Most recent paper

Brain Imaging Behav. 2025 Mar 6. doi: 10.1007/s11682-025-00993-z. Online ahead of print.

ABSTRACT

Numerous previous studies have classified brain regions related to social processing into the "social brain" regions. Recent genetic studies showed that gene expression has a crucial effect on both brain functions and behavioral social performance. However, studies still lack a clear understanding of the organization of the social gene expression (SocGene) network. This study aimed to distinguish the difference between the SocGene network and the social brain network (SBN) and further explored their deficits in schizophrenia (SCZ) patients. The SocGene network was constructed by generating the gene expression maps of six social neuropeptide receptors from the Allen Human Brain Atlas. Then, we recruited a general population sample of 37 participants and a clinical sample including 26 SCZ and 25 Healthy controls (HCs) successively to construct the resting-state SocGene and SBN at the individual level. The integration (global efficiency, GE) and segregation (local efficiency, LE) of these brain networks were calculated using the graphic analysis. Results showed that the GE and LE of the SocGene network were significantly higher than those of the SBN in both two cohorts. The SCZ patients showed significantly diminished LE of the two brain networks compared to HCs, especially in the SocGene network. These findings implied that the SocGene network strengthened the integration and segregation compared to the SBN. SCZ patients mainly exhibited deficits in the segregation of these two brain networks. The current findings provide a new perspective on combining genetic expression and brain function in understanding the psychopathology of social functioning.

PMID:40045109 | DOI:10.1007/s11682-025-00993-z

Brain Dev. 2025 Mar 4;47(2):104340. doi: 10.1016/j.braindev.2025.104340. Online ahead of print.

ABSTRACT

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and/or impulsivity that significantly affects academic, occupational, and social functioning. This review summarizes key findings of structural and functional magnetic resonance imaging (MRI) studies investigating the neural underpinnings of ADHD, focusing on T1-weighted structural MRI, diffusion tensor imaging (DTI), task-based functional MRI (task fMRI), and resting-state functional MRI (rs-fMRI). T1-weighted structural MRI studies have revealed reduced gray matter volume in regions implicated in executive function, particularly the frontal cortex, basal ganglia, and cerebellum, along with evidence of delayed cortical maturation. DTI findings highlighted abnormalities in white matter integrity, particularly in the fronto-striatal-cerebellar circuits and connections between the corpus callosum and cingulum. Task fMRI studies have demonstrated reduced activation of brain networks involved in cognitive control, timing, and reward processing, including fronto-striatal and fronto-parietal networks. Furthermore, rs-fMRI research has shown altered connectivity patterns within and between key brain networks, including the default mode, fronto-parietal, and salience networks. Despite these insights, inconsistencies across studies underscore the need for larger and more standardized research efforts. Future research should employ multimodal imaging techniques and advanced analytical methods such as machine learning to better subtype ADHD and customize interventions. Moreover, establishing harmonized imaging protocols across institutions, as exemplified by innovative strategies, such as the traveling-subject method, is crucial for mitigating intersite variability. Through collaborative efforts, neuroimaging studies in ADHD are anticipated to enhance our understanding of the disorder's heterogeneity while informing the development of precise clinical diagnoses and personalized therapeutic interventions.

PMID:40043540 | DOI:10.1016/j.braindev.2025.104340

Brain Imaging Behav. 2025 Mar 5. doi: 10.1007/s11682-024-00947-x. Online ahead of print.

ABSTRACT

Alteration of functional connectivity in brain regions is one of the potential neuropathological mechanisms underlying cognitive impairment in patients with minimal hepatic encephalopathy (MHE). Artificial liver therapy has been shown to improve cognitive impairment in patients, suggesting a potential neuroprotective effect on the brain. This study investigates the impact of artificial liver therapy (AL) on cognitive impairment in patients with minimal hepatic encephalopathy (MHE) by examining alterations in brain functional connectivity. Resting-state functional magnetic resonance imaging (fMRI) data was collected from healthy controls and MHE patients before and after therapy. The MHEpost-AL group showed improved memory, reaction time, and executive function compared to the MHEpre-AL group. Functional connectivity analysis revealed increased connectivity in specific brain regions in the MHEpre-AL group compared to healthy controls, with subsequent decreased connectivity after therapy. Lower MoCA scores, higher blood ammonia levels, and lower cholinesterase levels were associated with higher functional connectivity in the MHEpre-AL group. The study suggests that artificial liver therapy improves cognitive impairment in MHE patients, with changes in blood biochemistry mediating the link between functional connectivity and cognitive function. Correcting blood biochemistry levels may reverse abnormal brain connectivity and enhance cognitive function in MHE patients.

PMID:40042700 | DOI:10.1007/s11682-024-00947-x

Hum Brain Mapp. 2025 Mar;46(4):e70163. doi: 10.1002/hbm.70163.

ABSTRACT

Movie-watching fMRI has emerged as a theoretically viable platform for studying neurobiological substrates of affective states and emotional disorders such as pathological anxiety. However, using anxiety-inducing movie clips to probe relevant states impacted by psychopathology could risk exacerbating in-scanner movement, decreasing signal quality/quantity and thus statistical power. This could be especially problematic in target populations such as children who typically move more in the scanner. Consequently, we assessed: (1) the extent to which an anxiety-inducing movie clip altered in-scanner data quality (movement, censoring, and DVARS) in a pediatric sample with and without anxiety disorders (n = 78); and (2) investigated interactions between anxiety symptoms and movie-attenuated motion in a highly powered, transdiagnostic pediatric sample (n = 2058). Our results suggest anxiogenic movie-watching in fact reduces in-scanner movement compared to resting-state, increasing the quantity/quality of data. In one measure, pathological anxiety appeared to impact movie-attenuated motion, but the effect was small. Given potential boosts to data quality, future developmental neuroimaging studies of anxiety may benefit from the use of movie paradigms.

PMID:40042099 | DOI:10.1002/hbm.70163

J Neurosci Res. 2025 Mar;103(3):e70029. doi: 10.1002/jnr.70029.

ABSTRACT

Laterality of motor deficits is a hallmark of Parkinson's disease (PD), which is strongly correlated with disease progression. The cerebellum is an important node in the motor-related network in PD. However, the role of the cerebellum in PD lateralization remains unclear. This study enrolled 48 left-dominant-affected PD patients (LPD), 60 right-dominant-affected PD patients (RPD) and 92 age- and sex-matched healthy controls (HCs). We utilized dynamic functional connectivity and co-activation pattern analysis to investigate dynamic alterations of the cerebellum between PD patients and HCs by resting-state fMRI. Pearson partial correlation was used to measure brain-clinical correlations. We revealed two states and five co-activation patterns during the scans. Compared to HCs and RPD, LPD patients more frequently displayed State II and persisted in this state for a more extended period. The mean dwell time (MDT) in State II rose from HCs to RPD and to LPD. The MDT in State II was positively correlated with sleep disturbance in LPD patients. Regarding co-activation patterns (CAPs), LPD and RPD patients were less likely to exhibit CAP2. LPD patients were less likely to demonstrate CAP1 compared to HCs. The CAP1 metrics were positively associated with motor deficits in LPD patients. These results revealed the dynamic alterations of the cerebellum in different dominant-affected PD patients, which were related to motor deficits and sleep disturbances in PD patients. Our findings suggest that the dynamic cerebellar features may be significant factors in the lateralization of PD.

PMID:40041986 | DOI:10.1002/jnr.70029

Neural Plast. 2025 Feb 25;2025:8003718. doi: 10.1155/np/8003718. eCollection 2025.

ABSTRACT

Background and Purpose: Motor impairment is a common occurrence in patients with acute basal ganglia (BG) ischemic stroke (ABGIS). However, the underlying mechanisms of poststroke motor dysfunction remain incompletely elucidated. In this study, we employed multifrequency band wavelet transform-based amplitude of low-frequency fluctuations (Wavelet-ALFFs) to investigate the alterations of spontaneous regional neural activity in patients with ABGIS. Methods: A total of 39 ABGIS patients with motor dysfunction and 45 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Wavelet-ALFF values were calculated in the conventional frequency band (0.01-0.08 Hz), slow-5 frequency band (0.01-0.027 Hz), and slow-4 frequency band (0.027-0.073 Hz). A two-sample t-test was performed to compare the Wavelet-ALFF values between the two groups with sex as a covariate and Gaussian random field (GRF) theory (voxel p < 0.001, cluster p < 0.05, two-tailed) was used for the multiple corrections. Furthermore, spearman correlation analysis was performed to assess the relationship between alterations in regional neural activity between Fugl-Meyer Assessment (FMA) and National Institutes of Health Stroke Scale (NIHSS) scores. Results: In comparison to HCs, patients with ABGIS showed significantly increased Wavelet-ALFF in the left middle temporal gyrus (MTG) and decreased Wavelet-ALFF in the right inferior frontal operculum (IFO) across all three frequency bands (conventional, slow-4, and slow-5). In the left superior occipital gyrus (SOG), Wavelet-ALFF was decreased in the conventional frequency band but increased in the slow-4 frequency band. Additionally, patients with ABGIS demonstrated reduced Wavelet-ALFF in the right superior temporal gyrus (STG) in the conventional and slow-4 frequency bands. In the slow-5 frequency band, increased Wavelet-ALFF was observed in the left calcarine cortex (CC), left middle frontal gyrus (MFG), left supramarginal gyrus (SMG), and left postcentral gyrus (PCG), while decreased Wavelet-ALFF was noted in the right precuneus (PCu). Correlation analysis revealed that increased Wavelet-ALFF in the left CC in the slow-5 frequency band was positively correlated with the FMA score. No other correlations were detected in the conventional and slow-4 frequency bands. Conclusions: The altered spontaneous neural activity was frequency-specific in patients with ABGIS, and the slow-5 frequency band exhibited better results. Furthermore, the relationship between spontaneous brain activity and clinical characteristics highlighted patterns of neural alterations associated with motor dysfunction. These findings may provide novel insights into the neural mechanisms underlying motor dysfunction in ABGIS.

PMID:40041455 | PMC:PMC11879565 | DOI:10.1155/np/8003718

Imaging Neurosci (Camb). 2024 Nov 18;2:1-18. doi: 10.1162/imag_a_00373. eCollection 2024 Nov 1.

ABSTRACT

Premature birth affects brain maturation, illustrated by altered brain functional connectivity at term equivalent age (TEA) and alters neurobehavioral outcome. To correct early developmental differences and improve neurological outcome, music during the neonatal intensive care unit (NICU) stay has been proposed as an auditory enrichment with modulatory effects on functional and structural brain development, but longitudinal effects of such interventions have not been studied so far. We longitudinally investigated resting-state functional connectivity (RS-FC) maturation in preterm infants (n = 43). Data-driven Independent Component Analyses (ICA) were performed on scans obtained at 33- and 40-week gestational age (GA), determining the presence of distinct resting-state networks (RSNs). Connectome analysis "accordance measure" quantitively examined the RS-FC both at 33- and 40-week GA. Further comparing the internetwork RS-FC at 33- and 40-week GA provided a circuitry of interest (COI) for significant maturational changes in which the effects on the RS-FC of a music intervention were tested. The connectome analyses resulted in a COI of RS-FC connections significantly maturing from 33 to 40 weeks GA, namely between the thalamic/brainstem and prefrontal-limbic, salience, sensorimotor, auditory, and prefrontal cortical networks; between the prefrontal-limbic and cerebellar, visual and left hemispheric precuneus networks; between the salience and visual, and cerebellar networks; and between the sensorimotor and auditory, and posterior cingulate/precuneus networks. The infants exposed to music exhibited significantly increased maturation in RS-FC between the thalamic/brainstem and salience networks, compared with controls. This study exemplifies that preterm infant RS-FC maturation is modulated through NICU music exposure, highlighting the importance of environmental enrichment for neurodevelopment in premature newborns.

PMID:40041298 | PMC:PMC11873764 | DOI:10.1162/imag_a_00373

Heliyon. 2025 Feb 6;11(4):e42469. doi: 10.1016/j.heliyon.2025.e42469. eCollection 2025 Feb 28.

ABSTRACT

BACKGROUND: Generalized anxiety disorder (GAD) is the most common type of anxiety disorder and can cause severe damage to patients and increase medical and social burdens. Vagus nerve stimulation (VNS) has been used for treating mental disorders, but the involvement of surgery, perioperative risks, and potentially significant side effects have limited this treatment. Anatomical studies have shown that the ear is the only area where the afferent vagus nerve is distributed on the skin. Recently, the safety and efficacy of transcutaneous auricular vagus nerve stimulation (t-VNS) with electrical stimulation for depression and epilepsy have been objectively evaluated. This trial is trying to evaluate the efficacy of t-VNS with electrical stimulation for the treatment of GAD and explore the potential underlying neural mechanism using fMRI.

METHODS: An assessor-participant blinded, randomized sham-controlled trial will be performed. Sixty participants with GAD will be randomly assigned to the t-VNS group or sham t-VNS group. The treatment will last for 8 weeks, once every 30 min and twice a day. Four clinical assessments will be conducted: before treatment, at 2 weeks, at 4 weeks, and posttreatment. The primary outcome parameter is the categorical classification of treatment response in the Hamilton Anxiety Rating Scale (HAMA) score. Functional magnetic resonance imaging (fMRI) scans will be applied, and the alterations in Amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) on resting-state fMRI will be compared between the two groups before and after treatment. Moreover, the correlation between the changes in clinical symptoms and the changes in the altered ALFF and FC in the two groups will be analyzed.

DISCUSSION: This high-level evidence-based medical research is expected to evaluate the value of t-VNS in treating GAD and provide a preliminary explanation of its mechanism of action in brain functional imaging. In addition, the use of t-VNS devices has substantially decreased time and financial costs, potentially providing a promising option for complementary alternative medicine in the treatment of GAD, thereby advancing treatment decisions for this condition.

TRIAL REGISTRATION: International Traditional Medicine Clinical Trial Registry, ITMCTR2022000099. Registered on June 30, 2022.

PMID:40041000 | PMC:PMC11876880 | DOI:10.1016/j.heliyon.2025.e42469

Heliyon. 2025 Feb 13;11(4):e42695. doi: 10.1016/j.heliyon.2025.e42695. eCollection 2025 Feb 28.

ABSTRACT

PURPOSE: Without any visible indicator on structure magnetic resonance imaging (MRI), the diagnosis of MRI-negative temporal lobe epilepsy (NTLE) gets harder. By considering healthy control (HC), a specific functional connectivity (FC) model was constructed in a network topology to improve FC computation to a high-level.

METHODS: MRI data of 20 NTLE patients and 60 HC were pre-processed. Relative to HC, a network-level specific FC model of each network index was built to score the network functions for each NTLE patient. The specific brain areas (regarded as ROIs) were extracted for NTLE by sensitivity analysis of scores. By considering scores of specific ROIs as feature vectors to input into a SVM respectively, a specific NTLE classifier was constructed. Both 10-fold cross validation and hold-out method were utilized to validate the classification and to evaluate the effectiveness of our specific FC models. Simultaneously, the specific FC model was compared to the conventional FC model of Pearson correlation.

RESULTS: By the constructed model for specific FC at a network-level, 11 specific ROIs, such as, frontal lobe, temporal lobe, parietal lobe, hippocampus, and occipital lobe, were extracted for NTLE. Accuracy of our specific NTLE classifier could reach up nearly 93 %, over 6 % greater than conventional FC model of Pearson correlation.

CONCLUSIONS: The network-level specific FC model might provide a new methodology for machine-aiding detection of functional abnormal lesions of NTLE by resting-state functional MRI.

PMID:40040985 | PMC:PMC11876875 | DOI:10.1016/j.heliyon.2025.e42695

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