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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

Front Endocrinol (Lausanne). 2025 Aug 8;16:1598943. doi: 10.3389/fendo.2025.1598943. eCollection 2025.

ABSTRACT

Diminished ovarian reserve (DOR) is characterized by a decrease in the quantity and quality of oocytes, leading to reduced chances of natural conception and a poorer response to fertility treatments. Along with these reproductive challenges, DOR often causes psychological symptoms such as depression, anxiety, and sleep disturbances, which negatively affect overall well-being and quality of life. Acupuncture has been proposed as a promising complementary therapy for DOR, but the mechanisms through which it exerts its effects are not yet fully understood. This study aims to investigate the effects of acupuncture on ovarian function, psychological well-being, and the central nervous system in women with DOR. We will recruit 42 women with DOR and 21 healthy controls (HCs), randomly assigning DOR patients to receive either verum acupuncture (VA) or sham acupuncture (SA) for 12 weeks. Ovarian function will be assessed using Anti-Müllerian hormone (AMH), antral follicle count (AFC), and follicle-stimulating hormone (FSH). Psychological well-being will be evaluated using the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Self-Rating Scale of Sleep (SRSS). To explore the neurological effects, resting-state functional connectivity (rsFC) of the hypothalamus will be assessed using functional magnetic resonance imaging (fMRI). This research aims to clarify how acupuncture affects the central nervous system, hormonal regulation, and ovarian function in women with DOR. The findings may provide valuable insights for developing evidence-based acupuncture protocols that can improve both reproductive outcomes and quality of life for women with DOR.

PMID:40862111 | PMC:PMC12370520 | DOI:10.3389/fendo.2025.1598943

IBRO Neurosci Rep. 2025 Aug 12;19:417-425. doi: 10.1016/j.ibneur.2025.08.006. eCollection 2025 Dec.

ABSTRACT

OBJECTIVE: The aim of the study was to investigate gender differences in the brains of young healthy adults, by calculating the regional homogeneity (ReHo) values of resting-state functional magnetic resonance imaging (rs-fMRI). Thereby providing candidate imaging biomarkers for risk stratification of neurodegenerative diseases and offering a basis for their early screening and targeted intervention.

METHODS: Forty-two (42) healthy young adults (21males and 21females) were examined using resting-state fMRI. We employed the statistical method of regional homogeneity (ReHo) to compare the brains of males and females.

RESULTS: The female group exhibited higher activity intensity in the right supramarginal gyrus, but significantly lower activity intensity in the left dorsolateral prefrontal cortex, the right frontal eye field, the right premotor cortex and the right superior temporal gyrus compared to the male group.

CONCLUSION: Males have greater advantages in working memory, conscious decision-making behavior, visual-motor skills, physical reaction speed, rhythmic perception and language perception, while females show better episodic memory and visual imagination. High ReHo in the left DLPFC of men is a screening marker for high-risk groups of men with AD. High ReHo in the right superior marginal gyrus of women is an early warning biomarker for PTSD or depression.

PMID:40861248 | PMC:PMC12375249 | DOI:10.1016/j.ibneur.2025.08.006

Front Neurosci. 2025 Aug 11;19:1627838. doi: 10.3389/fnins.2025.1627838. eCollection 2025.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder with some progressive impairment and an unclear pathogenesis.

PURPOSE: This study aimed to use resting-state functional magnetic resonance imaging (rs-fMRI) and graph analysis approaches to compare changes in brain functional network topology in PD at different disease stages.

MATERIALS AND METHODS: A total of 58 PD patients, comprising 29 early-stage PD (PD-E) and 29 middle-to-late stage PD (PD-M), and 29 age- and sex-matched healthy control (HC) participants, were recruited. All subjects underwent clinical assessments and magnetic resonance imaging (MRI) scanning. We analyzed alterations in the global, regional, and modular topological characteristics of brain functional networks among different disease stages of PD patients and HC participants. Furthermore, we also examined the relationship between topological features with significant group effects and clinical characteristics, including the Movement Disorder Society's Unified Parkinson's Disease Rating Scale III (MDS-UPDRS III) score and Hoehn and Yahr (H&Y) stage.

RESULTS: At the global level, PD-M and PD-E exhibited lower clustering coefficient, and PD-M also exhibited lower local efficiency and normalized characteristic path length relative to HC. At the regional level, PD-M and PD-E showed lower nodal centrality in temporal-occipital regions and higher centrality in brain regions related to the default mode network and the frontoparietal control network compared to HC. Notably, nodal centrality metrics of the left middle frontal gyrus and the temporal pole of the right middle temporal gyrus were associated with the MDS-UPDRS III score and H&Y stage.

CONCLUSION: This study found that the brain functional networks were disrupted at varying degrees in patients with PD at different disease stages. These findings contribute to our understanding of the topological changes in the neural networks associated with the severity of PD.

PMID:40860845 | PMC:PMC12375575 | DOI:10.3389/fnins.2025.1627838

Commun Biol. 2025 Aug 27;8(1):1287. doi: 10.1038/s42003-025-08703-7.

ABSTRACT

While traditionally regarded as "noise", blood-oxygenation-level-dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) fluctuations coupled to systemic physiology-such as heart rate and respiratory changes-also hold valuable information about brain vascular properties and autonomic function. In this study, we leverage these physiological signals to characterize age-related changes in brain physiology, drawing on a large dataset from the Lifespan Human Connectome Project Aging study. Our findings reveal that aging is associated with globally slower respiratory fMRI responses, alongside faster cardiac fMRI responses and enhanced brain-cardiac signal coupling. Moreover, we show that the impact of age on physiological fMRI signals exhibits a notable turning point after age 60, suggesting a critical role of declining vascular health and autonomic function in aging. The potential impact of age-related changes in brain structure, tissue perfusion, and in-scan arousal states on the identified physiological fMRI patterns is also tested and discussed. Altogether, our results underscore significant age effects in the fMRI signatures of systemic physiology, emphasizing the pivotal role of altered vascular properties and autonomic function in aging. Methodologically, this study also demonstrates the utility of resting-state fMRI for extracting multi-parametric information about brain physiology, offering new biomarker opportunities that complement established functional connectivity metrics.

PMID:40858848 | DOI:10.1038/s42003-025-08703-7

Eur J Phys Rehabil Med. 2025 Jun;61(3):462-471. doi: 10.23736/S1973-9087.25.08872-0.

ABSTRACT

BACKGROUND: Visuospatial function is a core domain of functional cognition in stroke. Post-stroke cognitive impairment disrupts rehabilitation practice, highlighting the importance of characterizing patients with higher-order visuospatial dysfunction to inform personalized rehabilitation strategies. Although neuroimaging offers insights into disease-related mechanisms, its clinical application remains limited.

AIM: The aim of this paper was to investigate whether the residual resting-state functional connectivity supports higher-order visuospatial function after stroke and whether changes in connectivity can characterize patients with visuospatial dysfunction.

DESIGN: Observational study.

SETTING: Inpatient rehabilitation ward at Fujita Health University Hospital in Japan.

POPULATION: Fifty-eight patients with stroke.

METHODS: Visuospatial analogical reasoning was assessed using Raven's Colored Progressive Matrices (RCPM). Resting-state functional connectivity was evaluated using functional magnetic resonance imaging (fMRI). Empirical covariance matrices and group-sparse inverse covariance (GSIC) matrices were computed from the fMRI data, with the latter negated to estimate partial correlations representing direct connectivity. Correlations between connectivity measures and RCPM scores were analyzed, alongside data-driven clustering to stratify patients.

RESULTS: No significant correlation was found between empirical covariance connectivity and RCPM scores. However, GSIC-based analysis revealed a significant inverse correlation between connectivity of the posteromedial and the left inferior parietal cortex and RCPM scores. Higher parietal connectivity was associated with lower RCPM performance. Patients in the highest connectivity cluster exhibited severe impairments in visuospatial analogical reasoning, particularly in tasks requiring the integration of discrete figures into spatially related wholes. The lesions in these patients were predominantly localized in the left subcortex.

CONCLUSIONS: Medio-lateral parietal connectivity may underlie visuospatial analogical reasoning after stroke.

CLINICAL REHABILITATION IMPACT: Clustering analysis highlighted a distinct pattern of low scores in patients with increased parietal connectivity, suggesting that parietal connectivity changes have the potential for characterizing patients with severe dysfunction.

PMID:40856377 | DOI:10.23736/S1973-9087.25.08872-0

Psychol Med. 2025 Aug 26;55:e245. doi: 10.1017/S0033291725101451.

ABSTRACT

BACKGROUND: Methadone maintenance treatment (MMT) and protracted abstinence (PA) effectively reduce the craving for heroin among individuals with heroin use disorder (HUD). However, the difference in their effects on brain function, especially the coupling among the large-scale brain networks (default mode [DMN], salience [SN], and executive control [ECN] networks), remains unclear. This study analyzed the effects of the MMT and PA on these networks and the predictive value of the bilateral resource allocation index (RAI) for craving for heroin.

METHODS: Twenty-five individuals undergoing the MMT, 22 undergoing the PA, and 51 healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis identified the ECN, DMN, and SN. The SN-ECN and SN-DMN connectivity and the bilateral RAI were evaluated. The relationships between network coupling and clinical and psychological characteristics were analyzed. The multiple linear regression model identified significant variables for predicting craving scores.

RESULTS: The MMT group showed significantly stronger SN-left ECN (lECN) coupling and left RAI than the PA group. In the MMT group, SN-lECN connectivity and bilateral RAI were positively correlated with the total methadone dose. In both treatment groups, SN-right ECN (rECN) connectivity and right RAI were negatively correlated with craving. The models revealed that the bilateral RAI and the MMT and PA were associated with the craving.

CONCLUSIONS: The MMT enhances SN-lECN coupling and the left RAI more than the PA, possibly due to higher control modulation. The RAI could help predict heroin craving in individuals with HUD undergoing either treatment program.

PMID:40856291 | DOI:10.1017/S0033291725101451

Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635270. Epub 2024 Aug 22.

ABSTRACT

High-resolution fMRI provides a window into the brain's mesoscale organization. Yet, higher spatial resolution increases scan times, to compensate for the low signal and contrast-to-noise ratio. This work introduces a deep learning-based 3D super-resolution (SR) method for fMRI. By incorporating a resolution-agnostic image augmentation framework, our method adapts to varying voxel sizes without retraining. We apply this innovative technique to localize fine-scale motion-selective sites in the early visual areas. Detection of these sites typically requires ≤ 1mm isotropic data, whereas here, we visualize them based on lower resolution (2-3mm isotropic) fMRI data. Remarkably, the super-resolved fMRI is able to recover high-frequency detail of the interdigitated organization of these sites (relative to the color-selective sites), even with training data sourced from different subjects and experimental paradigms - including non-visual resting-state fMRI, underscoring its robustness and versatility. Quantitative and qualitative results indicate that our method has the potential to enhance the spatial resolution of fMRI, leading to a drastic reduction in acquisition time.

PMID:40855854 | PMC:PMC12376370 | DOI:10.1109/isbi56570.2024.10635270

Psychiatry Clin Neurosci. 2025 Aug 25. doi: 10.1111/pcn.13887. Online ahead of print.

ABSTRACT

AIM: Cognition varies across people with psychosis, including within a specific diagnosis. An important issue is identifying psychosis-specific neuro-cognitive dysfunctions. We addressed this issue by studying patterns of relationships between cognition and multiple other measures in persons with psychosis, their first-degree biological relatives, and healthy individuals (largest possible n = 2826).

METHODS: Brief Assessment of Cognition and Wide Range Achievement Test estimated cognitive performance. Neuroanatomical measures were FreeSurfer parcellations of 3T MRI structural brain scans. Brain functioning measures included saccades, smooth pursuit eye movements, stop signal, EEG, ERPs, resting state fMRI, plus clinical characteristics. Overall associations between 452 measures of brain structure-function and clinical characteristics (predictors) with cognitive performance (criterion) were estimated using the High Dimensional Empirical Bayes Screening algorithm.

RESULTS: The model yielded a common slope of predictors on cognitive performance (slope = 0.18, r = 0.33, P < 0.001). The majority (85%) of predictors fit this function, called the BAsic NeuroCognitive Continuum (BANCC). This relationship was stronger for psychosis probands (slope = 0.20, r = 0.38) than for relatives (slope = 0.09, r = 0.17) and healthy persons (slope = 0.11, r = 0.22). There were predictor-specific deviations from the common slope. Variables more strongly associated with cognitive performance (frontal-temporal-parietal lobe volumes, hippocampal regions, antisaccade performance) may tap neural architecture common to primary psychosis pathology. Variables unrelated to cognitive performance (intrinsic neural activity, volumes of lateral thalamic nuclei) distinguish specific neurophysiologically defined B-SNIP psychosis Biotypes and may capture signatures of psychosis pathophysiology.

DISCUSSION: BANCC is identifiable across humans, but deviations from that common attribute identify features of brain structure-function perhaps most central and specific to psychosis-related pathophysiology.

PMID:40855769 | DOI:10.1111/pcn.13887

Clin Neuroimaging (Hoboken). 2025;2(1):e70022. doi: 10.1002/neo2.70022. Epub 2025 Jun 26.

ABSTRACT

BACKGROUND AND PURPOSE: Neurogenic lower urinary tract dysfunction (NLUTD) affects over 80% of individuals with multiple sclerosis (MS), leading to significant morbidity and mortality due to storage and voiding dysfunction. This study aims to investigate the altered functional connectivity (FC) in cerebellar regions involved in bladder control in women with MS and NLUTD, compared to healthy controls, in both empty and full bladder states using concurrent urodynamics and functional magnetic resonance imaging (fMRI).

METHODS: We recruited 11 women with clinically stable MS and NLUTD and 10 healthy controls. Brain imaging data was collected using 7T MRI scanners, and functional connectivity was analyzed with three cerebellar regions of interest (ROIs) associated with bladder control. Functional connectivity data was processed using the CONN toolbox, and FC patterns were compared between groups during both resting empty and full bladder states.

RESULTS: In the empty bladder state, MS patients exhibited stronger intracerebellar FC, particularly in the right Crus 1, suggesting decreased motor control of the pelvic floor. Additionally, decreased FC was observed in the precuneus and prefrontal cortex, regions associated with bladder control. During the full bladder state, MS patients showed decreased FC in temporal, occipital, and prefrontal cortex, indicating impaired executive control over voiding.

CONCLUSION: This study highlights altered cerebellar connectivity in MS patients with NLUTD, providing novel insights into the neural mechanisms underlying bladder dysfunction and identifying potential therapeutic targets for restoring continence.

PMID:40852051 | PMC:PMC12369983 | DOI:10.1002/neo2.70022

Imaging Neurosci (Camb). 2025 Aug 20;3:IMAG.a.114. doi: 10.1162/IMAG.a.114. eCollection 2025.

ABSTRACT

Independent component analysis (ICA) denoising methods can be highly effective for reducing functional magnetic resonance imaging (fMRI) noise. ICA denoising method success heavily depends, however, on the accurate classification of fMRI data ICs as either neural signal or noise. While manual IC classification ("manual ICA denoising") is a current gold-standard, it requires extensive time and training. Automated methods of IC classification ("automated ICA denoising"), meanwhile, are less accurate and effective, especially in clinical populations where motion artifacts are more common. To address these challenges, a novel denoising method, Comprehensive Independent Component Analysis Denoising Assistant (CICADA), was developed. Uniquely, CICADA uses manual classification guidelines to automatically, comprehensively, and accurately capture most common sources of fMRI noise. As such, we hypothesized that CICADA would perform similarly to manual ICA denoising and outperform other current automated denoising methods. CICADA was evaluated against two well-established automated ICA denoising methods (FIX and ICA-AROMA) across three fMRI datasets. The datasets included high-motion resting-state (N = 57) and visual-task data (N = 53), both from individuals with schizophrenia, as well as low-motion resting-state healthy control data from an openly available dataset (N = 56). IC classification accuracy was first evaluated against manual IC classification in a subset (N = 30) of each dataset. Denoising performance efficacy was then evaluated with commonly used quality control (QC) benchmarks and correlations with fMRI noise profiles across all data. With a 97.9% mean overall accuracy in IC classification, CICADA performed nearly as well as manual IC classification and was significantly more accurate than FIX (92.9% mean overall accuracy; all p-values < 0.01) and ICA-AROMA (83.8% mean overall accuracy; all p-values < 0.001). CICADA also matched or outperformed FIX and ICA-AROMA across most QC and noise profile metrics across all data. Furthermore, CICADA greatly eased implementation of manual ICA denoising by decreasing the number of ICs a user must inspect by an average of 75%. Overall, CICADA is a novel, accurate, comprehensive, and automated ICA denoising tool for use in both resting-state and task-based fMRI. It performed similarly to the labor-intensive manual IC classification gold-standard and, in some datasets, outperformed current automated ICA denoising methods. Finally, CICADA may facilitate more efficient manual ICA denoising without reducing efficacy.

PMID:40851911 | PMC:PMC12368612 | DOI:10.1162/IMAG.a.114