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Front Neurol. 2025 Jan 28;16:1465467. doi: 10.3389/fneur.2025.1465467. eCollection 2025.

ABSTRACT

OBJECTIVE: The present study aimed to investigate alterations in neural activity and reorganization of functional networks within critical brain regions associated with reduced cardiorespiratory fitness (CRF) in stroke patients. By employing resting-state functional magnetic resonance imaging (fMRI), we sought to identify specific brain areas that may be implicated in CRF decline among this patient population.

METHODS: A total of 22 patients with stroke and 15 healthy subjects matched for age, gender, and body mass index were recruited. Rehabilitation assessments included peak oxygen uptake (VO2peak), peak work-rate, 10-meter walk test (10mWT), five times sit-to-stand test (FTSST), and 6-min walking distance (6MWD). Resting-state fMRI data were collected for the two groups, and correlation between changes in the amplitude of low-frequency fluctuations (ALFF) and CRF was analyzed to detect brain regions related to CRF and local neural activity in patients with stroke. On the basis of ALFF analysis, brain network analysis was performed, and the CRF-related brain regions in patients with stroke were selected as seed points. Functional connectivity (FC) analysis was the used to identify brain regions and networks potentially associated with CRF in patients with stroke.

RESULTS: Patients with stroke exhibited significantly lower VO2peak, peak work-rate, 10mWT, and 6MWD compared to healthy controls (p < 0.001). FTSST was significantly higher in patients with stroke than healthy controls (p < 0.001). ALFF analysis identified CRF-related brain regions in patients with stroke, including the ipsilesional superior temporal gyrus (r = 0.56947, p = 0.00036), middle frontal gyrus (r = 0.62446, p = 0.00006), and precentral gyrus (r = 0.56866, p = 0.00036). FC analysis revealed that the functional connectivity of brain regions related to CRF in patients with stroke involved the ipsilesional M1 to ipsilesional precentral gyrus and contralesional postcentral gyrus, and the correlation coefficients were r = 0.54802 (p = 0.00065) and r = 0.49511 (p = 0.0025), respectively. The correlation coefficients of ipsilesional middle frontal gyrus to contralesional middle frontal gyrus, angular gyrus and ipsilesional superior frontal gyrus were r = 0.58617 (p = 0.00022), r = 0.57735 (p = 0.00028), and r = -0.65229 (p = 0.00002), respectively.

CONCLUSION: This study observed that CRF levels were lower in stroke patients compared to those in healthy individuals. Resting fMRI analysis was applied to identify CRF-related brain regions (ipsilesional superior temporal, middle frontal, precentral gyri) and networks in patients with stroke.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.html?proj=151095.

PMID:40040907 | PMC:PMC11877007 | DOI:10.3389/fneur.2025.1465467

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782925.

ABSTRACT

INTRODUCTION: Although the interaction between the brain and the heart, through the autonomic nervous system, is an established phenomenon, multimodal studies that have explored their bidirectional interplay are still limited.

AIM: In this context, the objective of the present study was to investigate the coupling between sympathetic and vagal dynamics and brain functional connectivity during resting state, thanks to simultaneously acquired electrocardiogram and functional magnetic resonance imaging (fMRI) data.

METHODS: Twenty healthy controls (67.42 ± 10.81 years, 60% females) were included in the study. Unimodal fMRI and heart rate variability (HRV) results were integrated in a joint analysis framework. Trivariate dynamic functional connectivity (dFC) features were correlated with time-varying HRV parameters to identify brain regions involved in autonomic modulation.

RESULTS: In a data-driven approach, the present analysis allowed to extract triplets of brain regions whose dFC was coupled with both sympathetic and vagal activity dynamics. The identified brain regions often belonged to the central autonomic network, which is a network of brain structures that are involved in the regulation of autonomic processes at high central level.

CONCLUSION: The present multimodal HRV and fMRI dFC analysis provided new findings on the physiological brain-heart interactions, paving the way to explore the same mechanisms in disorders of the brain-heart axis.

PMID:40040234 | DOI:10.1109/EMBC53108.2024.10782925

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782811.

ABSTRACT

Early diagnosis of Acute Stress Disorder (ASD) is important, given its potential progression to post-traumatic system disorder (PTSD). The current diagnostic tool has some degree of subjectiveness in assessing emotional responses to trauma and the severity of stress reactions. To this end, we proposed a new method to detect ASD using machine learning with resting-state functional magnetic resonance imaging (rs-fMRI) data. We used 48 subjects of rs-fMRI data and PTSD Check List - Civilian Version (PCL-C) questionnaire from Advancing Understanding of RecOvery afteR traumA (AURORA) dataset. We extracted five frequency-domain features from each blood-oxygen-level dependent (BOLD) signal from 48 cortical and 21 subcortical regions. We also extracted four graph features from sparse inverse covariance matrices of the BOLD signals. Eighteen features appeared to be significantly different (p<0.05). Using these features, multi-layer perceptron showed accuracy 91.7%, sensitivity 96.8%, and specificity 82.4% using the leave-one-subject-out cross validation scheme. We found that the Right Accumbens and Lingual Gyrus has high effect size and substantial impact within the machine learning model.

PMID:40040221 | DOI:10.1109/EMBC53108.2024.10782811

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10781953.

ABSTRACT

Functional connectivity is commonly used for studying functional interactions among brain regions. However, its results are affected by noise and/or physiological artifacts, especially when computed using blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals. In this study, we assessed the effect of these artifacts by simulating physiological and BOLD fMRI signals during resting and task conditions and quantifying the resulting functional connectivity results patterns by well established methods (full and partial correlation). Our results reveal that the regions with similar physiological response functions were adversely affected by physiological artifacts. Notably, functional connectivity values computed during task execution exhibited lower errors compared to those computed during the rest period. Furthermore, the results computed using the partial correlation method consistently yielded lower errors compared to those computed using full correlation. Overall, our findings quantitatively characterize the impact of physiological artifacts on functional connectivity patterns and emphasize the importance of method choice in mitigating the impact of artifacts.

PMID:40040207 | DOI:10.1109/EMBC53108.2024.10781953

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782387.

ABSTRACT

In the exploration of dynamic changes in network connectivity within resting-state functional magnetic resonance imaging (rs-fMRI), the dominant focus has traditionally been on a holistic study of the entire brain. Various methodologies and analyses have been applied in prior research within this domain. This study takes a novel approach by delving into a comparative analysis of the similarities between electroencephalogram (EEG) signals with motor imagery tasks and rs-fMRI signal. Both data types collect time series data from their respective datasets. Drawing from the insights of previous research, the common spatial patterns (CSP) method, mostly used for its efficacy in handling EEG signals, was employed. Notably, CSP is a supervised learning transformation of signals, offering advantages over the implementation of deep learning models. this study pioneers the integration of the CSP method with fMRI datasets. Validation of this approach was conducted through a rs-fMRI study focused on schizophrenia, includes two primary classes: patients and controls. In addition to CSP, principal component analysis (PCA) was explored as an unsupervised dimensionality reduction technique, serving as a benchmark for comparison. The results revealed that CSP has better performance relative to PCA and other examined methods. This study contributes to the expanding landscape of understanding time-varying network connectivity, emphasizing the potential applicability of CSP beyond its traditional domain of EEG signals, and take benefit of its effectiveness in the context of rs-fMRI.

PMID:40040201 | DOI:10.1109/EMBC53108.2024.10782387

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782009.

ABSTRACT

The human brain is an enormous conundrum composed of billions of neurons with trillions of connections. The functional brain network is extremely complicated, with multiple statistical, structural, and dynamic features. Complex networks theory provides a sensible and robust technique for understanding and analyzing the functions and structures of complex systems, including the brain. This paper investigates a functional brain network based on the large resting-state fMRI dataset to discover its features using complex networks theory and methodologies at various spatial resolutions. The resting-state functional brain network follows a broad-scale distribution, which contains both small-world and scale-free features besides its community structure. However, the network's degree and betweenness are largely varied among different scales, yet the majority of the other complex brain-network measures are primarily conserved.

PMID:40040171 | DOI:10.1109/EMBC53108.2024.10782009

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782518.

ABSTRACT

Resting-state functional magnetic resonance imaging (rsfMRI) plays a pivotal role in estimating intrinsic brain functional connectivity within healthy and clinical populations. However, the pervasive impact of head motion confounds the interpretation of rsfMRI data and is typically addressed through preprocessing without further exploration. This investigation aims to scrutinize the intricate interplay between head motion and neurobiologically relevant BOLD signal as well as its potential clinical implications. Here, we use independent component analysis (ICA) to extract large-scale brain networks from BOLD fMRI and modeled head motion time series for 508 subjects sourced from three major psychosis projects. Our approach uncovers the presence of latent network information within modeled head motion data. Moreover, we find altered functional network connectivity (FNC) between healthy controls (HC) and individuals with schizophrenia (SZ) for BOLD and motion networks, revealing that projections of BOLD time series onto network features extracted from head motion data reflect cohort-specific information. Our approach challenges conventional perspectives by treating motion-related signals not as mere noise, but as potential repositories of valuable insights into functional brain connectivity across diverse populations.

PMID:40040138 | DOI:10.1109/EMBC53108.2024.10782518

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10781658.

ABSTRACT

Different brain imaging methods provide valuable insights, and their combination enhances understanding of the brain. Existing fusion approaches typically use precomputed functional magnetic resonance imaging (fMRI) features, such as amplitude of low frequency fluctuations, regional homogeneity, or functional network connectivity while linking fMRI and structural MRI (sMRI). The fusion step typically ignores the detailed temporal information available in the complete 4D fMRI. Motivated by prior work showing covarying sMRI networks resemble resting fMRI networks, we introduce a new technique called copula linked parallel ICA (CLiP-ICA). This innovative method simultaneously estimates independent sources and an unmixing matrix for each modality while also linking spatial sources through a copula model. We tested the effectiveness of CLiP-ICA in both a simulation and a real-data using fMRI and sMRI data from an Alzheimer study. Results showed significant linkage in several domains including cerebellum, sensorimotor and default mode. In sum, we provide an approach to simultaneously estimate and link independent components of fMRI and sMRI while preserving temporal information.

PMID:40040121 | DOI:10.1109/EMBC53108.2024.10781658

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782528.

ABSTRACT

In this study, we present a multimodal fusion approach, combining gray matter (GM) and multiple resting functional magnetic resonance imaging (fMRI) networks via a novel approach called parallel multilink joint independent component analysis (jICA) which combines 4D fMRI with 3D sMRI data. We focus on network-specific reconstruction and estimating joint relationship from differently distributed data by relaxing jICA assumption. Our methodology facilitates a detailed examination of altered connectivity patterns associated with Alzheimer's disease (AD). The study compares healthy controls (HC) and individuals with AD, employing two-sample t-tests with false discovery rate (FDR) correction to rigorously assess group differences. Network-specific correlation analysis reveals the joint relationships between different brain functions, allowing for a comprehensive exploration of AD pathology. Our approach also finds joint independent sources of altered activation patterns in key regions, such as the precuneus of the DMN, paracentral lobule of the sensorimotor domain, and cerebellum. This provides localized insights into the impact of AD on specific brain regions.

PMID:40039683 | DOI:10.1109/EMBC53108.2024.10782528

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782425.

ABSTRACT

Repetitive transcranial magnetic stimulation (rTMS) has been proved a potential therapeutic approach for improving the cognitive performance of patients with mild cognitive impairment (MCI). However, no biomarker is available for identifying who is most likely to respond to rTMS. The purpose of this study was to demonstrate that cognitive improvement after rTMS may be associated with functional connectivity of salience network at baseline. Resting-state functional magnetic resonance imaging (rs-fMRI) data of fifty-three MCI patients were collected before a 10-day of rTMS treatment. Multivoxel pattern analysis was applied to realize the classification of the MCI patients responded or not to rTMS treatment, and the prediction to the cognitive scores. The analysis yielded a significant overall accuracy of 84.91% (90.00% sensitivity, 78.26% specificity). Right anterior cingulate cortex contributed most to the classification. Besides, regression analysis also showed the predictive value of salience network to the changes of cognitive performance. Our study demonstrated that the functional connectivity of salience network is predictive of treatment response to rTMS.

PMID:40039580 | DOI:10.1109/EMBC53108.2024.10782425

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782625.

ABSTRACT

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients.

PMID:40039369 | DOI:10.1109/EMBC53108.2024.10782625

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782404.

ABSTRACT

Intrinsic Connectivity Networks (ICNs) reflect functional brain organization responsible for various cognitive processes, including sensory perception, motor control, memory, and attention. In this study, we used the Multivariate-Objective Optimization Independent Component Analysis with Reference (MOO-ICAR) and the NeuroMark 2.1 (adult) template to estimate subject-specific ICNs in resting-state functional magnetic resonance imaging (rsfMRI) data of infants. The NeuroMark 2.1 template contains 105 multi-scale canonical ICNs derived from 100k+ adults across multiple datasets. The multi-scale ICNs capture functional segregation across various levels of granularity across brain, revealing functional sources and their interactions. The results showed that the 105 ICNs in infants were spatially aligned with those in the template and revealed age-related distinctive patterns in static Functional Network Connectivity (sFNC), particularly in the sub-cortical and high-level cognitive domains. This study is the first to investigate the presence and development of these multi-scale ICNs in infant rsfMRI data. Our findings confirmed the presence of identifiable canonical ICNs in infants as young as six months, showcasing a strong association between these networks and age and suggesting potential biomarkers for early identification of neurodevelopmental disability.

PMID:40039283 | DOI:10.1109/EMBC53108.2024.10782404

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782953.

ABSTRACT

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders.

PMID:40039134 | DOI:10.1109/EMBC53108.2024.10782953

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782408.

ABSTRACT

Autism spectrum disorder (ASD) is a neurodevelopmental disorder impacting a person's social communication skills and behaviors. Due to its a wide range of symptoms and presentations, diagnosis is a subjective process reliant on clinician experience and symptom reports. Our pilot study aims to improve this process using a resting-state fMRI biomarker based on dynamic network modeling. Using matched cohorts (14 healthy, 14 ASD) from the DecNef rsfMRI open dataset we built generative models of the influence between cortical regions of the brain, encapsulated by a value we call the sink index; a network-based biomarker that measures the influence on between brain regions. A high sink index suggests high influence from and minimal influence on other parts of the network over time. Three cortical regions were found to have statistically significant differences between ASD and control patients: the left lateral occipital cortex, right frontal pole, and the left postcentral gyrus. Using these results, a high accuracy (AUC 0.91) classifier was generated that can quantitatively predict ASD status.

PMID:40039050 | DOI:10.1109/EMBC53108.2024.10782408

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782267.

ABSTRACT

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications.

PMID:40039021 | DOI:10.1109/EMBC53108.2024.10782267

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10782250.

ABSTRACT

Using neuroimaging-derived data for age estimation serves as a prominent approach in comprehending the normal pace of brain development and mechanisms underlying cognitive declines due to aging and neurological diseases. Despite the promise of resting-state functional magnetic resonance imaging (rs-fMRI) for brain age prediction, previous deep learning models have prioritized capturing either the temporal dynamics via time courses (TCs) or the inherent network topology revealed by functional network connectivity (FNC). These fragmented models neglect the complementary information available by synergistically integrating both. To address this, we introduced BrainFTFCN, a novel feature fusion network that synergistically integrates TCs and FNC for enhanced brain age prediction and model interpretability. BrainFTFCN uniquely combines a Temporal Attention Autoencoder (TAAE) to model evolving activity patterns within TCs and a Functional Connectivity Graph Attention Network (FCGAT) to capture spatial relationships embedded within FNC. The fused features were then fed into a support vector regression model for final age prediction. BrainFTFCN's efficacy shone on Cam-CAN dataset, outperforming state-of-the-art models by 28.21% in mean absolute error (MAE) and demonstrating consistent improvement across other metrics. Ablation studies solidified the critical role of multi-feature integration in boosting prediction. Notably, the most crucial brain regions and discriminative FNC can be easily unveiled via LASSO regression and GNNExplainer respectively, together unlocking biological interpretability and highlighting the model's potential for uncovering valid aging biomarkers.

PMID:40038971 | DOI:10.1109/EMBC53108.2024.10782250

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782978.

ABSTRACT

Schizophrenia is a chronic mental disorder thought to affect cognitive processes and emotional regulation by disrupting communication between brain regions. The high level of training and experience required for accurate diagnosis limits access to care for many patients with this debilitating illness, leading to delays in diagnosis and progression of illness for an uncertain period. To improve accuracy of treatment, we investigated a potential quantitative method of tracking progression of schizophrenia using resting-state fMRI. Using data sourced from the DecNef rsfMRI open dataset in High and Low Duration cohorts, we constructed personalized dynamic network models that characterize influence between cortical regions of the brain. The contrasting levels of influence were converted to a phase space and ranked according to a novel network-based biomarker we call the "sink index." When the sink index is high it suggests that a region is being heavily influenced by other parts of the network and is not itself influencing the network strongly. Out of seventy cortical regions, the sink index of the left banks of the superior temporal sulcus was identified as able to significantly differentiate between cohorts and built a classifier of very high accuracy (sens 0.86, spec 1.0, AUC 0.99). Our results support the hypothesis that the pathophysiology of schizophrenia is indicative of aberrant network connectivity patterns.

PMID:40038957 | DOI:10.1109/EMBC53108.2024.10782978

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782970.

ABSTRACT

One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses such as schizophrenia (SZ) is the high noise levels variability among individuals including age, sex, and different protocols used in labs. To deal with these challenging problems, we designed a recognition method for using brain functional networks to classify SZs and healthy controls (HCs). Our method includes two stages of training. In the first stage, we use a deep convolutional neural network (DCNN) to extract valuable deep features from functional network connectivity (FNC) images. In the next stage, these deep features are used as inputs to a gradient-boosting trees classifier. After the training process, the boosting trees classifier gains a remarkable performance compared to the DCNN classifier. We evaluate this approach using a large dataset of schizophrenia and healthy controls divided into separate validation and training sets. Experimental results showed that the recognition accuracy is over 98 %, compared to a support vector machine baseline of 77% demonstrating the ability of our system to distinguish differences between the two groups. We also estimate heatmaps for each FNC image, representing a 2D FNC matrix indicated which pairs of networks are most predictive of SZ. Our method thus provides both high accuracy, and provides insights into the relevant brain regions for SZ.

PMID:40038933 | DOI:10.1109/EMBC53108.2024.10782970

Schizophr Bull. 2025 Mar 4;51(Supplement_2):S160-S172. doi: 10.1093/schbul/sbad116.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Social anhedonia is a core feature of schizotypy and correlates significantly with social functioning and range adaptation. Range adaptation refers to representing a stimulus value based on its relative position in the range of pre-experienced values. This study aimed to examine the resting-state neural correlates of range adaptation and its associations with social anhedonia and social functioning.

STUDY DESIGN: In study 1, 60 participants completed resting-state magnetic resonance spectroscopy and fMRI scans. Range adaptation was assessed by a valid effort-based decision-making paradigm. Self-reported questionnaires was used to measure social anhedonia and social functioning. Study 2 utilized 26 pairs of participants with high (HSoA) and low levels of social anhedonia (LSoA) to examine the group difference in range adaptation's neural correlates and its relationship with social anhedonia and social functioning. An independent sample of 40 pairs of HSoA and LSoA was used to verify the findings.

STUDY RESULTS: Study 1 showed that range adaptation correlated with excitation-inhibition balance (EIB) and ventral prefrontal cortex (vPFC) functional connectivity, which in turn correlating positively with social functioning. Range adaptation was specifically determined by the EIB via mediation of ventral-medial prefrontal cortex functional connectivities. Study 2 found HSoA and LSoA participants exhibiting comparable EIB and vPFC connectivities. However, EIB and vPFC connectivities were negatively correlated with social anhedonia and social functioning in HSoA participants.

CONCLUSIONS: EIB and vPFC functional connectivity is putative neural correlates for range adaptation. Such neural correlates are associated with social anhedonia and social functioning.

PMID:40037829 | DOI:10.1093/schbul/sbad116

Schizophr Bull. 2025 Mar 4;51(Supplement_2):S194-S204. doi: 10.1093/schbul/sbad183.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Mental time travel (MTT) is a crucial ability for daily life. Personal goal-related MTT events has stronger phenomenological characteristics than personal goal-unrelated ones, ie, the "personal goal-advantage effect". However, it remains unclear whether this effect is impacted in individuals with high schizotypal traits (HST) and the neural correlates of this effect have yet to be elucidated. The present study aimed to fill these knowledge gaps. We hypothesized that HST would show a reduced "personal goal-advantage effect" in MTT and would exhibit altered relationships with resting-state functional connectivity.

STUDY DESIGN: In Study 1, 37 HST and 40 individuals with low schizotypal traits (LST) were recruited. Participants generated MTT events with personal goal-related and personal goal-unrelated cues. In Study 2, 39 HST and 38 LST were recruited, they completed the same behavioral task and resting-state functional magnetic resonance imaging (fMRI) scanning.

STUDY RESULTS: Both Study 1 and Study 2 revealed that HST exhibited reduced "personal goal-advantage effect" on MTT specificity. Moreover, Study 2 showed that compared with LST, HST exhibited altered association between the "personal goal-advantage effect" and functional connectivity (ie, between the right precuneus and the left postcentral gyrus and "personal goal-advantage effect" on emotional valence, between the left hippocampus and the right temporal fusiform gyrus and "personal goal-advantage effect" on emotional intensity).

CONCLUSIONS: These findings suggest that HST exhibit a reduced "personal goal-advantage effect" in MTT specificity and altered neural correlates related to this effect. The "personal goal-advantage effect" may be a potential target for intervention in HST.

PMID:40037825 | DOI:10.1093/schbul/sbad183