Most recent paper

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

Neuroimage. 2025 Nov 7:121571. doi: 10.1016/j.neuroimage.2025.121571. Online ahead of print.

ABSTRACT

While research on imaging correlates of magnetic resonance guided focused ultrasound (MRgFUS) outcomes accumulates, a comprehensive synthesis across different disease populations remains absent. This systematic review aims to identify, technique-specific imaging biomarkers linked to treatment outcomes across distinct clinical conditions, including essential tremor (53 studies), Parkinson's disease (8 studies), and psychiatric disorders (2 studies). Key findings demonstrate that in ET, larger ventral intermediate nucleus (Vim) lesions intersecting the dentatorubrothalamic tract (DRTT) correlate with greater tremor reduction but increased adverse event risks. Diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) revealed post-treatment white matter remodeling and functional restoration within sensorimotor, cerebellar, and visual networks. For PD, symptom improvement depended on ablation specificity within subthalamic nucleus (STN) or Vim subregions, with pre-treatment connectivity to motor cortices serving as predictive markers. Limited psychiatric studies implicated anatomical targeting and lesion-related functional connectivity in treatment response. The review concludes that lesion characteristics and brain connectivity are pivotal for optimizing MRgFUS strategies and outcomes across diseases.

PMID:41207452 | DOI:10.1016/j.neuroimage.2025.121571

Neurobiol Dis. 2025 Nov 7:107181. doi: 10.1016/j.nbd.2025.107181. Online ahead of print.

ABSTRACT

OBJECTIVE: The olfactory cortex is among the earliest brain regions affected by Alzheimer's disease (AD), with olfactory deficits frequently preceding cognitive decline. This study aimed to characterize the functional and structural degeneration trajectory of the olfactory cortex from normal cognition (NC) to mild cognitive impairment (MCI) and eventually to AD using multimodal neuroimaging techniques.

MATERIALS AND METHODS: A total of 105 participants (28 with NC, 35 with MCI, and 42 with AD) were subjected to olfactory [University of Pennsylvania Smell Identification Test (UPSIT)] and cognitive [e.g., Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)] assessments. This was followed by olfactory task-based functional magnetic resonance imaging (fMRI; olfactory activation), resting-state fMRI [amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo)], and structural MRI [gray matter volume (GMV) and white matter volume (WMV)] in 12 olfactory-related regions of interest. Group comparisons using one-way analysis of variance/Kruskal-Wallis and multivariate logistic regression analyses were performed to identify stage-specific imaging biomarkers and evaluate diagnostic performance.

RESULTS: From the NC to MCI and then to AD groups, a consistent pattern of declining olfactory activation values, GMV, and WMV, coupled with increased ALFF and ReHo in olfactory subregions, was observed. Moreover, corresponding decreases in olfactory and cognitive scores were noted. Our multivariate logistic regression models yielded the following classification performance: NC versus MCI [right primary olfactory cortex (POC) olfactory activation, right insula olfactory activation, left POC GMV, left insula ReHo, right amygdala ALFF, and MMSE scores] achieved 90.5 % accuracy; MCI versus AD (left hippocampal GMV, left insula ReHo, and MMSE scores) reached 94.8 % accuracy; and NC versus AD (left hippocampal GMV and UPSIT scores) achieved 92.9 % accuracy.

CONCLUSIONS: Our findings delineate a spatiotemporal progression of olfactory cortex degeneration, with early POC alterations in MCI evolving into widespread atrophy and functional dysregulation in AD. Multimodal MRI metrics and logistic modeling yield highly accurate stage classification, underscoring their potential as sensitive biomarkers for early AD detection and monitoring.

PMID:41207394 | DOI:10.1016/j.nbd.2025.107181

Drug Alcohol Depend. 2025 Nov 4;277:112952. doi: 10.1016/j.drugalcdep.2025.112952. Online ahead of print.

ABSTRACT

BACKGROUND: Methamphetamine use disorder (MUD) imposes severe neurological and societal challenges, yet the sex-specific alterations in brain network topology remain poorly understood.

METHODS: Resting-state fMRI data were acquired from 78 patients with MUD (49 male, 29 female) and 65 demographically matched healthy controls (HCs). Functional connectivity matrices were constructed using RESTplus V1.30, and graph metrics (global efficiency, nodal centrality) were computed using GRETNA V2.0.0. Group comparisons (MUD vs. HCs; male vs. female) and correlation analyses with Barratt Impulsiveness Scale scores were conducted, applying false discovery rate correction.

RESULTS: Compared to HCs, patients with MUD exhibited disrupted nodal metrics across multiple brain regions, including bilateral anterior-inferior triangular regions, right gyrus rectus, left cuneus, bilateral supplementary motor areas, bilateral parietal regions, and occipital lobes, without significant alterations in global network metrics. Furthermore, significant sex-related main effects were observed in widespread brain areas, involving key nodal metrics such as degree centrality, local efficiency, clustering coefficient, and nodal efficiency. Female patients with MUD demonstrated higher global network measures and showed more extensive nodal metric differences compared to males. These findings highlight distinct sex-dependent network alterations in MUD and emphasize the need for sex-stratified therapeutic approaches targeting specific network vulnerabilities.

CONCLUSION: This study provides evidence of sexually dimorphic network pathology in MUD, revealing that females exhibit widespread front-parietal-occipital disruptions, whereas males demonstrate relative network preservation. These findings underscore the importance of sex-stratified therapeutic strategies targeting network-specific vulnerabilities in MUD.

PMID:41207270 | DOI:10.1016/j.drugalcdep.2025.112952

Asian J Psychiatr. 2025 Nov 6;114:104764. doi: 10.1016/j.ajp.2025.104764. Online ahead of print.

ABSTRACT

BACKGROUND: Alterations in resting-state brain activity in bipolar disorder (BD) and their pharmacological response remain unclear. This study investigated dynamic local brain activity, assessed the discriminative ability of dALFF and dReHo, and explored related transcriptomic correlates.

METHODS: Resting-state fMRI and clinical data were obtained from 77 BD patients (with 38 completing the 3-month follow-up) and 83 healthy controls (HC). Dynamic local metrics (dALFF and dReho) were computed to quantify brain activity variability. Support vector machine (SVM) models were used for group classification and treatment-response prediction. Neuroimaging-transcriptomic associations were evaluated using gene expression data from the Allen Human Brain Atlas.

RESULTS: Baseline, BD patients showed decreased dReho in the right calcarine gyrus and decreased dALFF in the left superior and middle frontal gyri, alongside increased dALFF in the right hippocampus, corrected by Gaussian Random Field theory, with voxel-level significance set at p < 0.001 and cluster-level significance at p < 0.05. However, no significant alterations were observed following pharmacological treatment. The SVM models demonstrated moderate discriminative performance (AUC_dALFF = 0.83; AUC_dReho = 0.71). Cross-sample transcriptomic analyses identified 171 and 240 genes, after Bonferroni correction, that were potentially correlated with dReHo and dALFF variability, respectively, and were enriched in broad synaptic and immune-related pathways.

CONCLUSION: In summary, altered dALFF and dReHo patterns may represent neuroimaging markers of BD, reflecting disrupted dynamic brain activity. Neuroimaging-transcriptomic associations offer molecular insights into BD pathophysiology. However, findings should be interpreted cautiously due to the modest sample size, follow-up attrition, and lack of external validation.

PMID:41207165 | DOI:10.1016/j.ajp.2025.104764

Neuroimage. 2025 Nov 6:121567. doi: 10.1016/j.neuroimage.2025.121567. Online ahead of print.

ABSTRACT

BACKGROUND: GABAergic dysfunction contributes to Major Depressive Disorder (MDD). This study examines excitatory/inhibitory (E/I) imbalance, specifically GABA deficits in the left dorsolateral prefrontal cortex (DLPFC), and their link to anhedonia and cognitive impairment in MDD. It also investigates alterations in the coupling between local E/I activity and functional connectivity (FC) within the triple network.

METHODS: We included 41 medication-naïve MDD patients and 33 healthy controls (HCs). Participants underwent Snaith-Hamilton Pleasure Scale (SHAPS) and cognitive assessments. GABA+/Cr, Glx, and GABA+/Glx ratios were measured in the left DLPFC using proton magnetic resonance spectroscopy (¹H-MRS). Resting-state functional magnetic resonance imaging (fMRI) data were analyzed via independent component analysis (ICA), identifying five major brain networks.

RESULTS: Female MDD patients exhibited reduced GABA+/Cr in the left DLPFC (p = 0.021). GABA+/Cr negatively correlated with SHAPS scores (r = -0.33, p = 0.04) and Trail Making Test Part B (TMT-B) completion times (r = -0.36, p = 0.03) in MDD patients. HCs showed positive correlations between GABA+/Cr and FC within the LECN (r = 0.41, p = 0.02) and between the LECN-dDMN (r = 0.39, p = 0.03) and LECN-pSN (r = 0.46, p = 0.01); these correlations were absent in the MDD group.

CONCLUSIONS: Female patients with MDD exhibit a specific reduction in GABA levels in the left DLPFC. Furthermore, these lower GABA levels are associated with increased anhedonia and poorer executive function. Critically, the neurochemical coupling between GABA and large-scale brain networks is also disrupted in MDD.

PMID:41205935 | DOI:10.1016/j.neuroimage.2025.121567