two diseases.

OBJECTIVE: This study aims to investigate changes in brain network connectivity of AD and PD with mild cognitive impairment, shedding light on the underlying neuropathological mechanisms and potential treatment options.

METHODS: A total of 33 AD-MCI patients, 55 PD-MCI patients, and 34 healthy controls (HCs) underwent resting-state functional MRI and cognitive function assessment using Independent Components Analysis (ICA). We compared intra- and inter-network functional connectivity among the three groups and analyzed the correlation between changes in functional connectivity and cognitive domain performance.

RESULTS: Using ICA, we identified eight functional networks. In the AD-MCI group, reductions in internetwork functional connectivity were mainly around the default mode network (DMN). Intra-network functional connectivity was widely reduced, especially in the DMN, while intra-network functional connectivity in the Salience Network (SN) increased. In contrast, in the PD-MCI group, reductions in internetwork functional connectivity were mainly around the SN. Intra-network functional connectivity in the SN decreased, while intra-network functional connectivity in other networks increased.

CONCLUSION: This study highlights distinct yet overlapping changes in brain network connectivity in AD and PD, providing new insights into the underlying mechanisms of cognitive impairment disorders.

PMID:39828502 | DOI:10.1016/j.acra.2024.09.017

Pharmacol Res. 2025 Jan 16:107609. doi: 10.1016/j.phrs.2025.107609. Online ahead of print.

ABSTRACT

Post-surgical pain affects millions each year, hindering recovery and quality of life. Surgical procedures cause tissue damage and inflammation, leading to peripheral and central sensitization, resulting in pain at rest or hyperalgesia to mechanical stimuli, among others. In a rat model for post-surgical pain, spinal GABAergic transmission via GABAA receptors reduces mechanical hypersensitivity but has no effect on pain at rest. While fMRI studies show consistent brain activity changes during mechanical stimulation in post-surgical pain, central processing of pain at rest and the role of spinal GABAergic circuits on surgical pain processing is currently unclear. The aim of this study was to evaluate the influence of an acute surgical incision, a proxy for post-surgical pain, on the cerebral processing of pain at rest and mechanical hypersensitivity, and to assess the influence of spinal GABAA-circuits on this processing. In rats, a unilateral incision injury affected sensorimotor and thalamo-limbic subnetworks at rest and following mechanical stimulation, indicating changes in neural processing relevant to pain at rest and mechanical hypersensitivity in post-surgical pain. Enhancing spinal GABAergic tone increased functional connectivity (FC) in parts of these subnetworks during mechanical stimulation, but not at rest, highlighting spino-cerebral interactions in post-surgical pain regulation relevant for mechanical hypersensitivity and potentially the development of chronic pain after surgery but likely not for pain at rest. These findings underscore the complex and interconnected nature of brain networks in post-surgical pain processing, and provide insights into potential spinal targets for pharmacological intervention to alleviate post-surgical pain and prevent it's chronification.

PMID:39826820 | DOI:10.1016/j.phrs.2025.107609

Neuroimage. 2025 Jan 16:121030. doi: 10.1016/j.neuroimage.2025.121030. Online ahead of print.

ABSTRACT

INTRODUCTION: Chronic exercise has been linked to structural and functional changes in the hippocampus and surrounding areas. However, less is known about how a single session of exercise can induce immediate effects that may contribute to these longterm changes.

OBJECTIVE/METHODS: Resting-state fMRI was used to investigate changes in brain networks 19 minutes after a 20-minute bout of vigorous-intensity acute exercise. Fortyseven healthy young adults, aged 18-29, were recruited for the study.

RESULTS: Whole-brain Independent Component Analysis revealed that only the medialtemporal network-including the bilateral hippocampus, amygdala, anterior temporal lobe, and parahippocampus-exhibited a reduction in intrinsic functional connectivity. All other brain networks remained unchanged. This reduction occurred specifically during the period following exercise and became less pronounced as more time elapsed since its completion. Additionally, the significance of this change was assessed using various correlates. The reduction was less pronounced in participants with higher levels of physical fitness, better performance in post-exercise memory tests, or a more positive post-exercise affective state compared to baseline.

CONCLUSIONS: A single bout of exercise leads to specific functional changes in the medial temporal network, which may be related to individual differences in the chronic changes resulting from repeated exercise bouts over time.

PMID:39826771 | DOI:10.1016/j.neuroimage.2025.121030

BMC Psychiatry. 2025 Jan 17;25(1):43. doi: 10.1186/s12888-025-06485-0.

ABSTRACT

BACKGROUND: Although childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.

METHODS: This study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression. First, we employed Network-Based Statistics (NBS) on resting-state functional magnetic resonance imaging (fMRI) data to identify functional connectivity (FC) patterns associated with depression and aggression. Mediation analyses were then conducted to assess the role of these FC patterns in the relationship between childhood maltreatment and each outcome.

RESULTS: The results demonstrated that FC within the default mode network (DMN) and between the cingulo-opercular network (CON) and dorsal attention network (DAN) mediated the association between childhood maltreatment and aggression, whereas FC within the reward system and between the CON and the reward system mediated the link between childhood maltreatment and depression.

CONCLUSIONS: We speculate that the control system may serve as a transdiagnostic neural basis accounting for the sequela of childhood maltreatment, and the attention network and the reward network may act as specific neural basis linking childhood maltreatment to depression and aggression, respectively.

PMID:39825275 | DOI:10.1186/s12888-025-06485-0

J Affect Disord. 2025 Jan 15:S0165-0327(25)00087-4. doi: 10.1016/j.jad.2025.01.071. Online ahead of print.

ABSTRACT

OBJECTIVES: Major depressive disorder (MDD) in adolescents is associated with an increased risk of suicide, and electroconvulsive therapy (ECT) is an effective treatment for MDD and suicidal ideation. To investigate underlying central mechanisms, this study examined functional connectome topological organization in adolescents with MDD and suicidal ideation prior to and following ECT.

METHODS: Resting-state fMRI images were collected from 28 adolescents with MDD and suicidal ideation and 31 demographically similar healthy adolescents. Whole-brain functional networks were constructed and topological metrics were analyzed using graph theory approaches.

RESULTS: Prior to ECT, depressed adolescents showed disrupted global and nodal properties, indicating altered functional connectivity. Following ECT, significant reductions in depression and suicidality symptoms were observed, with a 75 % response rate. ECT led to an increase in the small-worldness of the brain network, suggesting restoration of functional connectivity. Significant improvements were seen in nodal properties, particularly in the central executive network. Group-by-time interactions revealed differences between responders and non-responders in nodal degree and efficiency.

LIMITATIONS: Larger sample sizes and extended followed-up periods following ECT treatment are needed to further investigate the neural basis of clinical changes.

CONCLUSION: The results of this study reveal dynamic changes in brain network topology of adolescents with depression during the course of ECT, and have an advanced understanding of the neurobiological biomarkers associated with the efficacy of ECT treatment.

PMID:39824319 | DOI:10.1016/j.jad.2025.01.071

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Jan 15:S2451-9022(25)00029-1. doi: 10.1016/j.bpsc.2024.12.017. Online ahead of print.

ABSTRACT

BACKGROUND: Although combat-deployed soldiers are at a high risk for developing trauma-related psychopathology, most will remain resilient for the duration and aftermath of their deployment tour. The neural basis of this type of resilience is largely unknown, and few longitudinal studies exist on neural adaptation to combat in resilient individuals for whom a pre-exposure measurement was collected. Here, we delineate changes in the architecture of functional brain networks from pre- to post-combat in psychopathology-free, resilient participants.

METHODS: Tier 1 infantry recruits (n=50) participated in this longitudinal functional MRI (fMRI) study, along with a comparison group of university students (n=50). Changes in within- and between-network functional connectivity as a function of exposure group were analyzed.

RESULTS: Significant group-by-time interactions manifested in the default mode, cognitive control, and ventral attention networks: significant increases from baseline, in both within- and between-network connectivity, were noted post-deployment in soldiers only.

CONCLUSIONS: These results indicate global changes in brain functional architecture in resilient combat-deployed participants relative to age-matched students, suggesting that neural adaptation may support resilience to combat exposure.

CLINICALTRIALS: gov Identifier: NCT04651192; https://clinicaltrials.gov/study/NCT04651192.

PMID:39824285 | DOI:10.1016/j.bpsc.2024.12.017

J Clin Neurosci. 2025 Jan 16;133:111053. doi: 10.1016/j.jocn.2025.111053. Online ahead of print.

ABSTRACT

BACKGROUND: Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connectivity, particularly within the context of static and dynamic functional network connectivity (sFNC and dFNC), may provide valuable insights into disease pathophysiology. This study investigates brain-wide connectivity alterations in CSM patients using both sFNC and dFNC, combined with machine learning approaches, to explore their potential as biomarkers for disease classification and progression.

METHODS: A total of 191 participants were included in this study, comprising 108 CSM patients and 83 healthy controls (HCs). Resting-state fMRI data were used to derive functional connectivity networks (FCNs), which were further analyzed to obtain sFNC and dFNC features. K-means clustering was applied to identify distinct dFNC states, and machine learning models, including support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), logistic regression (LR), and random forests (RF), were constructed to classify CSM patients and HCs based on FNC features.

RESULTS: The sFNC analysis revealed significant alterations in brain network connectivity in CSM patients, including enhanced connectivity between the posterior default mode network (pDMN) and ventral attention network (vAN), and between the right and left frontoparietal networks (rFPN and lFPN), alongside weakened connectivity in multiple other network pairs. K-means clustering of dFNC identified four distinct functional states, with CSM patients exhibiting altered connectivity in State 1 and State 3. Machine learning models based on sFNC demonstrated excellent classification performance, with the SVM model achieving an AUC of 0.92, accuracy of 85.86%, and sensitivity and specificity both exceeding 0.80. Models based on dFNC also performed well, with the State 3-based model yielding an AUC of 0.91 and accuracy of 84.97%.

CONCLUSIONS: Our findings highlight significant alterations in both sFNC and dFNC in CSM patients, suggesting that these connectivity changes may reflect underlying neural mechanisms of the disease. Machine learning models based on FNC features, particularly SVM, exhibit strong potential for classifying CSM patients and may serve as valuable neuroimaging biomarkers for diagnosis and monitoring disease progression. Future research should explore longitudinal studies and multimodal neuroimaging approaches to further validate these findings.

PMID:39823911 | DOI:10.1016/j.jocn.2025.111053

Eur Child Adolesc Psychiatry. 2025 Jan 17. doi: 10.1007/s00787-025-02643-7. Online ahead of print.

ABSTRACT

Important associations between emotional dysregulation (ED) and ADHD have been identified in adults, with a key manifestation of this being differential use of emotion regulation strategies: reduced use of cognitive reappraisal (CR), but elevated expressive suppression (ES). These associations have been observed at both behavioral and neuroimaging levels. The present study aims to explore the use of CR and ES in children with ADHD, and their relationship to ED. 148 children with ADHD and 265 healthy controls (age 9-16 years) were recruited and evaluated and correlated their ED, CR, and ES. Resting-state fMRI functional connectivity, with 6 amygdala subregions as regions-of-interest, were analyzed in a subsample to identify potential neural correlates. Children with ADHD showed significant higher ED, and lower use of both CR and ES. A significant negative correlation was found between CR and ED. Mediation analysis indicated that CR has an indirect influence on the relationship between ADHD diagnosis and ED. In the neuroimaging analyses, the functional connectivity between the right superficial amygdala and left middle occipital gyrus showed a significant group-by-ES interaction, highlighting potential neural correlates for elevated ED in children with ADHD. Children with ADHD expressed elevated levels of ED, and used less CR and ES compared to healthy controls. The lower use of ES may relate to abnormal amygdala connectivity in children with ADHD. This finding suggested that brain immaturity in children may preclude effective deployment of ES in emotion regulation processes.

PMID:39821692 | DOI:10.1007/s00787-025-02643-7

JAMA Netw Open. 2025 Jan 2;8(1):e2455165. doi: 10.1001/jamanetworkopen.2024.55165.

ABSTRACT

IMPORTANCE: Opioid use disorder (OUD) impacts millions of people worldwide. Prior studies investigating its underpinning neural mechanisms have not often considered how brain signals evolve over time, so it remains unclear whether brain dynamics are altered in OUD and have subsequent behavioral implications.

OBJECTIVE: To characterize brain dynamic alterations and their association with cognitive control in individuals with OUD.

DESIGN, SETTING, AND PARTICIPANTS: This case-control study collected functional magnetic resonance imaging (fMRI) data from individuals with OUD and healthy control (HC) participants. The study was performed at an academic research center and an outpatient clinic from August 2019 to May 2024.

EXPOSURE: Individuals with OUD were all recently stabilized on medications for OUD (<24 weeks).

MAIN OUTCOMES AND MEASURES: Recurring brain states supporting different cognitive processes were first identified in an independent sample with 390 participants. A multivariate computational framework extended these brain states to the current dataset to assess their moment-to-moment engagement within each individual. Resting-state and naturalistic fMRI investigated whether brain dynamic alterations were consistently observed in OUD. Using a drug cue paradigm in participants with OUD, the association between cognitive control and brain dynamics during exposure to opioid-related information was studied. Variations in continuous brain state engagement (ie, state engagement variability [SEV]) were extracted during resting-state, naturalistic, and drug-cue paradigms. Stroop assessed cognitive control.

RESULTS: Overall, 99 HC participants (54 [54.5%] female; mean [SD] age, 31.71 [12.16] years) and 76 individuals with OUD (31 [40.8%] female; mean [SD] age, 39.37 [10.47] years) were included. Compared with HC participants, individuals with OUD demonstrated consistent SEV alterations during resting-state (99 HC participants; 71 individuals with OUD; F4,161 = 6.83; P < .001) and naturalistic (96 HC participants; 76 individuals with OUD; F4,163 = 9.93; P < .001) fMRI. Decreased cognitive control was associated with lower SEV during the rest period of a drug cue paradigm among 70 participants with OUD. For example, lower incongruent accuracy scores were associated with decreased transition SEV (ρ58 = 0.34; P = .008).

CONCLUSIONS AND RELEVANCE: In this case-control study of brain dynamics in OUD, individuals with OUD experienced greater difficulty in effectively engaging various brain states to meet changing demands. Decreased cognitive control during the rest period of a drug cue paradigm suggests that these individuals had an impaired ability to disengage from opioid-related information. The current study introduces novel information that may serve as groundwork to strengthen cognitive control and reduce opioid-related preoccupation in OUD.

PMID:39821393 | DOI:10.1001/jamanetworkopen.2024.55165

Child Neuropsychol. 2025 Jan 17:1-21. doi: 10.1080/09297049.2025.2451799. Online ahead of print.

ABSTRACT

The current study investigated the relationship between adverse childhood experiences (ACEs), post-traumatic stress disorder (PTSD) symptoms, within-network resting-state functional connectivity (rs-FC), and alcohol use during adolescence using functional magnetic resonance imaging (fMRI) data from the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA; N = 687). Significant rs-FC differences emerged that linked participant ACEs, PTSD symptoms, and alcohol use problems. Participants with ACEs compared to those without had diminished rs-FC within the default mode, salience, and medial frontoparietal networks (p ≤ 0.005). Further reduction in rs-FC within the default mode and medial frontoparietal networks (p ≤ 0.005) was found when PTSD symptoms were present in addition to ACEs. Findings suggest that PTSD symptoms are associated with lower within network rs-FC beyond exposure to ACEs, and some of these rs-FC changes were associated with worsened alcohol use problems (i.e. withdrawal symptoms). These findings highlight the importance of addressing PTSD symptoms in adolescents with a history of ACEs as it may mitigate problematic changes in brain connectivity and reduce the risk of developing alcohol use problems.

PMID:39819312 | DOI:10.1080/09297049.2025.2451799

J Affect Disord. 2025 Jan 14:S0165-0327(25)00083-7. doi: 10.1016/j.jad.2025.01.066. Online ahead of print.

ABSTRACT

BACKGROUND: Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but the underlying neuromodulatory mechanisms remain largely unknown. Functional stability represents a newly developed method based on the dynamic functional connectivity framework. This study aimed to explore ECT-evoked changes in functional stability and their relationship with clinical outcomes.

METHODS: We collected longitudinal resting-state fMRI data from 58 MDD patients (39 of whom experienced remission after ECT, and 19 did not, referred to as remitters and non-remitters, respectively) and 42 age- and sex-matched healthy controls. We utilized voxel-level whole-brain functional stability analysis to examine the neural effects of ECT in MDD patients.

RESULTS: After ECT, MDD patients showed increased functional stability in the bilateral middle frontal gyrus, orbital part, and bilateral angular gyrus as well as decreased functional stability in the right fusiform gyrus. Additionally, the subgroup analysis revealed that functional stability of the right hippocampus significantly decreased in remitters yet significantly increased in non-remitters after ECT.

CONCLUSIONS: Our data demonstrated the modulatory effect of ECT on brain functional stability in MDD patients and further revealed the differences in this modulation between patients with and without clinical remission, highlighting the potential usefulness of functional stability as a prognostic biomarker for monitoring ECT efficacy and stratifying MDD patients to optimize treatment strategies.

PMID:39818339 | DOI:10.1016/j.jad.2025.01.066

J Affect Disord. 2025 Jan 14:S0165-0327(25)00078-3. doi: 10.1016/j.jad.2025.01.061. Online ahead of print.

ABSTRACT

AIM: Major depressive disorder (MDD) is characterized by altered activity in various higher-order regions like the anterior cingulate and prefrontal cortex. While some findings also show changes in lower-order sensory regions like the occipital cortex in MDD, the latter's exact neural and temporal, e.g., dynamic characterization and symptom severity remains yet unclear.

METHODS: We conducted resting state fMRI in MDD (N = 49) and healthy controls to investigate the global activity representation of the brain's spontaneous activity in occipital cortex including lower-order (V1) and higher-order (hMT+) regions in the hierarchy of the visual cortex. We further explored (i) these regions' functional connectivity to higher-order prefrontal and subcortical regions, (ii) global signal correlation differences between MDD and controls in different frequency bands, and (iii) their power spectrum's correlation (using median frequency/MF) with symptom severity.

RESULTS: Our findings in MDD show: (i) abnormally high functional connectivity of the occipital cortex to both subcortical and higher-order cortical regions; (ii) occipital global signal correlation is reduced mainly in the faster infraslow frequency range (slow 3: 0.073 to 0.198 Hz) as distinguished from the slower ones (slow 5 and 4: 0.01 to 0.027 Hz, and 0.027 to 0.073 Hz); (iii) the reduced neural dynamics in occipital cortex (MF) correlate with the severity of both overall depressive symptoms and psychomotor retardation scores.

CONCLUSIONS: MDD shows reduced global activity with abnormally slow neural dynamics in occipital cortex that is functionally connected with higher-order regions like the anterior cingulate cortex. The slow dynamics in occipital cortex relates to overall symptom severity and psychomotor retardation.

PMID:39818334 | DOI:10.1016/j.jad.2025.01.061

Front Child Adolesc Psychiatry. 2024 Nov 5;3:1481957. doi: 10.3389/frcha.2024.1481957. eCollection 2024.

ABSTRACT

INTRODUCTION: Autism Spectrum Disorder (ASD) is characterized by deficits in social cognition, self-referential processing, and restricted repetitive behaviors. Despite the established clinical symptoms and neurofunctional alterations in ASD, definitive biomarkers for ASD features during neurodevelopment remain unknown. In this study, we aimed to explore if activation in brain regions of the default mode network (DMN), specifically the medial prefrontal cortex (MPC), posterior cingulate cortex (PCC), superior temporal sulcus (STS), inferior frontal gyrus (IFG), angular gyrus (AG), and the temporoparietal junction (TPJ), during resting-state functional magnetic resonance imaging (rs-fMRI) is associated with possible phenotypic features of autism (PPFA) in a large, diverse youth cohort.

METHODS: We used cross-sectional parent-reported PPFA data and youth rs-fMRI brain data as part of the two-year follow-up of the Adolescent Brain Cognitive Development (ABCD) study. Our sample consisted of 7,106 (53% male) adolescents aged 10-13. We conducted confirmatory factor analyses (CFAs) to establish the viability of our latent measurements: features of autism and regional brain activation. Structural regression analyses were used to investigate the associations between the six brain regions and the PPFA.

RESULTS: We found that activation in the MPC (β = .16, p < .05) and the STS (β = .08, p < .05), and being male (β = .13, p < .05), was positively associated with PPFA. In contrast, activation in the IFG (β = -.08, p < .05) was negatively associated.

DISCUSSION: Our findings suggest that regions of the "social brain" are associated with PPFA during early adolescence. Future research should characterize the developmental trajectory of social brain regions in relation to features of ASD, specifically brain regions known to mature relatively later during development.

PMID:39816599 | PMC:PMC11731829 | DOI:10.3389/frcha.2024.1481957

Heliyon. 2024 Jul 10;10(14):e34245. doi: 10.1016/j.heliyon.2024.e34245. eCollection 2024 Jul 30.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge. In this regard, this work proposes a model featuring a dual-path cross-attention framework for spatial and temporal patterns, named STDCformer, aiming to enhance the accuracy of ASD identification. STDCformer can preserve both temporal-specific patterns and spatial-specific patterns while explicitly interacting spatiotemporal information in depth. The embedding layer of the STDCformer embeds temporal and spatial patterns in dual paths. For the temporal path, we introduce a perturbation positional encoding to improve the issue of signal misalignment caused by individual differences. For the spatial path, we propose a correlation metric based on Gramian angular field similarity to establish a more specific whole-brain functional network. Subsequently, we interleave the query and key vectors of dual paths to interact spatial and temporal information. We further propose integrating the dual-path attention into a tensor that retains spatiotemporal dimensions and utilizing 2D convolution for feed-forward processing. Our attention layer allows the model to represent spatiotemporal correlations of signals at multiple scales to alleviate issues of information distortion and loss. Our STDCformer demonstrates competitive results compared to state-of-the-art methods on the ABIDE dataset. Additionally, we conducted interpretative analyses of the model to preliminarily discuss the potential physiological mechanisms of ASD. This work once again demonstrates the potential of deep learning technology in identifying ASD and developing neuroimaging biomarkers for ASD.

PMID:39816341 | PMC:PMC11734066 | DOI:10.1016/j.heliyon.2024.e34245

Cogn Neurodyn. 2025 Dec;19(1):22. doi: 10.1007/s11571-024-10202-0. Epub 2025 Jan 13.

ABSTRACT

Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.

PMID:39816217 | PMC:PMC11729590 | DOI:10.1007/s11571-024-10202-0

Cogn Affect Behav Neurosci. 2025 Jan 15. doi: 10.3758/s13415-024-01240-6. Online ahead of print.

ABSTRACT

In a sequence, at least two aspects of information-the identity of items and their serial order-are maintained and supported by distinct working memory (WM) capacities. Verbal serial order WM is modulated by spatial processing, reflected in the Spatial Position Association of Response Codes (SPoARC) effect-the left-beginning, right-end positional association between space and serial position of verbal WM memoranda. We investigated the individual differences in this modulation with both behavioral and neurobiological approaches. We administered a battery of seven behavioral tasks with 160 healthy adults and collected resting-state fMRI data from a subset of 25 participants. With a multilevel mixed-effects modeling approach, we found that the SPoARC effect's magnitude predicts individual differences in verbal serial order WM capacity and is related to spatial item WM capacity. With a graph-theory-based analytic approach, this interaction between verbal serial order WM and spatial WM was corroborated in that the level of interaction between corresponding cortical regions (indexed by modularity) was predictive of the magnitude of the SPoARC effect. Additionally, the modularity of cortical regions associated with verbal serial order WM and spatial attention predicted the SPoARC effect's magnitude, indicating the involvement of spatial attention in this modulation. Together, our findings highlight multiple sources of the interplay between verbal serial order WM and spatial processing.

PMID:39815117 | DOI:10.3758/s13415-024-01240-6

Sci Rep. 2025 Jan 15;15(1):2108. doi: 10.1038/s41598-024-81305-1.

ABSTRACT

The alteration of neurovascular coupling (NVC), where acute localized blood flow increases following neural activity, plays a key role in several neurovascular processes including aging and neurodegeneration. While not equivalent to NVC, the coupling between simultaneously measured cerebral blood flow (CBF) with arterial spin labeling (ASL) and blood oxygenation dependent (BOLD) signals, can also be affected. Moreover, the acquisition of BOLD data allows the assessment of resting state (RS) fMRI metrics. In this study a multiband, multi-echo (MBME) pseudo-continuous ASL (pCASL) sequence was used to collect simultaneous BOLD and ASL data in a group of healthy control subjects, and the patterns of BOLD-CBF coupling were evaluated. Coupling was also correlated with the BOLD RS measures. The variability, reproducibility, and reliability of the metrics were also computed in a multi-session subgroup. Areas of higher coupling were observed in the visual, motor, parietal, and frontal cortices and corresponded to major brain networks. Areas of significant correlation between coupling and BOLD RS measures corresponded to areas of heightened coupling. Higher variability and lower reliability were found for coupling metrics compared to BOLD RS metrics. These results indicate BOLD-CBF coupling metrics may be useful for studying neurovascular physiology.

PMID:39814790 | DOI:10.1038/s41598-024-81305-1