Most recent paper

Biol Psychiatry. 2025 Nov 28:S0006-3223(25)01645-2. doi: 10.1016/j.biopsych.2025.11.016. Online ahead of print.

ABSTRACT

BACKGROUND: Posttraumatic stress disorder (PTSD) is a heterogeneous condition with diverse symptom presentations and emotional experiences. While fear is traditionally viewed as central, growing evidence highlights the role of non-fear-based emotions - such as sadness, guilt, and shame - collectively termed Emotional Pain. This study aimed to identify Emotional Pain and Fear-based PTSD symptom profiles and their neural correlates across two independent samples.

METHODS: In Study 1 (n=838), trauma-exposed individuals with probable PTSD completed the PTSD Checklist for DSM-5 (PCL-5) and subjective ratings of Fear and Emotional Pain. Item-level network analysis was conducted to identify central symptoms and relationships. In Study (n=162), recent trauma survivors with high PTSD symptoms underwent resting-state and task-based functional MRI (fMRI) scans 1-month post-trauma, and completed follow-up clinical assessment at 14-months post-trauma. Connectome-based predictive modeling (CPM) was used to predict chronic symptom severity for Fear and Emotional Pain-based profiles, identified in Study 1.

RESULTS: Emotional Pain was rated as more impairing than Fear by most participants (69%). Symptom networks showed distinct patterns: Fear was associated with flashbacks, nightmares, distressing memories, exaggerated startle, and external avoidance; Emotional Pain was linked to anhedonia, negative beliefs, negative emotions, sleep disturbance and emotional reactivity. CPM predicted chronic Fear-based symptom severity (rho=0.228, p<0.001), but not Emotional Pain (rho=0.167, p=0.055). Predictive features included connections across anterior default-mode, central executive, salience, motor-sensory and subcortical networks.

CONCLUSIONS: Emotional Pain and Fear may represent distinct PTSD dimensions. Disentangling their neural signatures may improve diagnostic precision and guide personalized, mechanism-based interventions for trauma-related psychopathology.

PMID:41319905 | DOI:10.1016/j.biopsych.2025.11.016

Neuroimage. 2025 Nov 27:121579. doi: 10.1016/j.neuroimage.2025.121579. Online ahead of print.

ABSTRACT

Data collection technology in functional magnetic resonance imaging (fMRI) is rapidly developing, leading to continuous growth of spatio-temporal resolution. The need to understand brain dynamics, as it plays a crucial role in understanding brain function, continues to push innovation in this direction as limits on the frequency of data measurement limit the kinds of questions that may be asked. In parallel, researchers continue to amass large volumes of fMRI data using the highest sampling frequencies available with current technology. A common and plausible assumption is that higher measurement frequencies may lead to more informative data about the brain dynamics and help mitigate physiological noise from neurovascularly coupled signal. This assumption leads to the tendency to discard the older datasets collected with lower temporal resolution in favor of more recent collections. Moreover, as we will show, it leads to under-utilizing the current MRI technology by only collecting at the fastest available rate. A recent theoretical study demonstrated that combining high frequency data with data collected at a deliberately slower sampling rate can, in some conditions, lead to gains in information about the dynamics. We hypothesize that similar effects can be observed in fMRI datasets where data is collected at multiple timescales, as opposed to datasets created by subsampling from a single acquisition rate. A resting state fMRI dataset collected from 10 subjects at a slow (2150 ms) and fast (100 ms) repetition time (TR) is analyzed, demonstrating informative gains in predictive power by combining the two. This gain is in contrast to diminishing returns in the single TR dataset performance, where the data has been manually-undersampled to a slower sampling rate and combined with the original. Performance outcomes were also compared in gender prediction across a multi-rate dataset and single rate dataset, with multi-rate results showing gains in composite features. Our experiments demonstrate agreement with the theoretical results in showing that features formed as a combination of slow and fast sampling rates yield greater predictive power than features from either slow or fast rates alone in some settings.

PMID:41318042 | DOI:10.1016/j.neuroimage.2025.121579

Drug Alcohol Depend. 2025 Nov 22;278:112972. doi: 10.1016/j.drugalcdep.2025.112972. Online ahead of print.

ABSTRACT

Alcohol intake disrupts cognitive and sensory processing. However, its effects on the role of individual structures within cortical networks, or on the larger network structure, remain unclear. This acute alcohol administration study addressed this gap using graph theory analysis. Healthy individuals (n = 107, 21-45yrs, 61 women) consumed alcohol (0.08g/dL target BrAC) or a placebo drink in 2 double-blinded sessions and self-reported their perceived intoxication using a visual analog scale. Resting state fMRI was acquired with a Siemens Prisma 3T scanner 30min after consumption. The effect of alcohol on graph theory outcomes in a network of 106 cerebral ROIs was identified using the CONN toolbox. We also determined the association between graph theory metrics and subjective intoxication. Results revealed alcohol 1) significantly decreased global efficiency in several occipital nodes and increased global efficiency for nodes within the frontal and temporal cortex; 2) increased local efficiency at a network level as well as in specific nodes in the temporal and frontal cortices; 3) increased degree in frontal and temporal regions; 4) decreased closeness centrality and increased mean path length in parietal and occipital regions as well at the network level compared with placebo conditions. Additionally, decreases in global efficiency and increases in local efficiency and clustering coefficient in the alcohol vs. placebo condition significantly predicted subjective intoxication. Taken together, results provide new evidence that alcohol intake produces changes in the overall topography of the cerebral network that at least partially underlie individual differences in subjective alcohol response.

PMID:41317510 | DOI:10.1016/j.drugalcdep.2025.112972

J Headache Pain. 2025 Nov 27;26(1):272. doi: 10.1186/s10194-025-02210-0.

ABSTRACT

BACKGROUND: New daily persistent headache (NDPH) is a rare, refractory primary headache with an unclear pathophysiological mechanism. Previous neuroimaging studies on NDPH have been largely limited to 3T MRI, which fails to thoroughly reveal microstructural changes, particularly subregional abnormalities and iron metabolism alterations. Based on this, the present study employs 7T multimodal imaging techniques to investigate structural, functional, and iron metabolism abnormalities in the whole brain and, in particular, the changes in subregions of the limbic system.

METHODS: A total of 23 individuals with NDPH and 23 healthy controls (HCs) underwent 7T MRI, including T1-weighted three-dimensional magnetization-prepared 2 rapid acquisition gradient echo (3D-T1WI-MP2RAGE) and resting-state fMRI (rs-fMRI); among these patients, 19 also underwent quantitative susceptibility mapping (QSM). Structural volumes, functional metrics (fractional amplitude of low-frequency fluctuations [fALFF], regional homogeneity [ReHo]), and iron deposition (assessed via QSM) were analyzed, and their correlations with clinical parameters (e.g., headache history, anxiety/depression scores) were examined.

RESULTS: Compared to HCs, individuals with NDPH exhibited significantly less volume in the right accumbens area and left caudal anterior cingulate cortex after false discovery rate (FDR) correction. Widespread changed fALFF and ReHo values were observed, with correlations to clinical features. QSM values were decreased in right paracentral, right cuneus and left precentral with increasing in left rostral middle frontal.

CONCLUSION: 7T multimodal MRI identifies widespread structural, functional, and iron metabolism abnormalities in NDPH, particularly in limbic subregions, highlighting a “pain-emotion” interaction mechanism. These findings provide preliminary insights into NDPH pathogenesis.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-025-02210-0.

PMID:41310432 | PMC:PMC12659571 | DOI:10.1186/s10194-025-02210-0

Hum Brain Mapp. 2025 Dec 1;46(17):e70424. doi: 10.1002/hbm.70424.

ABSTRACT

Transcranial magnetic stimulation (TMS) guided by magnetic resonance imaging (MRI) has significantly advanced the treatment of mood disorders by enabling precise targeting of brain circuits implicated in their pathophysiology. The integration of neuronavigation systems, which utilize real-time MRI-based coil positioning, has improved spatial targeting accuracy, individualization, and therapeutic outcomes. Despite these advancements, achieving optimal stimulation efficacy requires careful consideration of MRI techniques, including anatomical imaging, functional MRI (fMRI), and connectivity-based methods. Anatomical MRI provides a reliable structural foundation for neuronavigation but lacks specificity regarding functional neural networks implicated in mood disorders. In contrast, fMRI, through task-based and resting-state paradigms, enhances target selection precision by identifying patient-specific neural activity and functional connectivity patterns, although this approach is vulnerable to variability and imaging artifacts. Connectivity-based MRI neuronavigation represents a promising advancement by explicitly targeting disrupted neural networks. This review critically examines recent technological and methodological progress in MRI-guided neuronavigation for TMS, addressing current challenges such as image acquisition quality, co-registration accuracy, artifact mitigation, and practical constraints in clinical settings. Finally, it discusses emerging opportunities and innovations poised to enhance neuronavigation precision, foster wider clinical adoption, and ultimately improve therapeutic outcomes in interventional psychiatry for mood disorders.

PMID:41310980 | DOI:10.1002/hbm.70424

Psychol Med. 2025 Nov 28;55:e363. doi: 10.1017/S0033291725102638.

ABSTRACT

BACKGROUND: Psychomotor disturbance has long been observed in major depressive disorder (MDD) and is thought to be a key indicator of illness course. However, dominant methods of measuring psychomotor disturbance, via self-report and clinician ratings, often lack objectivity and may be less sensitive to subtle psychomotor disturbances. Furthermore, the neural mechanisms of psychomotor disturbance in MDD remain unclear.

METHODS: To address these gaps, we measured psychomotor agitation via a force variability paradigm and collected resting fMRI in 47 individuals with current MDD (cMDD) and 93 individuals with remitted MDD (rMDD). We then characterized whether resting-state cortico-cortical and cortico-subcortical connectivity related to force variability and depressive symptoms.

RESULTS: Behaviorally, individuals with cMDD exhibited greater force variability than rMDD individuals (t(138) = 3.01, p = 0.003, Cohen's d = 0.25). Furthermore, greater force variability was associated with less visuomotor connectivity (r(130) = -0.23, p = 0.009, 95% CI [-0.38, -0.06]). Visuomotor connectivity was significantly reduced in cMDD relative to rMDD (t(130) = -2.77, p = 0.006, Cohen's d = -0.24) and mediated the group difference in force variability (ACME β = -0.06, 95% CI [-0.16, -0.01], p = 0.04).

CONCLUSIONS: Our findings represent a crucial step toward clarifying the pathophysiology of psychomotor agitation in MDD. Specifically, altered visuomotor functional connectivity emerged as a candidate neural mechanism, highlighting a promising direction for future research on dysfunctional visually guided movements in MDD.

PMID:41310957 | DOI:10.1017/S0033291725102638

Chin Med. 2025 Nov 28;20(1):207. doi: 10.1186/s13020-025-01236-5.

ABSTRACT

BACKGROUND: Chronic insomnia disorder (CID) is characterized by dysregulation in brain function and is closely associated with neuroinflammation. Although acupuncture has been shown to improve insomnia symptoms, its underlying mechanisms, particularly at both the macro brain connectivity and corresponding molecular levels, remain unclear METHODS: Forty-eight CID patients were randomly assigned to either an acupuncture group or a waitlist group. Clinical data and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before and after the intervention. Changes in brain connectivity were analyzed using fMRI to assess global brain connectivity (GBC) in each group. Gene expression data from the Allen Human Brain Atlas were utilized to identify important genes contributing to these acupuncture-induced GBC changes. Gene set enrichment analysis was performed to annotate the molecular biological processes involved.

RESULTS: In the acupuncture group, fMRI analysis revealed decreased regional GBC in key regions, such as the pallidum and prefrontal cortex, correlating with symptom relief. In contrast, the waitlist group showed increased regional GBC without symptom relief. Gene set enrichment analysis revealed that specific genes associated with astrocytes and neuroinflammation-related biological processes were linked to the acupuncture-induced changes in GBC. The neuroinflammation-informed GBC-transcriptomic signatures induced by acupuncture were further validated by their significant correlation with reductions in IL-6 levels as insomnia symptoms improved.

CONCLUSION: Acupuncture may remodel brain functional connectivity by regulating neuroinflammation-related pathways, thereby improving insomnia symptoms.

PMID:41310834 | DOI:10.1186/s13020-025-01236-5

Commun Med (Lond). 2025 Nov 27;5(1):500. doi: 10.1038/s43856-025-01212-y.

ABSTRACT

BACKGROUND: This study investigated potential behavioral and neural biomarkers of adolescent impulsivity by analyzing adolescent responses in a memory test and examining task-independent brain connectivity.

METHODS: This research utilized immediate and delayed memory tasks, together with a similar distractor memory task (SMT), to examine adolescent impulsivity and its correlation with neural cognitive control strategies. Ninety-five healthy, right-handed teenagers (27 females, average age 14.9 years) participated in the functional magnetic resonance imaging (fMRI) sessions.

RESULTS: Elevated impulsivity correlates with an increased number of errors during target trials and a higher incidence of false alarms during catch trials. Neural activity and connectivity involving the insula and dorsal anterior cingulate cortex (dACC) are significantly associated with behavioral responses and individual impulsivity. Notably, both task-modulated and resting-state (intrinsic) coupling between the insula and locus coeruleus (LC), as well as between the dACC and LC, demonstrate significant positive correlation with impulsivity. These findings indicate that insula-LC and dACC-LC connectivity strength serve as reliable indicators of impulsivity.

CONCLUSIONS: The results indicate that the connection between the salience network and the noradrenergic locus coeruleus may function as a consistent neural indicator of impulsivity. Our findings indicate that this method can discern reliable brain biomarkers for impulsivity and can guide interventions aimed at enhancing self-control during adolescence.

PMID:41310178 | DOI:10.1038/s43856-025-01212-y

Sci Rep. 2025 Nov 27;15(1):42489. doi: 10.1038/s41598-025-26490-3.

ABSTRACT

Functional anorectal pain (FAP) is a chronic condition with unclear pathophysiological mechanisms that is often linked to psychological distress. This resting-state functional magnetic resonance imaging (rs-fMRI) study investigated aberrant interhemispheric connections in 30 FAP patients versus 21 matched healthy controls (HC) via seed-based functional connectivity (FC) and voxel-mirrored homotopic connectivity (VMHC). Compared with HC, FAP patients presented enhanced FC between the left middle frontal gyrus (MFG.L) and regions such as the right MFG (MFG.R) and left superior temporal gyrus (STG.L). VMHC analysis revealed increased patterns in the MFG.L and left superior medial frontal gyrus (SFGmed.L) in FAP patients. Moreover, the strength of FC between the MFG.L and MFG.R was negatively correlated with age, indicating that this heightened connection may diminish with age. These findings indicate that FAP involves aberrant interhemispheric hyperconnectivity, which may play crucial roles in pain perception and emotional processing. The age-dependent decline in FC highlights the eroding of neuroplasticity in aging patients. These neural alterations could serve as diagnostic biomarkers and provide targets for therapeutic interventions. Our work positions FAP within a brain-gut axis dysregulation framework and suggests circuit-specific therapeutics to restore neural homeostasis.

PMID:41309853 | DOI:10.1038/s41598-025-26490-3

NPJ Parkinsons Dis. 2025 Nov 27. doi: 10.1038/s41531-025-01219-1. Online ahead of print.

ABSTRACT

Parkinson's Disease (PD) with Rapid Eye Movement Sleep Behavior Disorder (RBD) occurred before (body-first) and after (brain-first) motor symptoms may exhibit different MRI features. We aimed to investigate the structural and functional MRI pattern differences between brain-first and body-first subtypes of PD. 23 body-first and 19 brain-first PD patients, along with 20 matched healthy controls (HC) were enrolled and underwent T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI) scans in NJ-dataset. We calculated and compared amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV) across groups to identify differential brain regions, which were subsequently extracted in PPMI and OASIS3 datasets. These values were combined with clinical data for binary classification machine learning training (with PPMI including 22 body-first and 35 brain-first patients used for feature selection and OASIS3 including 5 body-first and 10 brain-first patients used for external validation) and correlated with clinical scales. The body-first group exhibited higher zALFF values in the parietal lobe and greater GMV in the frontal lobe, while showing lower zALFF and GMV values in the cerebellum and subcortical nuclei (caudate nucleus for zALFF; medulla, hippocampus, amygdala, and olfactory bulb for GMV) than brain-first group. We identified significant axial asymmetry in functional and structural MRI between brain-first and body-first Parkinson's subtypes, characterized by greater gray matter retention and higher spontaneous neural activity in neocortex in the body-first subtype. Furthermore, integrating MRI and clinical scales effectively distinguished between brain-first and body-first subtypes.

PMID:41309711 | DOI:10.1038/s41531-025-01219-1

Eur Neuropsychopharmacol. 2025 Nov 26;102:28-38. doi: 10.1016/j.euroneuro.2025.11.002. Online ahead of print.

ABSTRACT

Structural and functional default mode network (DMN) alterations are common in neuropsychiatric disorders and may contribute transdiagnostically to social dysfunction. Normative modelling enables assessment of DMN alterations at the individual level. This study investigates whether individual deviations in cortical thickness, surface area, and between-network functional connectivity of the DMN differ between schizophrenia (SZ), major depressive disorder (MDD), Alzheimer's disease (AD), and healthy controls (HC), and whether these deviations transdiagnostically relate to social dysfunction. Social dysfunction was assessed using a composite score from the Social Functioning Scale and De Jong-Gierveld Loneliness scale. Structural MRI data was collected for 329 participants (SZ=86, MDD=44, AD=82, HC=117) and resting-state fMRI data for 317 participants. Individual deviation scores of DMN integrity were computed by adapting existing normative models of cortical thickness (N = 58,836), surface area (N = 43,524), and between-network functional connectivity (N = 21,515). Extreme deviations were quantified using a z-threshold of ±1.96. DMN deviation scores were not transdiagnostically associated with social dysfunction across the sample (ps>0.05). AD patients had more extreme negative deviations in DMN cortical thickness than all other groups (ps<0.0001; z = -4.14 to -6.34) and fewer extreme positive deviations in DMN surface area relative to SZ and HC (ps<0.05; z = 2.10 to 2.71). For between-network functional connectivity of the DMN, AD and SZ patients had more extreme negative deviations than MDD and HC (ps<0.05; z = -2.09 to -3.54). To conclude, normative modelling reveals differences in individual deviations of DMN integrity between neuropsychiatric groups, but these deviations do not transdiagnostically relate to social dysfunction.

PMID:41308510 | DOI:10.1016/j.euroneuro.2025.11.002

Neural Netw. 2025 Nov 21;196:108359. doi: 10.1016/j.neunet.2025.108359. Online ahead of print.

ABSTRACT

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects multiple brain systems. Altered brain function can be observed through resting-state functional magnetic resonance imaging (rs-fMRI). While machine learning offers significant advantages in capturing complex signal patterns across numerous voxels, its decision-making process often lacks transparency. This study aimed to develop an explainable machine learning pipeline to classify patients with ALS and healthy control (HC) using rs-fMRI data.

METHODS: Thirty patients with ALS and 30 HCs were enrolled. The pipeline consisted of three key components: (1) preprocessing of rs-fMRI data using independent component analysis, followed by dual regression to reduce dimensionality and generate individual network maps; (2) training of a three-dimensional convolutional neural network (3D-CNN) to classify each individual image as either ALS or HC; and (3) application of saliency map and Grad-CAM++ to visualize the reasoning behind the model's classification.

RESULTS: The 3D-CNN achieved high classification accuracy using the sensorimotor network (SMN) map (78.3%) and the visual network (VN) map (83.3%). Simultaneously, saliency map and Grad-CAM++ highlighted brain regions that contributed to the classification, and some of which were consistent with regions showing intergroup differences in the dual regression analysis.

DISCUSSION: This study developed a novel explainable machine learning model capable of extracting features and classifying rs-fMRI data. Our results showed altered functional integrity in the SMN and VN in ALS. Our pipeline holds the potential to extract features of rs-fMRI data, enabling classification of neurological diseases with explainability.

PMID:41308261 | DOI:10.1016/j.neunet.2025.108359

Neuroinformatics. 2025 Nov 27;23(4):57. doi: 10.1007/s12021-025-09756-z.

ABSTRACT

Modelling real-world networks allows investigating the structure and the dynamics of such networks, which led to significant developments in various scientific fields. One of the most used models in these investigations is the Watts-Strogatz, with a structure composed of high clustering and short path lengths known as small-world networks. This model proposes an interesting gradient between regular and random networks, but its generating process, which relies on a single rewiring probability parameter, is hard to access and to manipulate. In order to study the mechanics of the Watts-Strogatz model, the present work proposes a new method based on deep neural networks that could estimate its probability p. To illustrate its applicability, neuroimaging and phenotypic resting-state fMRI data were used from patients with ADHD and typical development children, obtained from the ADHD-200 database. The neural network efficiently estimated the probability parameter, resulting in small-world graphs for functional brain connectivity with a mean ± s.e.m. p distribution of 0.804 ± 0.003. Despite no difference was found considering the gender or diagnosis of participants, the generalized linear model revealed age as a significant predictor of p (mean ± s.e.m.: 4.410 ± 0.877; p < 0.001), indicating a great effect of neurodevelopment on the brain network's structure. The proposed approach is promising in estimating the probability of the Watts-Strogatz model, and its application has the potential to improve investigations of network connectivity with a relatively efficient and simple framework.

PMID:41307783 | DOI:10.1007/s12021-025-09756-z

J Neurol. 2025 Nov 27;272(12):787. doi: 10.1007/s00415-025-13515-0.

ABSTRACT

BACKGROUND: Resting-state functional connectivity (FC) alterations in people with multiple sclerosis (PwMS) have been hypothesized to reflect either adaptive or maladaptive plasticity. Investigating FC longitudinal evolution and its relationship with disability progression can help clarify this issue. This study examined 5-year FC changes in pwMS and their clinical relevance.

METHODS: From the Italian Neuroimaging Network Initiative database, we included 156 pwMS with two clinical visits and 3T-MRI scans acquired on the same scanner 4-6 years apart. Clinical/neuropsychological visits included the Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test (9HPT), Timed 25-Foot Walk Test (T25FWT), Paced Auditory Serial Addition Test (PASAT3), and Symbol Digit Modalities Test (SDMT). One hundred fifty-six age- and sex-matched healthy subjects (HS) with baseline MRI and the same tests were also included. Based on the EDSS, pwMS were divided into three groups: low disability (0-1.5; N = 78), mild disability (2-3.5; N = 50), and high disability (≥ 4; N = 28). Resting-state networks (RSNs) were identified using independent component analysis. Baseline and longitudinal FC changes were correlated with baseline and follow-up clinical/neuropsychological measures.

RESULTS: At baseline, the low-disability group showed significantly higher FC in all RSNs (FDR-corrected p < 0.05) compared to HS, which correlated with better baseline scores (SDMT, T25FWT) and less worsening at follow-up (PASAT3, 9HPT). The mild- and high-disability groups exhibited mixed FC abnormalities, with both higher and lower FC than HS in several RSNs. In the mild-disability group, higher FC was associated with worse baseline scores (SDMT, T25FWT) and greater clinical worsening (PASAT3, 9HPT, T25FWT). In the high-disability group, higher sensorimotor baseline FC correlated only with worse baseline 9HPT. Longitudinally, all RSNs showed FC increase in the low-disability group, but a FC decrease in the other groups. FC increases in the low-disability group generally correlated with better clinical outcome (T25FWT), while FC decreases in the mild-disability group correlated with clinical worsening (9HPT, T25FWT).

CONCLUSIONS: FC increases appear to reflect compensatory mechanisms in low-disability pwMS, while in more disabled patients, FC alterations likely represent maladaptive responses. These findings support resting-state FC as a biomarker for monitoring disease progression and treatment response in MS.

PMID:41307737 | DOI:10.1007/s00415-025-13515-0

Neuroinformatics. 2025 Nov 27;23(4):55. doi: 10.1007/s12021-025-09757-y.

ABSTRACT

The fasciola cinerea (FC) is a slender archicortical band at the posterior hippocampal tail, and its human morphology and network role are poorly defined. To generate a reproducible in vivo three-dimensional (3D) model of the FC, quantify its geometry, characterize structural and functional connectivity within posterior-medial memory networks, and test a tractography-constrained computational model in which the FC acts as a multiplicative gate. Open 7 T datasets, structural, diffusion, and resting-state functional magnetic resonance imaging (fMRI) were anchored to BigBrain and Julich-Brain priors. A semi-automated, atlas-guided pipeline was used to segment the FC and derive morphometrics (volume, thickness, width, curvature, and Laplace-Beltrami spectral shape). Reliability was assessed using the Dice, 95% Hausdorff distance, and test-retest intraclass correlation coefficient (ICC). Diffusion tractography was used to estimate the FC structural pathways toward retrosplenial (RSC), parahippocampal (PHC), posterior cingulate (PCC), and thalamic targets. Resting-state coupling was summarized using Fisher-z correlations and narrowband coherence. A Wilson-Cowan neural mass model, constrained by tractography, simulated FC-dependent FC-RSC coherence with morphometric scaling of gating gain. Segmentation was reliable (Dice = 0.78 ± 0.05; 95% Hausdorff = 1.62 ± 0.41 mm; ICC_volume = 0.88; ICC_thickness = 0.82). Group morphometrics: volume 84.3 ± 17.9 mm³, mean thickness 0.92 ± 0.15 mm, width 1.86 ± 0.31 mm, centerline length 14.2 ± 2.1 mm. FC showed preferential connectivity: FC→RSC 0.21 ± 0.09; FC→PHC 0.18 ± 0.08; FC→PCC 0.11 ± 0.06; FC→Thalamus 0.06 ± 0.04. Resting-state coupling was strongest for FC-RSC (z = 0.24 ± 0.12) with a slow-band coherence enhancement. Thickness predicted the FC→RSC strength (β = 0.17 per 0.1 mm) and FC-RSC z (β = 0.08 per 0.1 mm), and higher curvature was negatively related. The gating model reproduced empirical FC-RSC coherence (r = 0.52 ± 0.11), and morphometric scaling improved the fit (Δr = + 0.06). We provide an anatomically grounded and mathematically validated 3D FC model that links microstructures to mesoscale connectivity. Preferential posterior-medial coupling and morphometry-dependent gating support the FC as a modulatory interface in human memory networks and yield testable markers for individualized mapping and clinical translation.

PMID:41307597 | DOI:10.1007/s12021-025-09757-y

Pain. 2025 Nov 13. doi: 10.1097/j.pain.0000000000003841. Online ahead of print.

ABSTRACT

Impairments in lumbar sensory perception, including reduced tactile acuity, occur in patients with nonspecific low back pain (LBP). Tactile acuity is linked to primary somatosensory cortex (S1) activity and structure, but neural markers of lumbar-specific tactile acuity tests remain unvalidated. This cross-sectional study investigated associations between lumbar two-point discrimination (TPD) and estimation (TPE) with functional and structural properties of S1, as well as S1-thalamic connectivity. Resting-state functional MRI and diffusion-weighted MRI assessed S1-thalamic functional connectivity (FC) and structural connectivity, as well as regional homogeneity (ReHo) and mean diffusivity (MD) of S1 grey matter in 78 LBP patients and 39 pain-free controls. Participants with LBP were subdivided into 2 groups: 1 with pain (LBP+, n = 39) and 1 without pain (LBP-, n = 39) on the day of assessment. Higher TPD (ie, worse tactile acuity) was associated with higher contralateral S1-thalamic FC (β = 19.97 mm, 95% CI = 8.47-31.46 mm) and lower contralateral S1-MD (β = -76.98 mm, 95% CI = -142.83 to -11.13 mm). Higher TPE was associated with higher S1-ReHo (β = 19.67 mm, 95% CI = 0.35-39 mm). Two-point discrimination and two-point estimation were positively correlated (r = 0.25, P < 0.001). No between-group differences were found for the MRI variables or TPE, but the LBP+ group showed higher TPD thresholds than pain-free controls (MDiff. = 6.05 mm, Padj. = 0.023). Our findings question the validity of TPE as a measure of tactile acuity. Both neural markers of TPD may not explain tactile acuity impairments in LBP but instead reflect a baseline indicator of tactile performance capability, suggesting poor validity as an LBP-specific marker of neuroplasticity.

PMID:41307249 | DOI:10.1097/j.pain.0000000000003841

Eur J Neurosci. 2025 Nov;62(10):e70338. doi: 10.1111/ejn.70338.

ABSTRACT

Despite a renewed scientific interest in lysergic acid diethylamide (LSD), its local neural effects remain underexplored. This functional magnetic resonance imaging (fMRI) study explored and compared LSD-induced changes in local activity (amplitude of low-frequency fluctuations: ALFF) and local connectivity (regional homogeneity: ReHo), assessing their relationship to regional receptor density. Imaging data of 15 healthy adults from an open dataset were analyzed. For each participant, two pairs of resting-state runs were available (rest1 and rest2), one performed under placebo and one following the intravenous administration of 75-μg LSD. Voxel-wise paired t-tests compared ALFF and ReHo in the LSD versus placebo conditions. Rest1*rest2 test-retest reliability and ALFF*ReHo cross-modal associations were assessed with conjunction maps and vertex-wise correlations. Finally, neurochemical enrichment analyses related LSD-induced ALFF and ReHo changes to cortical density maps of LSD-related neurotransmitter receptors and transporters. Both ALFF and ReHo decreased in somatosensory/visual cortices under LSD compared to placebo. Specific decreases were observed for ALFF in associative regions belonging to the default mode and frontoparietal networks, and for ReHo in subcortical regions (cluster-based corrected p < 0.05). Test-retest reliability was high for ALFF (rho = 0.80, p = 0.001) and moderate for ReHo (rho = 0.46, p = 0.001). ALFF*ReHo LSD-induced changes were moderately associated (rest1: rho = 0.36, p = 0.001; rest2: rho = 0.56, p = 0.001). Neurochemical enrichment analysis showed that LSD-induced ALFF/ReHo alterations were reliably and negatively correlated with the density of D2 and 5-HT1A receptors (FDR-corrected p < 0.05). These preliminary findings suggest that LSD may engage complex and dynamic neurochemical processes beyond its known 5-HT2A receptor target, warranting further investigation.

PMID:41305961 | DOI:10.1111/ejn.70338

Brain Sci. 2025 Nov 20;15(11):1245. doi: 10.3390/brainsci15111245.

ABSTRACT

Background/Objectives: Deep brain stimulation (DBS) of the globus pallidus interna (GPi) is a safe and established therapy for management of refractory motor fluctuations and dyskinesia in Parkinson's disease (PD). However, the relationship between stimulation site connectivity and improvement of axial gait symptoms remains poorly understood, particularly when stimulating in the GPi. This study investigated functional and structural connectivity patterns specifically associated with axial symptom outcomes following bilateral GPi-DBS, and, as a secondary exploratory analysis, examined whether Volumes of tissue activated (VTAs)-based connectivity related to overall UPDRS-III change. Methods: We retrospectively analyzed 19 PD patients who underwent bilateral GPi-DBS at the University of Florida (2002-2017). Unified Parkinson's Disease Rating Scale (UPDRS-III) axial gait subscores were assessed at baseline and 36-month follow-up. VTAs were reconstructed using Lead-DBS and coregistered to Montreal Neurological Institute (MNI) space. Structural connectivity was evaluated with diffusion tractography, and functional connectivity was estimated using normative resting-state fMRI datasets. Correlations between VTA connectivity and clinical improvement were examined using Spearman correlation and voxelwise analyses. Results: Patients with axial improvement in motor scales demonstrated specific VTA connectivity to sensorimotor and supplementary motor networks, particularly lobule V and lobules I-IV of the cerebellum. These associations were specific to axial gait subscores. In contrast, worsening axial gait symptoms correlated with connectivity to cerebellar Crus II, cerebellum VIII, calcarine cortex, and thalamus (p < 0.05). Total UPDRS-III scores did not show a significant positive correlation with supplementary motor area or primary motor cortex connectivity; a non-significant trend was observed for VTA-M1 connectivity (R = 0.41, p = 0.078). Worsening total motor scores were associated with cerebellar Crus II and frontal-parietal networks. These findings suggest that distinct connectivity patterns underlie differential trajectories in axial and global motor outcomes following GPi-DBS. Conclusions: Distinct connectivity profiles might underlie axial gait symptom outcomes following GPi-DBS. Connectivity to motor and sensorimotor pathways supports improvement, whereas involvement of Crus II and occipital networks predicts worsening. Additional studies to confirm and expand on these findings are needed, but our results highlight the value of connectomic mapping for refining patient-specific targeting and developing future programming strategies.

PMID:41300251 | DOI:10.3390/brainsci15111245

Brain Sci. 2025 Oct 24;15(11):1139. doi: 10.3390/brainsci15111139.

ABSTRACT

Background/Objectives: Accumulating evidence suggests that cognitive training can induce functional reorganization of intrinsic connectivity networks involved in higher-order cognitive processes. However, few interventions have specifically targeted language, an essential domain tightly interwoven with memory, attention, and executive functions. Given their foundational role in communication, reasoning, and knowledge acquisition, enhancing language-related abilities may yield widespread cognitive benefits. This study investigated the neural impact of a new structured, language-based cognitive training program on neurotypical older adults. Methods: Twenty Brazilian Portuguese-speaking women (aged 63-77 years; schooling 9-20 years; low-to-medium socioeconomic status) participated in linguistic activities designed to engage language and general cognitive processing. Behavioral testing and resting-state functional Magnetic Resonance Imaging (fMRI) were conducted before and after the intervention. Results: Functional connectivity analyses revealed significant post-intervention increases in connectivity within the frontoparietal network, critical for language processing, and the ventral attentional network, associated with attentional control. Conclusions: The observed neural enhancements indicate substantial plasticity in cognitive networks among older adults, highlighting the effectiveness of linguistic interventions in modulating critical cognitive functions. These findings provide a foundation for future research on targeted cognitive interventions to promote healthy aging and sustain cognitive vitality.

PMID:41300147 | DOI:10.3390/brainsci15111139

Brain Sci. 2025 Oct 23;15(11):1136. doi: 10.3390/brainsci15111136.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition for which accurate and automated diagnosis is crucial to enable timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) serves as one of the key modalities for diagnosing ASD and elucidating its underlying mechanisms. Numerous existing studies using rs-fMRI data have achieved accurate diagnostic performance. However, these methods often rely on a single brain atlas for constructing brain networks and overlook the data heterogeneity caused by variations in imaging devices, acquisition parameters, and processing pipelines across multiple centers.

METHODS: To address these limitations, this paper proposes a multi-view, low-rank subspace graph structure learning method to integrate multi-atlas and multi-center data for automated ASD diagnosis, termed M3ASD. The proposed framework first constructs functional connectivity matrices from multi-center neuroimaging data using multiple brain atlases. Edge weight filtering is then applied to build multiple brain networks with diverse topological properties, forming several complementary views. Samples from different classes are separately projected into low-rank subspaces within each view to mitigate data heterogeneity. Multi-view consistency regularization is further incorporated to extract more consistent and discriminative features from the low-rank subspaces across views.

RESULTS: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 83.21%, outperforming most existing methods and confirming its effectiveness.

CONCLUSIONS: The proposed method was validated using the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results demonstrate that the M3ASD method not only improves ASD diagnostic accuracy but also identifies common functional brain connections across atlases, thereby enhancing the interpretability of the method.

PMID:41300144 | DOI:10.3390/brainsci15111136