Most recent paper

Brain Sci. 2025 Feb 14;15(2):200. doi: 10.3390/brainsci15020200.

ABSTRACT

Growth mindset, which asserts that intelligence and abilities can be cultivated through effort and learning, has garnered substantial attention in psychological and educational research. While the psychological and behavioral impacts of growth mindset are well-established, the underlying neural mechanisms remain relatively underexplored. Furthermore, there is a lack of comprehensive reviews synthesizing the neural evidence on growth mindset, hindering a fuller understanding of this concept. This scoping review aims to synthesize existing empirical studies on the neural mechanisms of growth mindset, focusing on research objectives, methods, and participant characteristics. A total of 15 studies were reviewed, revealing six primary research objectives: (1) neural mechanisms of error and feedback processing, (2) domain-specific mindsets, (3) neural changes resulting from mindset interventions, (4) mindsets and grit, (5) the neuroanatomy of mindsets, and (6) neural mechanisms of stereotype violation, with error and feedback processing being the most frequently investigated. Ten of the 15 studies employed EEG, while other techniques included structural MRI, task-based fMRI, and resting-state fMRI, with the majority of research focusing on adult populations. Although the existing literature offers valuable insights, further research is needed to explore additional aspects of mindsets, particularly in children, and to refine the methodologies used to investigate the neural mechanisms underlying growth mindset.

PMID:40002532 | DOI:10.3390/brainsci15020200

Brain Behav. 2025 Feb;15(2):e70356. doi: 10.1002/brb3.70356.

ABSTRACT

INTRODUCTION: Cognitive impairment after a stroke significantly affects patients' quality of life, yet not all strokes lead to such impairment, and the underlying reasons remain unclear. This study employs resting-state functional magnetic resonance imaging (rs-fMRI) to compare subcortical stroke patients with and without cognitive impairment. Our goal is to identify distinct abnormalities in regional brain spontaneous activity and functional connectivity (FC) to better understand the neural basis of post-stroke cognitive outcomes.

METHODS: A total of 62 first-ever non-acute subcortical stroke patients were classified into post-stroke with abnormal cognition (PSAC) and with normal cognition (PSNC) groups. Rs-MRI was utilized to assess regional homogeneity (ReHo) in 32 PSAC, 30 PSNC, and 62 age- and sex-matched healthy controls (HC). Then we performed the seed-based whole-brain FC analysis based on the ReHo results. A partial correlation analysis examined the relationship between altered ReHo or FC and Montreal Cognitive Assessment (MoCA) scores.

RESULTS: It showed varied activity in cognitive-related brain regions in both stroke groups compared to HC, such as the right superior frontal gyrus, the right middle temporal gyrus, the right postcentral gyrus, and the left cerebellar lobules. The PSAC group had increased activity in the bilateral inferior temporal gyrus as well. Significant differences in activity were also found between PSAC and PSNC groups, with the PSAC group showing decreased activity in the left gyrus rectus (REC) and increased activity in cerebellar lobules. FC analysis revealed decreased connections in the PSAC group, particularly involving the left REC. Activity and FC in left REC and cerebellum also significantly correlated with MoCA scores.

CONCLUSIONS: These findings suggest unique patterns of brain activity and connectivity in non-acute subcortical stroke patients with cognitive impairment, shedding light on potential neural mechanisms underlying post-stroke cognitive impairment. While the left REC may be a potential neural regulatory stimulus target in clinical applications.

PMID:40001287 | DOI:10.1002/brb3.70356

J Neuroimaging. 2025 Jan-Feb;35(1):e70027. doi: 10.1111/jon.70027.

ABSTRACT

BACKGROUND AND PURPOSE: In neurosurgery, functional MRI is crucial for preoperative planning to obtain the cortical cortex map of language areas. This preliminary work involved analyzing the functional MRIs of 20 oncological patients. Our question is if resting-state functional MRI (rs-fMRI) can replace standard task-based functional MRI (tb-fMRI) in routine clinical applications. The aim of this challenge is to determine if rs-fMRI is as effective as tb-fMRI and to develop a systematic approach for the extraction of a cortical language map.

METHODS: We started by analyzing our rs-fMRI images and validated the correct mapping of language regions using an independent components analysis approach; then, we used the analysis of connectivity networks to compare the two techniques.

RESULTS: The regions identified in rs-fMRI align with established medical knowledge; a comparison of rs-fMRI and tb-fMRI reveals that the four language regions-Broca's and Wernicke's areas in both hemispheres-exhibit activation in both techniques; furthermore, we highlighted that rs-fMRI reveals more comprehensive details about functional connectivity in contrast to tb-fMRI.

CONCLUSIONS: rs-MRI and tb-MRI provide similar levels of efficacy in revealing the functional areas of the brain for preoperative mapping when a lesion lies in areas related to language; thus, both techniques can be utilized for this goal. Based on this, we developed an rs-fMRI processing pipeline for clinical usage and applied it to a patient outside the study.

PMID:40000389 | DOI:10.1111/jon.70027

Brain. 2025 Feb 25:awaf075. doi: 10.1093/brain/awaf075. Online ahead of print.

ABSTRACT

Functional neuroimaging has provided several new tools for improving both the diagnosis and prognosis in patients with DoC. These tools are now being used to detect residual and covert awareness in behaviourally non-responsive patients with an acquired severe brain injury and predict which patients are likely to recover. Despite endorsement of advanced imaging by multiple clinical bodies, widespread implementation of imaging techniques such as functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET) in both acute and prolonged disorders of consciousness patients has been hindered by perceived costs, technological barriers, and lack of expertise needed to acquire, interpret, and implement these methods. In this review we provide a comprehensive overview of neuroimaging in DoC, the different technical approaches employed (i.e. fMRI, EEG, PET), the imaging paradigms used (active, passive, resting state) and the types of inferences that have been made about residual cortical function based on those paradigms (e.g., perception, awareness, communication). Next, we outline how these barriers might be overcome, discuss which select patients stand to benefit the most from these neuroimaging techniques, and consider when during their clinical trajectory imaging tests are likely to be most useful. Moreover, we make recommendations that will help clinicians decide which advanced imaging technologies and protocols are likely to be most appropriate in any particular clinical case. Finally, we describe how these techniques can be implemented in routine clinical care to augment current clinical tools and outline future directions for the field as a whole.

PMID:39997570 | DOI:10.1093/brain/awaf075

Mov Disord. 2025 Feb 25. doi: 10.1002/mds.30146. Online ahead of print.

ABSTRACT

BACKGROUND: Freezing of gait (FOG) is a common gait disorder that often accompanies Parkinson's disease (PD). The current understanding of brain functional organization in FOG was built on the assumption that the functional connectivity (FC) of networks is static, but FC changes dynamically over time. We aimed to characterize the dynamic functional connectivity (DFC) in patients with FOG based on high temporal-resolution functional MRI (fMRI).

METHODS: Eighty-seven PD patients, including 29 with FOG and 58 without FOG, and 32 healthy controls underwent resting-state fMRI. Spatial independent component analysis and a sliding-window approach were used to estimate DFC.

RESULTS: Four patterns of structured FC 'states' were identified: a frequent and sparsely connected network (State I), a less frequent but highly synchronized network (State IV), and two states with opposite connecting directions between the visual network and the sensorimotor network (positively connected in State II, negatively connected in State III). Compared with the non-FOG group, patients with FOG spent significantly less time in State II and more time in State III. The longer dwell time in State III was correlated with more severe FOG symptoms. The fractional window of State III tended to correlate to visual-spatial and executive dysfunction in FOG. Moreover, fewer transitions between brain states and lower variability in local efficiency were observed in FOG, suggesting a relatively 'rigid' brain.

CONCLUSIONS: This study highlights how visuomotor network dynamics are related to the presence and severity of FOG in PD patients, which provides new insights into understanding the pathophysiological mechanisms that underly FOG. © 2025 International Parkinson and Movement Disorder Society.

PMID:39996352 | DOI:10.1002/mds.30146

Front Aging Neurosci. 2025 Feb 10;17:1522591. doi: 10.3389/fnagi.2025.1522591. eCollection 2025.

ABSTRACT

The underlying neuropathological mechanisms in Binswanger's disease (BD) with mild cognitive impairment (BD-MCI) remain unclear. The multiparameter functional magnetic resonance imaging (fMRI) including amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), independent component analysis (ICA), and edge-link analysis was utilized to explore the abnormal brain networks of BD-MCI patients. Compared with the BD without MCI group, this study revealed that the ALFF values in the BD-MCI group were significantly increased in the Temporal_Inf_R, Frontal_Mid_Orb_L, and Hippocampus_L, while decreased in the SupraMarginal_R and Precuneus_R. The fALFF value in the BD-MCI group exhibited a reduction in the Frontal_Med_Orb_L. Additionally, ReHo values in the BD-MCI group increased in the Hippocampus_R but decreased in several areas including Precentral_L, Putamen_L, Postcentral_R, Supp_Motor_Area_R, and SupraMarginal_L. The results of ICA revealed that patients diagnosed with BD-MCI exhibited abnormal connectivity patterns across 12 groups of independent components and 5 distinct groups of brain networks. In one group, the internal connectivity within the brain network exhibited abnormalities. The correlation analysis between ALFF and ReHo values and clinical scales revealed a significant negative correlation between the bilateral hippocampus and Mini-Mental State Examination (MMSE) scores. Conversely, ReHo values for Postcentral_R and SupraMarginal_L were significantly positively correlated with MMSE scores. In summary, the results of our study suggest that patients diagnosed with BD-MCI display atypical activity across several brain regions. The observed changes in these areas encompass a range of functional networks. The reduced coordination among these functional networks may play a role in the deterioration of cognitive functions and decision-making capabilities, potentially serving as a critical mechanism contributing to the early manifestation of cognitive impairments.

PMID:39995946 | PMC:PMC11847846 | DOI:10.3389/fnagi.2025.1522591

Front Neurol. 2025 Feb 10;16:1498049. doi: 10.3389/fneur.2025.1498049. eCollection 2025.

ABSTRACT

INTRODUCTION: The brain's spontaneous neural activity can be recorded during rest using resting state functional magnetic resonance imaging (rs-fMRI), and intricate brain functional networks and interaction patterns can be discovered through correlation analysis. As a crucial component of rs-fMRI analysis, effective connectivity analysis (EC) may provide a detailed description of the causal relationship and information flow between different brain areas. It has been very helpful in identifying anomalies in the brain activity of depressed teenagers.

METHODS: This study explored connectivity abnormalities in brain networks and their impact on clinical symptoms in patients with depression through resting state functional magnetic resonance imaging (rs-fMRI) and effective connectivity (EC) analysis. We first introduce some common EC analysis methods, discuss their application background and specific characteristics.

RESULTS: EC analysis reveals information flow problems between different brain regions, such as the default mode network, the central executive network, and the salience network, which are closely related to symptoms of depression, such as low mood and cognitive impairment. This review discusses the limitations of existing studies while summarizing the current applications of EC analysis methods. Most of the early studies focused on the static connection mode, ignoring the causal relationship between brain regions. However, effective connection can reflect the upper and lower relationship of brain region interaction, and provide help for us to explore the mechanism of neurological diseases. Existing studies focus on the analysis of a single brain network, but rarely explore the interaction between multiple key networks.

DISCUSSION: To do so, we can address these issues by integrating multiple technologies. The discussion of these issues is reflected in the text. Through reviewing various methods and applications of EC analysis, this paper aims to explore the abnormal connectivity patterns of brain networks in patients with depression, and further analyze the relationship between these abnormalities and clinical symptoms, so as to provide more accurate theoretical support for early diagnosis and personalized treatment of depression.

PMID:39995788 | PMC:PMC11847690 | DOI:10.3389/fneur.2025.1498049

Quant Imaging Med Surg. 2025 Feb 1;15(2):1653-1668. doi: 10.21037/qims-24-916. Epub 2025 Jan 22.

ABSTRACT

BACKGROUND: Migraine is a common, disabling, chronic headache disorder. Acupuncture is one of the effective complementary therapies for migraine. However, the neural mechanisms of acupuncture on migraine remain unclear. With the increased number of neuroimaging studies of acupuncture for migraine in recent years, there is an urgent need to update the data for pooled analyses. This study aimed to comprehensively summarize the relevant literature, identify brain regions with significant changes in brain activity after acupuncture, and explore the potential neural mechanisms of acupuncture on migraine.

METHODS: A search was conducted by two independent researchers for neuroimaging studies using resting-state functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) on the effects of acupuncture on migraine up to October 2023 in the databases of PubMed, MEDLINE, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data, Chinese Science and Technology Journal Database (VIP), and Chinese Biomedical Literature Database (SinoMed). Observational studies and clinical trials in Chinese or English were included; abstracts and studies without peer review were excluded. Brain regions with increased or decreased activity in the true acupuncture (TA) and sham acupuncture (SA) groups were pooled. A meta-analysis was performed using the activation likelihood estimation (ALE) algorithm. Fail-safe N (FSN) analysis was performed for publication bias and jackknife analysis was implemented for sensitivity analysis.

RESULTS: The ALE meta-analysis included 15 peer-reviewed functional brain imaging studies with 514 migraine patients (401 female; mean age 32.38 years) and 163 healthy controls (130 female; mean age 27.28 years). A total of 12 studies scored 18 and above on the quality assessment (out of a total of 20). The results showed two increased activity clusters (the left pons and posterior insula) and four decreased activity clusters [the left cerebellum, temporal lobe, and right precuneus (two clusters)] after TA relative to baseline (P<0.001 uncorrected, volume >100 mm3). We also identified five clusters of increased and seven clusters of decreased activity of SA relative to the baseline, and no overlap regions were found between the TA and SA groups (P<0.001 uncorrected, volume >100 mm3). The results showed high replicability and reliability.

CONCLUSIONS: Acupuncture for migraine is a complex but targeted neuromodulation process, different from the random, nonspecific effects of SA. Emotional processing and sensitization reduction may be critical neurofunctional mechanisms of acupuncture. More high-quality randomized controlled studies are needed to validate the results.

PMID:39995740 | PMC:PMC11847202 | DOI:10.21037/qims-24-916

Autism Res. 2025 Feb 25. doi: 10.1002/aur.70008. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder characterized by atypical brain development. Previous whole-brain BrainAGE studies have unveiled the presence of accelerated or delayed brain function developmental patterns in individuals with ASD. However, it remains unclear whether these patterns manifest at a global level throughout the entire brain or are specific to certain functional sub-networks. The study included resting-state functional magnetic resonance imaging (fMRI) data from 127 individuals with ASD and 135 healthy controls (aged between 5 and 40 years). ALFF maps were measured for each participant. Then, sub-network-level BrainAGE analyses were conducted across 10 sub-networks using the Individual-weighted Multilayer Perceptron Network (ILWMLP) regression method. The BrainAGE analyses revealed atypical developmental trajectories in sensorimotor-related sub-networks, encompassing auditory, motor, and sensorimotor sub-networks. In individuals with ASD, delayed brain function development was observed in the auditory and sensorimotor networks, with a more pronounced delay observed in older individuals. Conversely, the motor network exhibited accelerated development in younger individuals but delayed development in older individuals. Our findings unveiled aberrant developmental patterns in sensorimotor-related sub-networks among individuals with ASD, exhibiting distinct atypical profiles across different sub-networks. These results might contribute to a deeper understanding of the deviant brain development observed in ASD.

PMID:39995361 | DOI:10.1002/aur.70008

Cogn Neurodyn. 2025 Dec;19(1):41. doi: 10.1007/s11571-025-10223-3. Epub 2025 Feb 20.

ABSTRACT

Rumination thinking is a type of negative repetitive thinking, a tendency to constantly focus on the causes, consequences and other aspects of negative events, which has implications for a variety of psychiatric disorders. Previous studies have confirmed a strong association between rumination thinking and poor sleep or insomnia, but the direction of causality between the two is not entirely clear. This study examined the relationship between rumination thinking and sleep quality using a longitudinal approach and resting-state functional MRI data. Participants were 373 university students (males: n = 84, 18.67 ± 0.76 years old) who completed questionnaires at two time points (T1 and T2) and had resting-state MRI data collected. The results of the cross-lagged model analysis revealed a bidirectional causal relationship between rumination thinking and sleep quality. Additionally, the functional connectivity (FC) of the precuneus and lingual gyrus was found to be negatively correlated with rumination thinking and sleep quality. Furthermore, mediation analysis showed that rumination thinking at T1 fully mediated the relationship between FC of the precuneus-lingual and sleep quality at T2. These findings suggest that rumination thinking and sleep quality are causally related in a bidirectional manner and that the FC of the precuneus and lingual gyrus may serve as the neural basis for rumination thinking to predict sleep quality. Overall, this study provides new insights for enhancing sleep quality and promoting overall health.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10223-3.

PMID:39991016 | PMC:PMC11842644 | DOI:10.1007/s11571-025-10223-3

Ther Adv Neurol Disord. 2025 Feb 21;18:17562864251319870. doi: 10.1177/17562864251319870. eCollection 2025.

ABSTRACT

BACKGROUND: Emerging evidence indicates that gut inflammatory and immune response play a key role in the pathophysiology of stroke and may become a promising therapeutic target. However, the specific role of the microbiota-gut-brain axis in poststroke aphasia (PSA) patients remains unclear.

OBJECTIVES: The aim of this study was to investigate the relationships among the gut microbiota, neuroendocrine-immune network, brain network properties, and language function in patients with PSA.

DESIGN: This is a cross-sectional, observational, monocentric study.

METHODS: This study enrolled 15 PSA patients, 10 non-PSA patients, and 15 healthy controls (HCs). All subjects underwent stool microbiota analysis, blood inflammatory cytokines assessment, and brain-gut peptide examination. PSA patients and HCs underwent additional resting-state functional MRI (rs-fMRI) brain scans. The rs-fMRI data were utilized to create whole-brain connectivity maps, and graph theory was employed to characterize the network topological properties. Analysis of variance and the Kruskal-Wallis test were used for comparisons among the three groups. Correlation analyses were subsequently conducted to explore relationships among factors showing significant group differences.

RESULTS: Compared with non-PSA patients and HCs, PSA patients displayed alterations in the gut microbiota composition, increased systemic inflammation, changes in brain-gut peptides, and had worse language performance. Graph theoretical analysis revealed that PSA patients exhibited small-world topology. Furthermore, nodal measures in brain network analysis showed activation of homologous speech areas in the right hemisphere, while the nodal properties of brain regions near the lesion in the left hemisphere decreased in patients with PSA compared with HCs.

CONCLUSION: The present study revealed, for the first time, that an imbalance in gut microbiota was accompanied by the neuroendocrine-immune network disorder and abnormal changes in the brain network in PSA patients.

PMID:39990867 | PMC:PMC11846115 | DOI:10.1177/17562864251319870

Sichuan Da Xue Xue Bao Yi Xue Ban. 2024 Nov 20;55(6):1386-1395. doi: 10.12182/20241160108.

ABSTRACT

OBJECTIVE: To investigate the changes in brain dynamic functional connectivity (dFC) in children and adolescents with idiopathic generalized epilepsy (IGE) who have negative findings for conventional magnetic resonance imaging (MRI) and to explore the correlation between dFC indicators and clinical variables.

METHODS: A total of 40 children and adolescents with IGE who have negative findings for routine brain MRI and 37 healthy controls were enrolled. T2-fluid attenuated inversion recovery (T2-FLAIR) was performed for all subjects. They also uinderwent 3-dimensional T1 weighted imaging (3D-T1WI) and resting-state functional MRI (rs-fMRI). Using independent component analysis (ICA), sliding time windows, and k-means clustering, we identified 6 functional connectivity states and derived dFC indicators, including fraction of time, mean dwell time, and the number of transitions. Then, SPSS18.0 and GIFT software Stats module were used to analyze the intergroup differences in dFC and its correlation with clinical variables. The reliability and stability of the dFC results were validated by changing the size of the sliding window.

RESULTS: There were no significant differences in the general clinical data between the IGE group and the control group (P>0.05). Compared with the control group, the IGE group showed in state 5 increased dFC within the default mode network (DMN), increased dFC between DMN and the frontoparietal network (FPN), and decreased dFC between DMN and the visual network (VN) (P<0.001). In state 6, the IGE group showed increased dFC between DMN and VN, increased dFC between the basal ganglia network (BGN) and the sensorimotor network (SMN), decreased dFC between the DMN and the attention network (ATTN), and decreased dFC within the VN (P<0.001). There were statistically significant differences between the two groups in the fraction of time (Z=－2.192, P=0.028) and the mean dwell time (Z=－2.144, P=0.032) in state 1, in the fraction of time (Z=－2.444, P=0.015) and the mean dwell time (Z=－2.368, P=0.018) in state 4, and in the fraction of time (Z=－2.047, P=0.041) in state 6. There was a negative correlation between the duration of the disease and the fraction of time of state 1 in the IGE group (r=－0.421, P=0.007, Bonferroni correction). In the validation analysis, significant differences in dFC indicators between the IGE group and the control group persisted when the size of the sliding window and the number of clusters were changed.

CONCLUSION: Children and adolescents with IGE and negative findings for conventional MRI exhibit abnormal dynamic properties of whole-brain functional connectivity, and the fraction of time of state 1 in IGE patients is correlated with clinical variables, providing new imaging evidence for research in the neural mechanisms of children and adolescents with IGE.

PMID:39990822 | PMC:PMC11839365 | DOI:10.12182/20241160108

medRxiv [Preprint]. 2025 Feb 14:2025.02.14.25322265. doi: 10.1101/2025.02.14.25322265.

ABSTRACT

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data from the multicenter Enhancing Neuro-Imaging and Genetics through Meta-Analysis (ENIGMA)-OCD consortium. Baseline clinical and resting-state functional magnetic imaging (rs-fMRI) data from 159 adult patients aged 18-60 years (88 female) with OCD who received CBT at four treatment/neuroimaging sites were included. Fractional amplitude of low frequency fluctuations, regional homogeneity and atlas-based functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p=0.001). Lower baseline symptom severity, younger age, an absence of cleaning obsessions, unmedicated status, and higher education had the highest model impact in predicting remission. The best predictive performance using only rs-fMRI was obtained with regional homogeneity for remission (AUC=0.59). Predicting response with rs-fMRI generally did not exceed chance level. Machine learning models based on clinical data may thus hold promise in predicting remission after CBT for OCD, but the predictive power of multicenter rs-fMRI data is limited.

PMID:39990555 | PMC:PMC11844585 | DOI:10.1101/2025.02.14.25322265

J Can Assoc Gastroenterol. 2025 Feb 21;8(Suppl 2):S36-S43. doi: 10.1093/jcag/gwae031. eCollection 2025 Mar.

ABSTRACT

Crohn's disease and ulcerative colitis are 2 subtypes of Inflammatory Bowel Disease (IBD). The chronic, alternating periods of relapsing, and remitting inflammation of the gastrointestinal tract that underlie these diseases trigger a range of gut-related symptoms, in addition to being related to burdensome psychological and cognitive comorbidities. With advancing knowledge of the brain-gut axis and its dysregulation in diseases such as IBD, understanding IBD-related brain changes is an important focus for current research in this area. "Resting state" function refers to the spontaneous fluctuations in neural activity when a person is awake and resting-not focussing attention on a task or stimulus. The recent surge in human resting-state functional magnetic resonance imaging (rs-fMRI) studies suggest that resting function is altered in IBD, representing a potential neural biomarker to target in the development of novel interventions. There are, however, multiple factors that contribute to the approach of these studies, including factors related to participant sample characteristics (IBD subtype and incorporation of disease activity in group definition and comparison), application of different resting-state metrics to assess resting brain activity (via regional homogeneity or amplitude of low-frequency fluctuations) or functional connectivity (via independent component analysis, region-of-interest, seed-to-voxel, or graph theory analyses) and incorporation of additional, multimodal variables of interest. The present review provides a summary of current approaches to studying resting-state brain function in IBD, the most commonly identified brain regions/networks to exhibit aberrant function, and avenues for advancement that forthcoming research in this field can strive to address.

PMID:39990517 | PMC:PMC11842902 | DOI:10.1093/jcag/gwae031

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Feb 21:S2451-9022(25)00062-X. doi: 10.1016/j.bpsc.2025.02.005. Online ahead of print.

ABSTRACT

INTRODUCTION: Generalized anxiety disorder (GAD) frequently emerges during childhood or adolescence, yet, few studies have examined functional connectivity differences in youth GAD. Functional MRI studies of adult GAD have implicated multiple brain regions; however, frequent examination of individual brain seed regions and/or networks has limited a holistic view of GAD-associated differences. The current study therefore used resting-state fMRI data from the Adolescent Brain Cognitive Development study to investigate connectivity in youth with GAD across multiple cortical networks and subcortical regions implicated in adult GAD, considering diagnosis changes across two assessment periods.

METHODS: Within- and between-network connectivity in 164 youth with GAD and 3158 healthy controls for 6 cortical networks and 6 subcortical regions was assessed using linear mixed effect models. Changes in GAD-associated connectivity between baseline and 2-year follow-up were then compared for subjects with: continuous GAD, GAD at baseline and not follow-up (GAD-remitters), GAD at follow-up and not baseline (GAD-converters), and controls.

RESULTS: Youth with GAD showed greater within-ventral attention network (VAN) connectivity, and hyperconnectivity between the amygdala and cingulo-opercular network, and between striatal regions and the cingulo-opercular, default mode, and salience networks (FDR p<0.05). Within-VAN connectivity decreased for GAD-remitters between baseline and follow-up. Sensitivity analyses revealed that these hyperconnectivity patterns were not observed in major depressive disorder (n=19), separation anxiety (n=33), or social anxiety disorder (n=111) without GAD.

DISCUSSION: Results indicate that GAD in childhood and adolescence is associated with altered subcortical to cortical network connectivity, and that within-VAN hyperconnectivity, in particular, is associated with clinically-significant GAD-specific symptoms.

PMID:39988295 | DOI:10.1016/j.bpsc.2025.02.005

Neuroscience. 2025 Feb 21:S0306-4522(25)00168-X. doi: 10.1016/j.neuroscience.2025.02.050. Online ahead of print.

ABSTRACT

Previous studies have revealed that phonological and semantic processing recruit separate brain networks. However, the intrinsic functional connectivity patterns of the phonological and semantic networks remain unclear. To address this issue, the present study explored the static and dynamic functional connectivity patterns of phonological and semantic networks during the resting state. The static functional connectivity pattern of the two networks was examined by adopting a voxel-based global brain connectivity (GBC) method. In this analysis, we estimated the within-network connectivity (WNC), between-network connectivity between phonological and semantic networks (BNC_PS), and between-network connectivity of the two language networks (i.e., phonological and semantic networks) with the non-language network (BNC_N). The results showed that both phonological and semantic networks exhibited stronger intra-network connectivity (i.e., WNC) than inter-network connectivity (i.e., BNC_PS and BNC_N), indicating that both networks are relatively encapsulated. The results of dynamic functional connectivity found that for a portion of the time, the two networks showed positive intra-network connectivity and negative inter-network connectivity. Taken together, our results revealed that the phonological and semantic networks showed an intra-network integration and inter-network segregation pattern. These findings deepen our understanding of the intrinsic functional connectivity patterns of phonological and semantic networks.

PMID:39988194 | DOI:10.1016/j.neuroscience.2025.02.050

J Affect Disord. 2025 Feb 21:S0165-0327(25)00282-4. doi: 10.1016/j.jad.2025.02.076. Online ahead of print.

ABSTRACT

BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.

METHODS: Three graph neural networks (GNN) models were applied to three types of causal connectomes (CCs): granger causality, regression DCM (rDCM), and TwoStep, obtained from a total of 1296 young adult participants in three large-scale datasets.

RESULTS: GNN models showed better performance for predicting depression when using causal connectomes such as TwoStep (average precision score, 0.882), granger causality (0.878), or rDCM (0.853) compared with using functional connectomes like Pearson's (0.850) and partial (0.823) correlation. Notably, nodal features derived only from rDCM and TwoStep showed spatial associations with positron emission tomography measures of receptors for neurotransmitters such as dopamine and serotonin. Further analysis revealed the shared directed edges among the subject's edge features, which included cortical causal connections in networks such as the default mode, control, dorsal attention, peripheral visual, and parietofrontal networks.

LIMITATIONS: The classification performance of leave-one-site-out cross-validation did not achieve a similar level with that of 10-fold cross-validation.

CONCLUSIONS: Our findings suggest that the connectomes derived from CCs using GNN, rather than functional connectomes, provide more accurate and neurobiologically relevant information for depression. Moreover, the observed spatial heterogeneity of this relevance and subject-specific edge features emphasizes the complexity of depression. These results have the potential to advance our understanding of depression's nature and potentially contribute to precision psychiatry by aiding in its diagnosis and treatment.

PMID:39988139 | DOI:10.1016/j.jad.2025.02.076

Psychiatry Res Neuroimaging. 2025 Feb 12;348:111962. doi: 10.1016/j.pscychresns.2025.111962. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the correlation between the theory of mind and the amplitude difference of low-frequency fluctuations in resting-state fMRI.

METHODS: This study included 38 depressed adolescents who had attempted suicide (SU group), 53 depressed patients who had not attempted suicide (NSU group), and 20 healthy controls (HC group). All participants used the 17-item Hamilton Depression Scale. The low-frequency fluctuation amplitude (zALFF) values were calculated using resting-state functional magnetic resonance imaging and compared between the groups. The theory of mind story picture task (theory of mind-picture sequencing task, ToM-PST) were used to test the psychological theory level of the three groups. Statistical analysis of the data was performed using SPSS 25.0. One-way ANOVA was used to compare the differences between the three groups. Pearson correlation analysis was used to explore the correlation between zALFF values and psychological theoretical damage in specific brain regions.

RESULTS: Significant zALFF values were found between the three groups (GRF correction), with decreased zALFF values in both the SU and NSU groups compared to HC. In the adolescent SU group, the primary belief, primary false belief, and deception scores were significantly higher than those in the NSU group. The primary false belief, reality, and deception detection scores were significantly lower than those in the HC group (all P < 0. 05). Pearson Correlation analysis showed that the zALFF value of the left dorsolateral superior frontal gyrus was significantly negatively correlated with secondary false beliefs, reciprocity, and the total score. (all P < 0.05).

CONCLUSION: Juvenile patients with depressive disorder with suicide attempts showed an ability to understand secondary false beliefs, reciprocity, and total scores. This ability showed a significant negative correlation with low-frequency fluctuation amplitude values in the left dorsolateral superior frontal gyri.

PMID:39985962 | DOI:10.1016/j.pscychresns.2025.111962

Brain Imaging Behav. 2025 Feb 22. doi: 10.1007/s11682-025-00986-y. Online ahead of print.

ABSTRACT

OBJECTIVES: Tinnitus affects millions worldwide. Its neural mechanisms remain unclear. This study aimed to explore the causal relationships between brain functional networks and tinnitus risk using Mendelian randomization (MR) analyses.

METHODS: We performed MR analyses using brain activity data from resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data from genome-wide association studies (GWAS). A total of 191 brain features, including amplitude traits and functional connectivity measures, were selected based on their genetic associations.

RESULTS: Forward MR analyses showed that increased activity in the parietal and inferior frontal regions was associated with a 41% reduction in tinnitus risk (OR = 0.59, p = 1.8 × 10-4). In contrast, increased activity in the precuneus, angular gyrus, and frontal areas was linked to a 49% increase in tinnitus risk (OR = 1.49, p = 8.9 × 10-4). Activities in the parietal and inferior frontal regions were negatively correlated with tinnitus risk (OR = 0.72, p = 0.0037). Additionally, higher activity in the parietal, frontal, and temporal regions doubled the risk (OR = 2.02, p = 0.015). Reverse MR showed that stronger connectivity between frontal and temporal regions was inversely related to tinnitus risk (beta = - 0.056, p = 0.049).

CONCLUSIONS: Specific brain activity and connectivity patterns are causally linked to tinnitus.

PMID:39984808 | DOI:10.1007/s11682-025-00986-y

Mol Psychiatry. 2025 Feb 21. doi: 10.1038/s41380-025-02897-2. Online ahead of print.

ABSTRACT

Chronic stress is a causal antecedent condition for major depressive disorder and associates with altered patterns of neural connectivity. There are nevertheless important individual differences in susceptibility to chronic stress. How functional connectivity (FC) amongst interconnected, depression-related brain regions associates with resilience and susceptibility to chronic stress is largely unknown. We used resting-state functional magnetic resonance imaging (rs-fMRI) to examine FC between established depression-related regions in susceptible (SUS) and resilient (RES) adult mice following chronic social defeat stress (CSDS). Seed-seed FC analysis revealed that the ventral dentate gyrus (vDG) exhibited the greatest number of FC group differences with other stress-related limbic brain regions. SUS mice showed greater FC between the vDG and subcortical regions compared to both control (CON) or RES groups. Whole brain vDG seed-voxel analysis supported seed-seed findings in SUS mice but also indicated significantly decreased FC between the vDG and anterior cingulate area compared to CON mice. Interestingly, RES mice exhibited enhanced FC between the vDG and anterior cingulate area compared to SUS mice. Moreover, RES mice showed greater FC between the infralimbic prefrontal cortex and the nucleus accumbens shell compared to CON mice. These findings indicate unique differences in FC patterns in phenotypically distinct SUS and RES mice that could represent a neurobiological basis for depression, anxiety, and negative-coping behaviors that are associated with exposure to chronic stress.

PMID:39984680 | DOI:10.1038/s41380-025-02897-2