Most recent paper

Eur Child Adolesc Psychiatry. 2025 Nov 12. doi: 10.1007/s00787-025-02906-3. Online ahead of print.

ABSTRACT

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder that often persists from childhood into adolescence and adulthood. Resting-state functional magnetic resonance imaging (rs-fMRI) provides valuable insights into the intrinsic neural activity associated with ADHD. However, despite increasing neuroimaging research, the developmental specificity of spontaneous alterations in brain activity in children and adolescents with ADHD remains poorly understood. A comprehensive activation likelihood estimation (ALE) meta-analysis was performed on rs-fMRI data to investigate alterations in spontaneous brain activity in children and adolescents with ADHD compared with healthy controls (HCs). A contrast analysis was conducted to assess potential overlap in altered brain regions between the child and adolescent ADHD groups. The robustness of the findings was evaluated using a jackknife sensitivity analysis. A systematic review of the literature identified 28 rs-fMRI studies (1019 ADHD patients and 943 HCs). In children with ADHD, ALE revealed decreased spontaneous neural activity in the left middle frontal gyrus, superior frontal gyrus, medial frontal gyrus, precentral gyrus, and subgyral region, with no regions showing increased activity. In adolescents with ADHD, increased activity was observed in the bilateral paracentral lobule, left postcentral gyrus, and medial frontal gyrus, whereas decreased activity was found in the cerebellar tonsil, uvula, declive, anterior lobe, and superior/medial frontal gyrus. No significant clusters were identified in the contrast analyses. The jackknife sensitivity analysis demonstrated robustness in 9 of 17 iterations for children and 4 of 5 iterations for adolescent-specific cerebellar findings. Spontaneous alterations in brain activity in children and adolescents with ADHD reflect developmentally distinct neural mechanisms and may guide future age-specific neuroimaging research.

PMID:41222592 | DOI:10.1007/s00787-025-02906-3

Mov Disord. 2025 Nov 12. doi: 10.1002/mds.70075. Online ahead of print.

ABSTRACT

BACKGROUND: Depression frequently precedes motor symptoms in Huntington's disease gene expansion carriers (HDGECs), yet the neural mechanisms remain poorly characterized.

OBJECTIVE: We investigated effective connectivity between the default mode network (DMN) and striatal regions in HDGECs.

METHODS: We analyzed 3-T resting-state functional magnetic resonance imaging data from 98 HDGECs (48.98% females; mean age, 42.82 years). Spectral dynamic causal modeling estimated subject-level connectivity, whereas parametric empirical Bayes determined group-level effective connectivity differences between participants with a diagnosed depression history and those without, across current, remitted, and never-depressed states. Brain-behavior associations with clinical depression measures were examined.

RESULTS: Model estimation was excellent (89.82% variance-explained). HDGECs with depression history showed decreased inhibitory posterior cingulate cortex-to-hippocampal connectivity, increased hippocampus-to-posterior cingulate cortex inhibition, and increased inhibitory influence of striatum on DMN. HDGECs with a depression history showed increased inhibitory striatal influence on DMN, including left putamen, a propensity for right hippocampal involvement, and disinhibitory posterior cingulate-hippocampal connectivity. Current versus never-depressed comparisons showed more pronounced dysconnectivity, with stronger striatum-to-network connections. Current versus remitted depression exhibited distinct patterns with increased medial prefrontal cortex-to-posterior cingulate cortex connectivity, increased medial prefrontal cortex self-connectivity, and decreased posterior cingulate cortex-to-medial prefrontal cortex connectivity.

CONCLUSIONS: These findings establish distinct striatal-network interaction patterns in depression for HDGECs that differ from non-neurological depression. Our findings suggested the posterior DMN-posterior cingulate and hippocampus-as drivers of depression for HDGECs and potential involvement of right DMN in keeping with compensatory patterns broadly in HD. These connectivity patterns could serve as functional biomarkers for depression in HDGECs. © 2025 International Parkinson and Movement Disorder Society. © 2025 International Parkinson and Movement Disorder Society.

PMID:41221779 | DOI:10.1002/mds.70075

Cogn Neurodyn. 2025 Dec;19(1):182. doi: 10.1007/s11571-025-10369-0. Epub 2025 Nov 8.

ABSTRACT

To automate the classification of functional brain networks in epilepsy patients using resting-state functional magnetic resonance imaging (rs-fMRI). The study introduces a deep learning framework that leverages spatial and temporal features to classify Independent Component Analysis (ICA)-derived networks into 11 functionally distinct classes, including seizure onset zone (SoZ), resting-state networks (RSNs), and artifact/noise. A hybrid deep learning architecture was developed combining a 3D Convolutional Neural Network (3D-CNN) to extract spatial features (SF) and a Long Short-Term Memory (LSTM) network to capture temporal dynamics from time-domain (TS) and frequency-domain (FS) signals. These multi-domain features were concatenated and classified into 11 distinct ICA component types. An ablation study assessed the individual and combined contributions of spatial, temporal, and spectral features. Additionally, expert neurologists independently rated four representative cases to qualitatively validate the model's interpretability and clinical relevance. The baseline 3D CNN (SF) model achieved an overall accuracy of 69% with a sensitivity of 0.52 and a ROC AUC of 0.76. Incorporating frequency-domain signals (SF + FS) enhanced sensitivity to 0.54 and improved the ROC AUC to 0.78 while maintaining a similar accuracy. Combining both time-domain and frequency-domain signals (SF + TS + FS) yielded the highest accuracy at 70%. At the class level, the Noise class consistently demonstrated robust performance (up to 0.94), whereas the temporal lobe network class Temporal class exhibited lower scores (0.14-0.24) across all configurations. Our results demonstrate that this data-driven framework can effectively automate the classification of rs-fMRI-derived functional brain networks including SoZ thereby reducing subjectivity and workload in clinical review. The inclusion of spatial, temporal, and spectral information enables a richer and more nuanced classification that supports downstream applications in epilepsy surgical planning.

PMID:41220406 | PMC:PMC12598743 | DOI:10.1007/s11571-025-10369-0

Brain Behav. 2025 Nov;15(11):e71058. doi: 10.1002/brb3.71058.

ABSTRACT

BACKGROUND: Neurovascular coupling (NVC) is the functional mechanism that links brain neural activity with the dynamic regulation of local blood flow and oxygenation. In recent years, there has been an increasing academic attention to the role of NVC in its pathophysiology and the application of new technologies.

OBJECTIVE: This study aims to map the research landscape related to NVC through scientometric analysis.

METHODS: Publications from the past 30 years were retrieved from the Web of Science Core Collection (WoSCC) database. Data were analyzed using CiteSpace, VOSviewer, and the bibliometrix R package, including co-citation and keyword co-occurrence network analyses. Key metrics such as publication counts and citation frequencies were assessed to identify trends and collaboration patterns among countries, institutions, and authors.

RESULTS: Among the 2047 articles included in the study, United States has maintained a clear leading position. Meanwhile, the number of Chinese research participants has grown rapidly over the past decade. The most prolific authors were Professors Iadecola Costantino and Tarantini Stefano. The research findings of Professor Tarantini Stefano have been widely recognized by researchers in the field. Keyword analysis identified "cerebral blood flow," "neuronal activity," and "neurovascular coupling" as dominant terms, emphasizing the central role of brain function and imaging techniques such as fMRI, TCD, and optical imaging. The emergence of "fNIRS," "resting-state fMRI," and "autoregulation" highlights the growing impact of noninvasive neuroimaging in studying brain-blood flow interactions. Cluster analysis revealed key research themes including functional connectivity, nitric oxide-mediated vascular regulation, cerebral autoregulation, Alzheimer's disease metabolism, and CO2-induced hemodynamic modulation.

CONCLUSION: Over the past three decades, NVC has emerged as a key research focus, driven by interdisciplinary collaboration and advances in brain connectivity, dysfunction, and technology. In the future, integrating artificial intelligence, multi-omics analysis, and high-resolution imaging will further elucidate NVC mechanisms in health and disease, promoting interdisciplinary translation and breakthroughs in neuroscience and brain health.

PMID:41220183 | DOI:10.1002/brb3.71058

BMC Psychiatry. 2025 Nov 11;25(1):1078. doi: 10.1186/s12888-025-07535-3.

ABSTRACT

BACKGROUND: Aerobic exercise (AE) has emerged as a promising non-pharmacological intervention for depression. However, the extent to which AE can alleviate depressive symptoms varies across individuals, and the neural or clinical factors that relate to treatment response remain incompletely understood. This study aimed to investigate whether baseline amygdala-based functional connectivity (FC) is associated with symptom improvement following AE and identify neural correlates of treatment responsiveness.

METHODS: Forty-three participants with subthreshold depression (StD) completed an AE intervention and were classified as remitters (n = 21) or non-remitters (n = 22) based on post-AE Patient Health Questionnaire-9 (PHQ-9) scores. Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired at baseline and after the intervention. Group comparisons and partial correlation analyses were conducted to assess associations between amygdala-based FC and depressive symptom outcomes.

RESULTS: Baseline left amygdala FC was positively correlated with post-AE PHQ-9 scores in the right precuneus and bilateral middle frontal gyrus (MFG), and negatively correlated with changes in PHQ-9 (ΔPHQ-9) scores in the left precuneus and left MFG. Additionally, remitters showed reduced FC between the left amygdala and left supplementary motor area (SMA) compared to non-remitters. Baseline right amygdala FC was positively correlated with post-AE PHQ-9 scores in the left inferior parietal lobe (IPL), right middle temporal gyrus (MTG), left superior medial frontal gyrus (mSFG) and left MTG, but there were no significant findings for ΔPHQ-9 or group differences. An exploratory analysis combining bilateral amygdala FC and clinical variables yielded high classification accuracy within the same sample (AUC = 0.93). A significant group effect was also observed in the right MTG for left amygdala FC, though no group × time interaction emerged.

CONCLUSIONS: Baseline amygdala FC is associated with symptom improvement during AE in StD. These exploratory findings suggest that amygdala connectivity may play a role in treatment responsiveness.

CLINICAL TRIAL NUMBER: Not applicable.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-025-07535-3.

PMID:41219928 | PMC:PMC12607056 | DOI:10.1186/s12888-025-07535-3

Zhonghua Jie He He Hu Xi Za Zhi. 2025 Nov 12;48(11):1020-1027. doi: 10.3760/cma.j.cn112147-20250303-00122.

ABSTRACT

Objective: To explore the characteristics of altered brain functional activity in patients with chronic cough using resting-state functional magnetic resonance imaging (fMRI). Methods: This was a prospective study. From January 2016 to January 2019, a total of 20 patients with refractory chronic cough [10 males and 10 females, (39.3±8.2) years], 19 patients with somatic cough syndrome [14 males and 5 females, (34.5±9.2) years], and 29 healthy controls [19 males and 10 females, (38.3±12.1) years] were recruited from the chronic cough outpatient clinic of the First Affiliated Hospital of Guangzhou Medical University for analysis. All participants underwent resting-state fMRI, as well as assessment of cough severity, and capsaicin cough challenge. The amplitude of low-frequency fluctuations (ALFF) was used to assess brain functional activity. First, differences in brain activity between patients with refractory chronic cough and healthy controls were compared. Subsequently, brain regions showing significant differences were selected as seed points, and seed-based whole-brain functional connectivity (FC) analyses were performed to examine group differences. Cough severity was evaluated using the visual analog scale (VAS), and cough sensitivity was defined as the capsaicin concentration that elicited five coughs (C5), expressed as lgC5. One-way analysis of variance (ANOVA) was used to compare the differences in lung function among groups. The Kruskal-Wallis test was applied to compare the differences in cough symptom scores (VAS) and capsaicin cough sensitivity (lgC5) among groups. The fMRI data were statistically analyzed using Rest 1.8 software, and two independent-sample t-tests were conducted for each group. Results: Patients with refractory chronic cough exhibited significantly higher ALFF values in the right cerebellar region 8 (0.96±0.14 vs. 0.72±0.15, t=5.46, P<0.001) and the right cerebellar region Crus2 (0.87±0.11 vs. 0.68±0.11, t=6.25, P<0.001) than healthy controls. Patients with somatic cough syndrome had significantly higher ALFF values in the rectus frontal muscle than healthy controls (1.19±0.26 vs. 0.90±0.16, t=4.92, P<0.001). With the right cerebellar region 8 as the seed point, the analysis of the whole brain FC showed that patients with refractory chronic cough had higher FC values in the left cerebellar region 8 (0.60±0.18 vs. 0.35±0.15, t=5.47, P<0.001), cerebellar vermis (0.85±0.17 vs. 0.69±0.16, t=5.26, P<0.001), and claustrum (0.33±0.13 vs. 0.14±0.10, t=6.02, P<0.001). With the right cerebellar region Crus2 as the seed point, the analysis of the whole brain FC showed that patients with refractory chronic had higher FC values in the right middle temporal gyrus, thalamus (0.31±0.17 vs. 0.10±0.11, t=5.57, P<0.001), right dorsolateral superior frontal gyrus (0.35±0.16 vs. 0.1±0.13, t=6.20, P<0.001) and right posterior central gyrus (0.41±0.19 vs. 0.17±0.17, t=4.52, P<0.001). In the correlation analysis, there was a moderate positive correlation (r=0.57, P=0.001) between the ALFF values of the right cerebellar region 8 and Crus2 regions in patients with refractory chronic cough. Conclusions: Enhanced FC in multiple brain regions was found in patients with refractory chronic cough and patients with somatic cough syndrome, suggesting central sensitization in these patients. The different active brain regions in patients with refractory chronic cough and patients with somatic cough syndrome indicate different central hypersensitivity mechanisms among different causes of chronic cough.

PMID:41218859 | DOI:10.3760/cma.j.cn112147-20250303-00122

J Affect Disord. 2025 Nov 9:120631. doi: 10.1016/j.jad.2025.120631. Online ahead of print.

ABSTRACT

BACKGROUND: Depression has been increasingly characterised as a disorder of functional brain connectivity. Over the last two decades aberrant functional connectivity of large-scale resting-state brain networks implicated in inhibitory cognitive control, affective regulation, and self-referential thought, has been compellingly linked to depression. Capitalising on network-based accounts of depression, subsequent research endeavours have aimed at identifying functional connectomic signatures of treatment response in depression. However, to date, there has been little research on the connectomic features of psychotherapy response.

METHODS: We investigated static and dynamic functional connectivity signatures of response to CBT in forty-six unmedicated patients with depression who underwent resting-state functional magnetic resonance imaging before and two months after completion of an Internet-delivered CBT intervention.

RESULTS: At baseline, responders dwelled in a brain state characterised by greater functional connectivity between cognitive control and affective networks. Conversely, functional connectivity between cognitive control and default mode networks was comparatively weaker in the responders group. Notably, baseline functional connectivity significantly classified CBT response at the individual level with an area under the receiver operating characteristic curve of 0.85.

CONCLUSION: These results are in accordance with current network-based accounts of CBT neural mechanisms, positing that greater cognitive control over negative emotion processing enables CBT response. This study extends previous findings on the network-based functional connectomic signatures of CBT response in depression.

PMID:41218741 | DOI:10.1016/j.jad.2025.120631

J Affect Disord. 2025 Nov 9:120652. doi: 10.1016/j.jad.2025.120652. Online ahead of print.

ABSTRACT

Metastability reflects the brain's dynamic balance between integration and segregation across networks, supporting flexible cognitive and behavioral functions. Although abnormal brain dynamics have been implicated in major depressive disorder (MDD), the alterations in metastable brain states and their clinical and transcriptomic correlates remain unclear. In this study, we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 569 patients with MDD and 563 healthy controls using leading eigenvector dynamics analysis (LEiDA), a phase-based method that captures transient brain states without predefined time windows. Between-group comparisons were performed at both global and modular levels, assessed their associations with clinical symptoms and their ability to predict depression severity. To explore underlying mechanisms, we integrated gene expression, cell-type specificity, and protein-protein interaction (PPI) networks. Patients with MDD exhibited widespread disruptions, including reduced global synchronization and metastability, but increased switching between states, particularly more frequent transitions from a globally coherent state (Global state) to a default mode-dominant state (DMN state). They also exhibited lower fractional occupancy of the Global state and higher fractional occupancy of a sensorimotor-dominant state. These disruptions were associated with symptoms such as insomnia and impaired insight, and predicted depression severity. Transcriptome-neuroimaging analysis revealed DMN state-related genes were enriched in pathways involved in presynaptic signal transduction and presynapse-to-nucleus signaling, and were preferentially expressed in excitatory and inhibitory neurons. CSMD1 emerged as a key hub gene in the PPI network. Our findings reveal widespread dynamic brain alterations in MDD and uncover their potential molecular mechanisms, providing new insights into the disorder's neurobiology.

PMID:41218738 | DOI:10.1016/j.jad.2025.120652

Sci Rep. 2025 Nov 11;15(1):39420. doi: 10.1038/s41598-025-23131-7.

ABSTRACT

Recording physiological signals during fMRI is valuable for multiple purposes but often requires additional setup, increasing complexity and participant discomfort. This is particularly challenging in simultaneous EEG-fMRI studies, which typically already include electrocardiogram (ECG) recordings. Here, we aim to leverage the known modulation of ECG by respiration to obtain an ECG-derived respiration (EDR) signal without extra equipment. We acquired EEG-fMRI data from 15 healthy subjects during resting state and two respiratory challenges (slow-paced breathing and breath-holding), with simultaneous ECG and respiratory recordings. Multiple methods were used to extract EDR signals, and the results were evaluated by comparing them with recorded respiration and assessing the quality of physiological regressors for denoising and cerebrovascular reactivity estimation. Amplitude-based EDR methods showed lower correlations with respiration, likely due to ECG distortion in the MRI. Nevertheless, coherence analysis showed that EDR preserved the relevant spectral content. EDR-based regressors were similar to those obtained from measured respiration. Notably, a method based on heart rate variability performed best overall, yielding physiological noise correction and reactivity estimates comparable to those using recorded respiration. Our results demonstrate that meaningful respiratory information can be extracted from ECG within the MRI environment, benefiting EEG-fMRI studies when respiration cannot be reliably recorded.

PMID:41219365 | DOI:10.1038/s41598-025-23131-7

Parkinsonism Relat Disord. 2025 Oct 28;142:108104. doi: 10.1016/j.parkreldis.2025.108104. Online ahead of print.

ABSTRACT

INTRODUCTION: As a deep learning algorithm, Graph convolutional network (GCN) can efficiently process graph-structured data to identify salient brain regions and brain connectivity patterns. We combine GCNs with a multi-connection pattern (MCGCN) to identify salient brain regions implicated in Essential Tremor (ET) and Dystonic Tremor (DT), aiming to explore the underlying neuropathological mechanisms of these conditions.

METHODS: Rs-fMRI data were collected from 55 ET patients, 51 DT patients, and 52 healthy controls (HCs). BOLD time series from each subject were extracted and functional connectivity (FC) matrices were constructed using three distinct connectivity modes. These matrices were then input to four GCN architectures for binary classification tasks (ET vs. HCs, DT vs. HCs, ET vs. DT). We utilized Grad-CAM to identify the more discriminative brain regions, and graph theory and correlation analyses were employed to validate the behavioral relevance of the discriminative regions identified by MCGCN, confirming the salient brain regions for ET and DT.

RESULTS: All GCN models demonstrated strong classification performance, with the highest mean accuracies of 91.36 % for DT vs. HCs, 85.91 % for ET vs. HCs, and 86.64 % for ET vs. DT. Discriminative brain regions were mainly localized in the basal ganglia, cerebello-thalamo-cortical motor circuitry, and non-motor cortical regions. Correlation analysis revealed that the nodal efficiency of the four salient brain regions was negatively correlated with clinical characteristics.

CONCLUSION: Our findings suggest the critical role of the classic tremor network in ET and DT pathogenesis, enhancing our comprehension of their FC-based pathophysiological mechanisms.

PMID:41218287 | DOI:10.1016/j.parkreldis.2025.108104

Psychoradiology. 2025 Oct 3;5:kkaf028. doi: 10.1093/psyrad/kkaf028. eCollection 2025.

ABSTRACT

In contemporary neuroscience, mapping the human brain's functional connectomes is essential to understanding its functional organization. Functional organizations in the brain gray matter have been the subject of previous research, but the functional information in white matter (WM), the other half of the brain, has been relatively underexplored. However, the dynamics of functional magnetic resonance imaging (fMRI) have been reliably identified in the brain WM. This review summarizes current knowledge about task-free (resting-state) fMRI neuroimaging analyses for the WM functional connectome. We present comparative findings of the WM functional connectome, including its mapping, physiological underpinnings, cognitive neuroscience relationships, and clinical applications. Furthermore, we explore the emerging consensus that WM functional networks have valid topological characteristics that can distinguish between individuals with brain diseases and healthy controls, predict general intelligence, and identify inter-subject variabilities. Lastly, we emphasize the need for further studies and the limitations, challenges, and future directions for the WM functional connectome. An overview of these developments could lead to new directions for cognitive neuroscience and clinical neuropsychiatry.

PMID:41216611 | PMC:PMC12596274 | DOI:10.1093/psyrad/kkaf028

Aging Dis. 2025 Oct 27. doi: 10.14336/AD.2025.0887. Online ahead of print.

ABSTRACT

Obesity is a recognized risk factor for age-related cognitive decline, with central (abdominal) obesity posing a particular strong threat to brain health. In a cross-sectional study of 89 cognitively healthy adults (52-79 years, mean 65.7 ± 6.4; 58 women), we compared the effects of central versus overall obesity on brain connectivity measured with resting-state fMRI. We focused on network segregation, an index of functional specialization that captures the balance between connections within and across large-scale brain networks. Central obesity, but not overall obesity, was associated with reduced segregation in associative and sensorimotor networks, even after adjusting for overall obesity, highlighting the role of abdominal fat accumulation. To explore underlying mechanisms, we combined a widely used clinical index of peripheral insulin resistance (HOMA-IR) with multimodal neuroimaging, including structural MRI for cortical thickness, T1w/T2w MRI for intracortical myelin, FDG-PET for glucose metabolism, and FBB-PET for Aβ load. Mediation analyses showed that central obesity was associated with insulin resistance, which was related to alterations in intracortical myelin, cortical glucose metabolism, and cortical Aβ accumulation. These changes were collectively linked to reduced network segregation. Modeling cortical Aβ load as preceding cortical glucose metabolism further revealed stronger and more widespread network disruption, which may reflect bidirectional interactions between amyloid pathology and metabolic dysfunction. These findings describe a pattern of metabolic and structural brain changes linked to central obesity that may compromise brain functional integrity. Although causality cannot be inferred from this cross-sectional design, targeting abdominal fat and related metabolic factors could help preserve brain health and reduce cognitive vulnerability with aging.

PMID:41213081 | DOI:10.14336/AD.2025.0887

IEEE Trans Med Imaging. 2025 Nov 10;PP. doi: 10.1109/TMI.2025.3631105. Online ahead of print.

ABSTRACT

Accurate identification of brain disorders enables timely intervention and improved patient outcomes. While numerous studies have developed AI models for resting-state functional magnetic resonance imaging (rs-fMRI) analysis, most rely on supervised learning, which can overlook hidden patterns that are less discriminatively associated with labels and require large annotated datasets. To address these limitations, we propose leveraging normative modeling, an unsupervised approach that constructs a model of normality based on healthy controls' data. Deviations from normality indicate potential disorders. However, applying normative modeling to rs-fMRI faces two significant challenges: constructing normality and ensuring explainability. To tackle these challenges, we propose BRAINEXA, a novel framework enhancing normative modeling for rs-fMRI-based brain disorder identification. Specifically, to construct accurate and stable normality, BRAINEXA introduces a training strategy that predicts more informative regions from less informative regions, discouraging trivial self-supervised learning solutions and improving representation learning without additional overhead. Furthermore, we incorporate spatiotemporal mutual information regularization to preserve distinctiveness between more informative regions and less informative regions during latent encoding, preventing potential representational distortions. For interpretability, BRAINEXA extracts normality-defining (ND) subregions, the core regions that characterize normal brain function. By combining ND subregions with anomaly scores, BRAINEXA can offer region- and connection-wise explanations that help identify clinically meaningful disruptions of normality in an unsupervised setting. We demonstrate the effectiveness of BRAINEXA on four public rs-fMRI datasets: REST-meta-MDD, ABIDE I, ADHD-200, and OASIS-3. Our code is available at https://github.com/ku-milab/BRAINEXA.

PMID:41212695 | DOI:10.1109/TMI.2025.3631105

Brain Struct Funct. 2025 Nov 10;230(9):174. doi: 10.1007/s00429-025-03039-8.

NO ABSTRACT

PMID:41212343 | DOI:10.1007/s00429-025-03039-8

iScience. 2025 Oct 8;28(11):113733. doi: 10.1016/j.isci.2025.113733. eCollection 2025 Nov 21.

ABSTRACT

Malevolent creativity (MC) involves generating original ideas to harm others, and it not only relies on cognitive flexibility but may also be related to the activities of emotional and motivational brain regions known as the subcortical regions. However, the relationship between the subcortical regions and MC performance remains unclear. We calculated dynamic graph-based analyses using resting-state fMRI. We found that MC originality was negatively correlated with functional connectivity (FC) between the right nucleus accumbens (NAcc) and cortical regions such as the right medial superior frontal gyrus (mSFG) and supplementary motor area (SMA). Similarly, benevolent creative (BC) originality was negatively correlated with FC between the right NAcc and SMA/superior frontal gyrus (SFG). MC malevolence was positively correlated with FC between the left caudate and postcentral gyrus and negatively correlated with FC between the right amygdala and SFG. These findings suggest that MC is associated with a complex interaction between the subcortical and cortical regions.

PMID:41210996 | PMC:PMC12590018 | DOI:10.1016/j.isci.2025.113733

Quant Imaging Med Surg. 2025 Nov 1;15(11):11262-11278. doi: 10.21037/qims-2025-736. Epub 2025 Oct 24.

ABSTRACT

BACKGROUND: Substantial evidence has shown the widespread structural and functional alterations within the white matter (WM) in patients with spinocerebellar ataxia type 3 (SCA3). However, investigation of the large-scale WM functional networks (WMFNs) remains incomplete in SCA3. This study aimed to comprehensively explore the functional organization, neural activity, and inter-network causal interactions within WMFNs relative to healthy controls (HCs).

METHODS: A total of 70 patients with SCA3 and 98 HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) and voxel-based morphometry. A total of 14 WMFNs were identified by K-means clustering algorithm, which were further classified as infratentorial, deep, middle, and superficial layers.

RESULTS: Dysfunctional WMFNs in SCA3 were mainly infratentorial, middle-layer, and deep-layer, with significantly decreased amplitudes in comparison with HCs [false discovery rate (FDR) corrected P<0.05]. In addition, the effective connectivity pattern within WMFNs in SCA3 was overall sparser than in HCs, whereas the directed connections from the dysfunctional WMFNs to the normal superficial-layer WMFNs and connections within the dysfunctional WMFNs were enhanced in SCA3 (FDR corrected P<0.05). Concurrently, the normal WMFNs showed reduced outflow strength of inter-network connections, whereas the dysfunctional WMFNs exhibited elevated outflow strength (FDR corrected P<0.05). Furthermore, the decline in neural activity and altered interactions observed can be partially attributed to the extent of WM volume (WMV) loss within the WMFNs, and are associated with the ataxia severity in SCA3 (P<0.05).

CONCLUSIONS: This study aimed to comprehensively explore the functional organization, neural activity, and inter-network causal interactions within WMFNs relative to HCs. The findings may improve understanding of the neuropathology of SCA3 and its progression throughout the nervous system from the perspective of WM function.

PMID:41209278 | PMC:PMC12591894 | DOI:10.21037/qims-2025-736

Quant Imaging Med Surg. 2025 Nov 1;15(11):11279-11291. doi: 10.21037/qims-2025-19. Epub 2025 Oct 20.

ABSTRACT

BACKGROUND: The coupling between cerebral blood flow (CBF) and blood oxygenation level-dependent signals at rest reflects the mechanism of neurovascular coupling (NVC), which holds great potential for the non-invasive assessment of developmental disorders in humans. However, this has not been illustrated in spastic cerebral palsy (SCP). This study aimed to evaluate alterations in NVC in children with SCP and to explore the clinical significance of these NVC changes.

METHODS: Twenty children with SCP (7.5±2.7) and 22 typically developing controls (TDC) (8.9±2.5) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and arterial spin labeling (ASL) to calculate regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuation (fALFF), and CBF, respectively. Two types of NVC metrics (CBF/ReHo, CBF/fALFF) were compared between SCP and TDC, and the inner association between altered NVC metrics and communication function level in the SCP group was further analyzed.

RESULTS: Compared to TDC, among regional level, SCP showed significantly higher CBF/ReHo coupling in the left fusiform gyrus, right lingual gyrus, bilateral thalamus, left calcarine fissure and surrounding cortex, and left caudate nucleus [P<0.005, Gaussian random field (GRF) correction] and increased CBF/fALFF coupling in the left lingual gyrus, left middle temporal gyrus, right middle occipital gyrus, bilateral caudate nucleus, left angular gyrus, and left median cingulate and paracingulate gyri (P<0.005, GRF correction). Furthermore, increased CBF/fALFF coupling was found in the left middle temporal gyrus (r=-0.560, P=0.010) and left angular gyrus (r=-0.541, P=0.014), and negatively correlated with the communication function level of SCP.

CONCLUSIONS: Children with SCP present altered NVC, associated with communication function level. The study provides a new insight into the pathophysiology of SCP and provides potential imaging biomarkers of communication performances in children with SCP.

PMID:41209187 | PMC:PMC12591913 | DOI:10.21037/qims-2025-19

Netw Neurosci. 2025 Oct 30;9(4):1221-1244. doi: 10.1162/NETN.a.29. eCollection 2025.

ABSTRACT

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Visually modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

PMID:41209086 | PMC:PMC12594490 | DOI:10.1162/NETN.a.29

Netw Neurosci. 2025 Oct 30;9(4):1176-1198. doi: 10.1162/NETN.a.27. eCollection 2025.

ABSTRACT

Anesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anesthetized condition with decreased complexity. However, anesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g., noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity, and spatiotemporal modularization in deep anesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject level, our analysis provides tentative evidence that these low-complexity attractor states appear to depend on subject-specific age and anesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organization in anesthesia can be characterized by mostly unchanging, isolated regional subnetworks that share some similarities with the brain's underlying structural connectivity, as determined from normative tractography data.

PMID:41209085 | PMC:PMC12594487 | DOI:10.1162/NETN.a.27

Netw Neurosci. 2025 Oct 30;9(4):1264-1278. doi: 10.1162/NETN.a.31. eCollection 2025.

ABSTRACT

Reading difficulty (RD; dyslexia) is a developmental condition with neurological origins and persistent academic consequences. Children with RD often show deficits in audiovisual integration (AVI) and executive functions. Visual rhythmic reading training (RRT) has been associated with improvements in these domains, but it remains unclear whether such effects generalize to the resting-state brain activity. English-speaking children aged 8-12 years, including typical readers (TRs) and children with RD, were randomly assigned to an 8-week visual RRT or control math training group. Reading assessments and resting-state functional MRI data were collected before and after the intervention. Functional connectivity (FC) analyses examined AVI and its interaction with frontoparietal-cingulo-opercular (FP-CO) cognitive control networks during rest. Following RRT, children with RD showed significant improvements in reading fluency. The RRT group also demonstrated greater changes in AVI, which were associated with increased FC between FP-CO networks and sensory regions during the resting state. RRT improves reading performance and promotes enhanced integration between sensory and executive networks in children with RD, even in the absence of task demands. These findings support the role of RRT in fostering domain-general neuroplasticity beyond reading-specific contexts.

PMID:41209082 | PMC:PMC12594489 | DOI:10.1162/NETN.a.31