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Phys Life Rev. 2025 Aug 25;55:58-60. doi: 10.1016/j.plrev.2025.08.011. Online ahead of print.

NO ABSTRACT

PMID:40884981 | DOI:10.1016/j.plrev.2025.08.011

Brain Res Bull. 2025 Aug 28:111524. doi: 10.1016/j.brainresbull.2025.111524. Online ahead of print.

ABSTRACT

BACKGROUND: Primary open-angle glaucoma (POAG) is one of the predominant causes of irreversible blindness. Though the glaucomatous transneuronal degeneration pass through the lateral geniculate nucleus (LGN) in the visual pathway, the functional changes associated with the LGN remains elusive. The current study aimed to investigate the seed-based whole-brain functional connectivity (FC) of the LGN and its correlation with retinal thickness in patients with POAG.

METHODS: T1-weighted scans and resting-state functional magnetic resonance imaging (rs-fMRI) were extracted from 54 patients with POAG and 54 matched healthy controls from the UK Biobank. An automatic LGN segmentation protocol and FC analysis were conducted on the bilateral LGN. The Pearson correlation between retinal thickness and FC was explored.

RESULTS: The total LGN volume in patients with POAG was significantly decreased compared with controls (P = 0.042). The patients with POAG showed a pattern of reduced ALFF, fALFF, ReHo, and degree centrality value in brain regions. The left LGN demonstrated an increased FC between the right lingual gyrus and diminished FC with the left middle frontal gyrus (MFG) and left superior parietal lobule, whereas the left middle occipital gyrus exhibited reduced FC with the right LGN in patients with POAG. A positive correlation between the FC in the left MFG and the retinal average thickness (r = 0.292, P = 0.012), retinal nerve fiber layer average thickness (r = 0.272, P = 0.013), and ganglion cell-inner plexiform layer average thickness (r = 0.380, P = 0.001) was found.

CONCLUSIONS: Patients with POAG exhibited LGN atrophy, reduced resting-state functional activity, and altered FC with the LGN in the regional cortex. The glaucomatous impairment of retinal thickness was associated with LGN volume and its connectivity strength with the left MFG. These findings offer a deeper insight into the LGN cortical connectivity alterations and its association with transneuronal degeneration in patients with POAG.

PMID:40885450 | DOI:10.1016/j.brainresbull.2025.111524

Apert Neuro. 2024;4:10.52294/001c.91992. doi: 10.52294/001c.91992. Epub 2024 Jan 10.

ABSTRACT

Psychosis related disorders are severe and difficult to define with brain-based biomarkers due, in part, to heterogeneous psychosis symptoms and individual differences in the brain. Recent innovations in computational neuroscience may address these difficulties. Hyperalignment aligns voxel-wise patterns of neural activity across individuals to improve signal in brain data. Transformation metrics may also serve as biomarkers that reflect clinically relevant differences in pattern connectivity (scale), baseline connectivity (translation), and network topography (rotation). In the present study, we apply hyperalignment to resting state functional connectivity between the frontal cortex and regions throughout the brain in a sample of individuals diagnosed with psychosis and healthy controls. We used binary class support vector machines (SVM) to classify psychosis using unaligned (accuracy=66.50%, p=0.0009) and hyperaligned data (accuracy=65.85%, p=0.0011). Follow-up analyses then used voxelwise rotation estimates to characterize those who were accurately versus inaccurately classified. This revealed two distinct biological subgroups of psychosis characterized by distinct topography of frontal connectivity. Additional analyses relate psychosis to composites of hyperalignment transformations. We report reduced pattern connectivity (t=-2.69, p=0.008) and heightened baseline connectivity (t=2.90, p=0.004) in the psychosis group. These findings may highlight imbalanced frontal connectivity, as those in the psychosis group appear to show general patterns of heightened frontal connectivity while connectivity in more specific regions appear blunted. Results highlight differences in frontal cortex connectivity related to psychosis. Novel methods in the present work may provide a path for future work to apply hyperalignment to brain data from clinical populations to accurately characterize clinical subpopulations within diagnostic categories.

PMID:40880983 | PMC:PMC12382791 | DOI:10.52294/001c.91992

J Am Acad Child Adolesc Psychiatry. 2025 Aug 26:S0890-8567(25)01456-X. doi: 10.1016/j.jaac.2025.08.014. Online ahead of print.

ABSTRACT

OBJECTIVE: Youth whose parents have depression histories are at elevated risk for psychopathology. Familial depression-related patterns of neurodevelopment and environmental stress (e.g., family conflict) likely contribute to heightened risk. However, knowledge remains limited due to few studies, small sample sizes, and cross-sectional designs. We sought to identify how neural circuitry, familial risk for depression, and family conflict interact during preadolescence to predict adolescent psychopathology.

METHOD: Participants included healthy (no lifetime psychiatric diagnoses) youth at high (HR, n=794; at least one parent with a depression history) and low (LR, n=1,708; no parental history of psychopathology) familial risk for depression, aged 9-10, from the Adolescent Brain Cognitive Development (ABCD) Study. We tested whether functional connectivity (FC) among 12 resting-state networks interacted with risk status and family conflict at ages 9-10 to predict psychiatric symptoms at ages 12-13.

RESULTS: Risk status significantly interacted with family conflict and cingulo-parietal network (CPN) FC at ages 9-10 to predict total problems and internalizing symptoms at ages 12-13 (R2=0.349, ΔR2=0.017, ηp2=0.005; R2=0.254, ΔR2=0.023, ηp2=0.004, respectively). Specifically, among youth in low (but not high) family conflict environments, there was a significant negative association between CPN FC at ages 9-10 and psychiatric symptoms at ages 12-13 for HR youth, whereas this association was significantly positive for LR youth.

CONCLUSION: Findings suggest that CPN connectivity and family conflict in preadolescence may be prognostic risk markers for future symptoms related to parental depression. These markers may shed light on brain-based processes by which environmental adversity relates to heightened familial risk for psychopathology, though small effect sizes necessitate future investigation to better understand potential clinical relevance.

PMID:40882867 | DOI:10.1016/j.jaac.2025.08.014

Comput Biol Med. 2025 Aug 28;197(Pt A):110996. doi: 10.1016/j.compbiomed.2025.110996. Online ahead of print.

ABSTRACT

Generative AI for image synthesis has significantly progressed with the advent of advanced diffusion models. These models have set new benchmarks in creating high-quality and meaningful visual information. In this paper, we introduce TransUNET-DDPM, a novel framework that fuses transformer-based architectures with denoising diffusion probabilistic models (DDPMs) to generate high-quality, 2D and 3D intrinsic connectivity networks (ICNs). This architecture addresses limitations of traditional linear methods like independent component analysis (ICA) by leveraging the nonlinear modeling capabilities of DDPMs, further enhanced through transformer blocks that enable attention-driven feature encoding. To produce subject-specific 3D ICNs, an image-conditioned variant of TransUNET-DDPM is employed, utilizing a spatiotemporal encoder to incorporate resting-state fMRI (rs-fMRI) conditional information. Efficient training is achieved through a transfer learning strategy in which a large-scale, unconditional TransUNET-DDPM is first pretrained to capture general spatial and temporal patterns, followed by fine-tuning on a smaller, condition-specific neuroimaging dataset. Additionally, a class-conditioned version of the model is introduced for data augmentation in schizophrenia classification. By generating synthetic ICNs based on diagnostic labels, this variant enhances the robustness of classifiers, particularly in data-scarce scenarios. Furthermore, quantitative and qualitative evaluations demonstrate that our framework surpasses existing generative models in producing anatomically and functionally meaningful ICNs, with external dataset validation confirming its generalizability.

PMID:40882475 | DOI:10.1016/j.compbiomed.2025.110996

Front Neurol. 2025 Aug 13;16:1614935. doi: 10.3389/fneur.2025.1614935. eCollection 2025.

ABSTRACT

OBJECTIVE: To investigate the characteristics of brain network centrality in patients with cervical spondylotic myelopathy (CSM) by using degree centrality (DC) based on resting-state functional magnetic resonance imaging.

METHODS: We recruited 20 patients with CSM, along with 20 healthy controls (HC) who were matched in terms of age, gender, and educational background. The DC method was utilized to evaluate the changed spontaneous brain activities. The relationships between the DC values of different brain regions and the clinical features were analyzed by means of Pearson correlation analysis.

RESULTS: Compared with HC, CSM group showed decreased DC values in the left medial frontal gyrus, middle temporal gyrus and angular gyrus, and increased DC values were found in the left middle occipital gyrus, right supplementary motor area (p < 0.05). There was no significant correlation between DC values of abnormal region and clinical function score of CSM patients (p > 0.05).

CONCLUSION: CSM patients have abnormal DC distribution in the whole-brain functional networks, which might be related to cortical reorganization after chronic spinal cord injury.

PMID:40881788 | PMC:PMC12380896 | DOI:10.3389/fneur.2025.1614935

Front Neurol. 2025 Aug 13;16:1604342. doi: 10.3389/fneur.2025.1604342. eCollection 2025.

ABSTRACT

AIM: This randomized controlled trial investigated the effects of interactive dynamic scalp acupuncture (IDSA) on brain functional connectivity density (FCD) in patients with bilateral basal ganglia ischemic stroke (BBGIS), focusing on its potential to enhance motor recovery.

METHODS: Seventy BBGIS patients (aged 45-75 years, 1-3 months post-stroke, Brunnstrom stage II-V) and 40 age-and sex-matched healthy controls (HCs) were enrolled. Resting-state functional MRI (rs-fMRI) assessed baseline FCD differences between groups, with regions showing significant alterations correlated to Fugl-Meyer Assessment (FMA) scores selected as seed points. Patients were randomized to IDSA (n = 35) or Sham IDSA (n = 35) therapy for 4 weeks. IDSA targeted the MS6 acupoint using stainless steel needles (0.3 × 40 mm) rotated at 200 rpm during active limb movement, while Sham IDSA used blunt needles without skin penetration. Post-treatment rs-fMRI and FMA evaluations were conducted.

RESULTS: Compared to HCs, BBGIS patients exhibited reduced FCD in the right supplementary motor area (SMA_R) and right cerebellum-8 (C8_R), which positively correlated with FMA scores (r = 0.82 and r = 0.86, respectively; p < 0.0001). Post-treatment, the IDSA group showed significant increases in FCD in SMA_R (Δ = 0.64 ± 0.22, p < 0.001) and C8_R (Δ = 0.77 ± 0.91, p < 0.05), along with higher FMA scores (53.23 ± 13.6 vs. Sham 44.35 ± 11.2, p < 0.05), indicating improved motor function.

CONCLUSION: IDSA therapy enhances functional connectivity in SMA_R and C8_R, which are associated with motor recovery in BBGIS patients. These findings support IDSA as a potential intervention for stroke rehabilitation (Study registration: China National Clinical Trial Registry, ChiCTR2200055463).

PMID:40881782 | PMC:PMC12381643 | DOI:10.3389/fneur.2025.1604342

Front Neurosci. 2025 Aug 13;19:1612271. doi: 10.3389/fnins.2025.1612271. eCollection 2025.

ABSTRACT

INTRODUCTION: The "Mesocircuit" model explains consciousness loss and recovery following severe brain injury as disconnection of cortical-subcortical circuits. Excessive inhibition of prefrontal cortex/thalamus by internal globus pallidus (GPi) is proposed as key to disorders of consciousness (DoC). However, recent research found external globus pallidus (GPe) crucial for arousal in DoC patients.

METHODS: To investigate the relationship between GPe and arousal, this study enrolled 50 patients with DoC who were admitted to the Department of Neurosurgery, Shaanxi Provincial People's Hospital, from November 2022 to November 2024. Patients were stratified into coma, UWS and MCS groups based on behavioral assessments (GCS/CRS-R) and underwent resting-state functional MRI (rs-fMRI). Whole-brain functional connectivity (FC) was analyzed with GPe as seed regions. Comparison and correlation analysis of FC strength between GPe and brain regions of subjects in two groups, with relation to arousal, were conducted.

RESULTS: In the FC analysis, Coma group showed weakened FC between GPe and left middle frontal gyrus, middle temporal gyrus, superior frontal gyrus, precuneus, and right precentral gyrus. Coma group demonstrated enhanced FC between GPe and thalamus: Central lateral (left CL and right CL). Correlation analysis revealed these abnormally weakened FC positively correlated with patients' arousal state, among which GPe-left superior frontal gyrus FC (rs = 0.61, P < 0.001) and GPe-left CL FC (rs = 0.86, P < 0.001) showed strongest correlation with arousal.

CONCLUSION: Our findings provide neuroimaging evidence of disrupted functional connectivity between GPe and distributed cortical regions, including the left middle frontal gyrus, middle temporal gyrus, superior frontal gyrus, precuneus, CL, and right precentral gyrus, CL. These connectivity abnormalities spanning executive control, default mode, and primary motor networks suggest their coordinated role in consciousness impairment. Notably, arousal levels positively correlated with FC between GPe and cortical regions, specifically the left superior frontal gyrus, while negatively correlating with FC between GPe and CL. This suggests that altered GPe-cortical and GPe-thalamic FC may underlie the neural substrate for arousal regulation in patients with DoC.

PMID:40880850 | PMC:PMC12380903 | DOI:10.3389/fnins.2025.1612271

PLoS One. 2025 Aug 29;20(8):e0330986. doi: 10.1371/journal.pone.0330986. eCollection 2025.

ABSTRACT

Symptoms of attention deficit/hyperactivity disorder (ADHD) are common, severe and highly impairing in children with prenatal alcohol exposure (PAE), but often non-responsive to medication, leaving many with no good treatment. External trigeminal nerve stimulation (TNS) is a minimal risk, non-invasive neuromodulatory intervention that is FDA-cleared for ADHD. No formal trial, however, has tested TNS in children with known PAE. We present here the protocol of the first clinical trial of TNS in children with ADHD associated with PAE. The study also uses multimodal MRI to explore possible brain mechanisms of TNS. An open-label pilot will be conducted of 4 weeks of TNS in 30 youth with ADHD associated with PAE, recruited in Southern California. Children will receive TNS nightly for 4 weeks. Safety, tolerability, and preliminary efficacy will be evaluated. Efficacy outcomes include change in the investigator-administered parent-rated ADHD Rating Scale and the Clinical Global Impression Improvement (CGI-I) score for ADHD. Effects on executive function, mood, and sleep are also assessed. Maintenance of effects is evaluated at 4-week follow-up. TNS-related brain changes and predictors of response are measured using structural MRI, diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), and resting-state fMRI before and after treatment. This study will determine whether TNS is feasible in children with ADHD and PAE, whether it improves clinical and cognition symptoms, and whether efficacy persists 4 weeks. Trial registration: clinicaltrials.gov (NCT06847165) and protocol ID: IRB-24-0648-AM-007 June 16, 2025.

PMID:40880352 | DOI:10.1371/journal.pone.0330986

Hum Brain Mapp. 2025 Sep;46(13):e70320. doi: 10.1002/hbm.70320.

ABSTRACT

Understanding functional brain development during childhood and adolescence is essential for identifying typical neurodevelopmental trajectories. While resting-state fMRI (rs-fMRI) has become a key tool in developmental neuroscience, few studies have jointly examined multiple functional metrics to comprehensively characterize typical brain maturation across youth. We analyzed rs-fMRI data from 395 neurotypical participants aged 6-20 years from the ABIDE I and II datasets. Voxel-wise analyses were conducted using three complementary rs-fMRI metrics: fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Data were harmonized across sites using ComBat and CovBat methods implemented in DPABI to minimize scanner-related variability. Correlation analyses and ANOVA/ANCOVAs were performed to examine developmental age effects. Our results revealed a general pattern of declining local and interhemispheric connectivity with increasing age, across all measures. fALFF decreases were most pronounced in the medial orbitofrontal, caudate, medial occipital cortex, and cerebellum (peak r = -0.210); ReHo showed reductions in the insula and caudate (peak r = -0.169); and VMHC declines were observed in the putamen, cerebellum, superior parietal lobules, and caudate (peak r range = -0.206 to -0.187). These findings outline a developmental trajectory characterized by increasing functional integration and network specialization from late childhood through adolescence. The combined use of fALFF, ReHo, and VMHC provides a robust multitechnical framework for characterizing typical brain development and offers a valuable benchmark for identifying developmental deviations in clinical populations.

PMID:40879533 | DOI:10.1002/hbm.70320

Diabetes Obes Metab. 2025 Aug 29. doi: 10.1111/dom.70058. Online ahead of print.

ABSTRACT

AIMS: Metabolic-bariatric surgery (MBS) remains a cornerstone of obesity treatment, yet 15%-30% of patients fail to achieve its intended benefits. Existing clinical and biochemical markers offer limited value in identifying who will respond favourably to this intervention. We hypothesize that the long-term success of MBS is influenced by individual differences in preoperative brain function.

MATERIALS AND METHODS: We collected presurgical resting-state fMRI data from 45 patients undergoing MBS, with the aim of identifying neural patterns associated with achieving at least 50% excess weight loss 12 months post-surgery. The data were analysed using both conventional methods and a high-powered machine learning approach. For the latter, we trained five predictive models on functional connectivity, regional brain activity, and clinical variables. We then applied SHapley Additive exPlanations (SHAP) to the best-performing model to interpret its internal logic, thereby revealing the neural features most strongly linked to treatment success.

RESULTS: Conventional methods proved inadequate for this study. A multilayer perceptron model, trained exclusively on functional connectivity data, achieved a noteworthy AUC of 0.85. Its SHAP analysis revealed key neural circuits in the postcentral gyrus, dorsolateral prefrontal cortex, and angular gyrus-regions associated with interoception, executive control, and social-cognitive processes such as theory of mind.

CONCLUSION: Explainable AI-powered fMRI analysis uncovered subtle neural patterns that conventional methods failed to detect. These findings suggest that a patient's "neural readiness" for MBS may extend beyond self-regulatory circuits. It may also depend on their capacity to perceive and interpret internal bodily signals and to process emotional information-both personal and social.

PMID:40878593 | DOI:10.1111/dom.70058

Epilepsy Behav. 2025 Aug 27;171:110645. doi: 10.1016/j.yebeh.2025.110645. Online ahead of print.

ABSTRACT

Posterior gliosis is a major substrate underlying drug resistant epilepsy (DRE) in children and young adults in low-middle income countries. Neonatal hypoglycemia and prolonged partial asphyxia either isolated or combined are major risk factors for posterior gliosis. The epilepsy associated with posterior gliosis has a spectrum of severity with early onset drug resistant epileptic encephalopathies with disabling co-morbidities at one end and pharmaco-responsive focal epilepsy in a normal child at the other. Intermediate severity syndromes are common. Disabling co-morbidities are typically cognitive and visual with sparing of motor skills. The imaging spectrum consist of bilateral symmetric or asymmetric parieto-occipital gliosis though unilateral occipital lesions are not uncommon. EEG too has a wide range of abnormalities but is of limited benefit in localisation and lateralisation. Standard management strategies for posterior gliosis associated epilepsy offer unique challenges in low-middle income countries where more DRE is encountered. Issues in anti-seizure medication (ASM) treatments and ketogenic diet are highlighted. Resective surgery for unilateral/asymmetric bilateral gliosis has an established place. Challenges remain in predicting post-operative visual deficits. The use of resting state fMRI is discussed. Seizure freedom is achievable in ∼25 % of medically managed patients, though ASM discontinuation fails in the majority.

PMID:40876198 | DOI:10.1016/j.yebeh.2025.110645

Mov Disord. 2025 Aug 28. doi: 10.1002/mds.70021. Online ahead of print.

ABSTRACT

BACKGROUND: The central pathology causing idiopathic focal dystonia remains unclear. The recently identified somato-cognitive action network (SCAN) has been implicated.

OBJECTIVE: We tested whether the effector-agnostic SCAN may constitute a central pathology shared across dystonia subtypes, whereas the effector-specific regions in the primary sensorimotor cortex may show distinct functional changes specific to the dystonic body part.

METHODS: We collected functional magnetic resonance imaging (MRI) from patients with focal dystonia (laryngeal dystonia [LD], N = 24; focal hand dystonia [FHD], N = 18) and healthy control participants (N = 21). Regions of interest were selected a priori within the basal ganglia-thalamo-cortical and cerebello-thalamo-cortical sensorimotor pathways. We investigated dystonia-dependent resting-state connectivity changes: between SCAN and related cortical regions, between cortical and noncortical regions, and among noncortical regions. Cortical network boundaries were individualized based on resting-state data. Separately, individualized hand and mouth/larynx regions were also generated from task-based MRI (finger-tapping and phonation, respectively) for comparison.

RESULTS: Both focal dystonia subtypes showed significant functional changes (P = 0.048 for LD, P = 0.017 for FHD) compared to controls, driven by SCAN's higher functional connectivity to task-based mouth/larynx region and concomitantly lower connectivity to the cingulo-opercular network. No significant subcortical or cerebellar changes were observed when LD and FHD were modeled as independent groups. However, exploratory analysis combining LD and FHD suggested a dystonia-dependent asynchronization between SCAN and sensorimotor cerebellum (P = 0.010) that may indicate a pathological rather than compensatory process.

CONCLUSIONS: We demonstrate that SCAN is uniquely associated with focal dystonia dysfunction beyond the dystonic effector regions, offering insights into pathophysiology and treatments. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PMID:40874549 | DOI:10.1002/mds.70021

J Neurosci Res. 2025 Sep;103(9):e70076. doi: 10.1002/jnr.70076.

ABSTRACT

Temporal lobe epilepsy (TLE) is a focal epilepsy extensively examined through advanced neuroimaging techniques to elucidate its pathophysiological mechanisms. This study investigates the differences in dynamic brain activity and gene expression in TLE patients. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 60 TLE patients and 30 healthy controls (HC). Dynamic amplitude of low-frequency fluctuations (dALFF) was employed to identify regions with dALFF variance differences, which were then designated as regions of interest (ROIs). Co-activation patterns (CAP) was constructed to compare brain dynamic changes. Pearson's correlation analysis and pathway enrichment analysis were used to explore the potential molecular mechanisms associated with atypical neural dynamics in TLE. Five CAP states were identified from the rs-fMRI data. Compared to HC, TLE with cognitive normal (TLE-CN) and TLE with cognitive impairment (TLE-CI) patients exhibited atypical state-specific temporal characteristics, including number of states (counts), fraction of time, persistence, resilience, and transition probability (TP) between states. Importantly, dynamic indicators of CAP states were significantly correlated with cognitive performance. Furthermore, 2752 genes were significantly associated with atypical dynamic brain states in TLE, with these genes primarily enriched in synapse-related pathways. This study offers novel insights into atypical neural dynamics from a temporal perspective. The brain network dynamics defined by CAP analysis deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, revealing a link between atypical neural architecture and gene expression in TLE.

PMID:40873228 | DOI:10.1002/jnr.70076

Entropy (Basel). 2025 Aug 1;27(8):820. doi: 10.3390/e27080820.

ABSTRACT

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains.

PMID:40870292 | DOI:10.3390/e27080820

Life (Basel). 2025 Aug 20;15(8):1324. doi: 10.3390/life15081324.

ABSTRACT

Olfactory impairment was assessed in akinetic-rigid (PDAR) and tremor-predominant (PDT) subtypes of Parkinson's disease (PD), classified based on motor symptoms. Seventeen PDAR, fifteen PDT, and twenty-four cognitively normal (CN) participants completed the University of Pennsylvania Smell Identification Test (UPSIT). Groups were well-matched for age and demographic variables, with cognitive performance statistically controlled. Resting-state fMRI (rs-fMRI) and seed-based functional connectivity (FC) analyses were conducted to characterize olfactory network (ON) connectivity across groups. UPSIT scores were significantly lower in PDAR compared to PDT. Consistently, ON FC values were reduced in PDAR relative to both PDT and CN. FC of the primary olfactory cortex (POC) significantly differed between CN and the PD subtypes. Furthermore, connectivity in the orbitofrontal cortex and insula showed significant differences between PDAR and PDT, as well as between PDAR and CN. Notably, ON FC between the left hippocampus and the posterior cingulate cortex (PCC) also differed significantly between PDAR and PDT. These findings reveal distinct ON FC patterns across PDAR and PDT subtypes. Variations in UPSIT scores suggest that motor symptom subtype is associated with olfactory performance. Moreover, ON connectivity closely paralleled the UPSIT scores, reinforcing a neural basis for olfactory deficits in PD. Given the accelerated motor and cognitive decline often observed in the PDAR, these results support the potential of olfactory impairment as a clinical marker for disease severity.

PMID:40868971 | DOI:10.3390/life15081324

Brain Sci. 2025 Jul 26;15(8):796. doi: 10.3390/brainsci15080796.

ABSTRACT

Background: Autism spectrum disorder (ASD) is characterised by atypical behavioural and cognitive diversity, yet the neural underpinnings linking brain activity and individual presentations remain poorly understood. In this study, we investigated the relationship between resting-state functional magnetic resonance imaging (fMRI) signal complexity and intelligence (full-scale intelligence quotient (FIQ); verbal intelligence quotient (VIQ); and performance intelligence quotient (PIQ)) in male adults with ASD (n = 14) and matched neurotypical controls (n = 15). Methods: We used three complexity-based metrics: Hurst exponent (H), fuzzy approximate entropy (fApEn), and fuzzy sample entropy (fSampEn) to characterise resting-state fMRI signal dynamics, and correlated these measures with standardised intelligence scores. Results: Using a whole-brain measure, ASD participants showed significant negative correlations between PIQ and both fApEn and fSampEn, suggesting that increased neural irregularity may relate to reduced cognitive-perceptual performance in autistic individuals. No significant associations between entropy (fApEn and fSampEn) and PIQ were found in the control group. Group differences in brain-behaviour associations were confirmed through formal interaction testing using Fisher's r-to-z transformation, which showed significantly stronger correlations in the ASD group. Complementary regression analyses with interaction terms further demonstrated that the entropy (fApEn and fSampEn) and PIQ relationship was significantly moderated by group, reinforcing evidence for autism-specific neural mechanisms underlying cognitive function. Conclusions: These findings provide insight into how cognitive functions in autism may not only reflect deficits but also an alternative neural strategy, suggesting that distinct temporal patterns may be associated with intelligence in ASD. These preliminary findings could inform clinical practice and influence health and social care policies, particularly in autism diagnosis and personalised support planning.

PMID:40867129 | DOI:10.3390/brainsci15080796

Adv Sci (Weinh). 2025 Aug 27:e04956. doi: 10.1002/advs.202504956. Online ahead of print.

ABSTRACT

The intrinsic brain activity observed through resting-state functional magnetic resonance imaging (rs-fMRI) offers significant information to investigate underlying brain processes. Since traditional latency analysis models are limited to assessing macroscopic functional dynamics, the physical significance of fMRI-derived latency structures remains unexplored. To fill the gap, the spatiotemporal characteristics of fMRI are investigated using latency structure analysis in 469 neurologically healthy adults. After calculating the lagged cross-covariance of the time series, principal component analysis is applied to generate latency eigenvectors. These eigenvectors are associated with neural parameters derived from the biophysical model, revealing significant correlations with excitatory and inhibitory synaptic gating, recurrent connection, and excitation/inhibition balance. Association analyses with temporal and spatial features revealed that the latency eigenvectors are significantly associated with intrinsic neural timescale, and each latency eigenvector is paired with major brain axes from functional gradients, including the sensory-transmodal, visual-motor, and multiple demand-task-negative systems. These findings indicate that the latency model aligns with a seminal model of cortical hierarchy and intrinsic neural signaling. The clinical implications of latency eigenvectors are validated in autism spectrum disorder. This study enhances the understanding of the spatiotemporal characteristics of fMRI signals, providing insights into the physiology underlying the latency structures of brain signals.

PMID:40867070 | DOI:10.1002/advs.202504956

Neuroimage. 2025 Aug 25:121431. doi: 10.1016/j.neuroimage.2025.121431. Online ahead of print.

ABSTRACT

Brain activity exhibits substantial temporal variability during cognitive processes, yet traditional fMRI analyses often fail to capture these dynamic patterns. Co-activation pattern (CAP) analysis has emerged as a promising method to study brain dynamics. CAP analysis provides a powerful framework for capturing transient brain states, however, its application to cognitive tasks remains very limited, with no prior studies specifically investigating its role in reasoning performance. This study investigated CAPs during reasoning tasks, their relationship with cognitive performance and individual differences. We applied CAP analysis to fMRI data from 303 participants performing three reasoning tasks-Matrix Reasoning, Letter Sets, and Paper Folding-along with resting-state data. Using K-means clustering, we identified four distinct CAPs, each exhibiting unique spatial and temporal characteristics. These CAPs were analyzed in relation to predefined resting-state networks, revealing their functional relevance to cognitive task engagement. Key temporal metrics, including fraction occupancy, dwelling time, and transition probabilities, were assessed across reasoning tasks and resting state. The results demonstrate that CAP2 and CAP3 are predominantly engaged during reasoning tasks, with CAP2 strongly overlapping with the visual network and CAP3 exhibiting concurrent default mode and sensorimotor network activations. CAP1, primarily dominant during rest, showed prolonged engagement in older individuals, while CAP4 appeared to function as a transitional state facilitating network reorganization. Regression analyses link longer dwelling times and higher fraction occupancy of CAP2 and CAP3 to superior reasoning performance, whereas excessive transitions to CAP4 negatively impacted cognitive task outcomes. Additionally, aging was associated with reduced engagement in task-relevant CAPs and an increased tendency to transition into baseline-like states. These findings underscore the critical role of dynamic brain state reconfigurations in supporting cognition specifically reasoning and highlight CAP analysis as a powerful tool for studying transient brain function and individual cognitive differences.

PMID:40865621 | DOI:10.1016/j.neuroimage.2025.121431

J Pers Med. 2025 Aug 3;15(8):352. doi: 10.3390/jpm15080352.

ABSTRACT

Background: Transcranial direct current stimulation (tDCS) has emerged as a promising neuromodulatory tool for language rehabilitation in chronic aphasia. However, the effects of bi-hemispheric, multisite stimulation remain largely unexplored, especially in people with chronic and treatment-resistant language impairments. The goal of this study is to look at the effects on behavior and brain activity of an individualized language training program that combines bi-hemispheric multisite anodal tDCS with personalized language training for Albert, a patient with long-standing, treatment-resistant non-fluent aphasia. Methods: Albert, a right-handed retired physician, had transcortical motor aphasia (TCMA) subsequent to a left-hemispheric ischemic stroke occurring more than six years before the operation. Even after years of traditional treatment, his expressive and receptive language deficits remained severe and persistent despite multiple rounds of traditional therapy. He had 15 sessions of bi-hemispheric multisite anodal tDCS aimed at bilateral dorsal language streams, administered simultaneously with language training customized to address his particular phonological and syntactic deficiencies. Psycholinguistic evaluations were performed at baseline, immediately following the intervention, and at 1, 2, 3, and 6 months post-intervention. Resting-state fMRI was conducted at baseline and following the intervention to evaluate alterations in functional connectivity (FC). Results: We noted statistically significant enhancements in auditory sentence comprehension and oral reading, particularly at the 1- and 3-month follow-ups. Neuroimaging showed decreased functional connectivity (FC) in the left inferior frontal and precentral regions (dorsal stream) and in maladaptive right superior temporal regions, alongside increased FC in left superior temporal areas (ventral stream). This pattern suggests that language networks may be reorganizing in a more efficient way. There was no significant improvement in phonological processing, which may indicate reduced connectivity in the left inferior frontal areas. Conclusions: This case underscores the potential of combining individualized, network-targeted language training with bi-hemispheric multisite tDCS to enhance recovery in chronic, treatment-resistant aphasia. The convergence of behavioral gains and neuroplasticity highlights the importance of precision neuromodulation approaches. However, findings are preliminary and warrant further validation through controlled studies to establish broader efficacy and sustainability of outcomes.

PMID:40863414 | DOI:10.3390/jpm15080352