Most recent paper

JMIR Res Protoc. 2025 Sep 12;14:e74981. doi: 10.2196/74981.

ABSTRACT

BACKGROUND: Poststroke cognitive impairment (PSCI) is a prevalent and disabling complication following stroke, affecting critical functions such as memory, attention, language, and executive abilities. Despite the growing clinical burden, standardized and effective treatment strategies for PSCI remain limited. Acupuncture, a key modality in traditional Chinese medicine, has shown promise in improving cognitive outcomes among survivors of stroke. However, the neural mechanisms underlying its efficacy are not well understood. The default mode network (DMN), a brain network implicated in cognition and memory, has been shown to exhibit altered functional and structural connectivity in patients with PSCI. Investigating whether acupuncture modulates DMN activity may provide critical insights into its therapeutic potential.

OBJECTIVE: This study aims to evaluate the efficacy of acupuncture in improving cognitive function in patients with PSCI and explore its potential neurobiological mechanisms, particularly those involving changes in the DMN, using multimodal neuroimaging techniques.

METHODS: We will conduct a single-blind, randomized controlled trial involving 54 eligible patients with PSCI who will be randomly assigned to either an acupuncture group or a sham acupuncture control group. Both groups will receive conventional rehabilitation therapies. The intervention group will undergo standardized scalp acupuncture targeting Baihui (GV20), Shenting (GV24), and Sishencong (EX-HN1) for 8 weeks. The control group will receive sham acupuncture at nonacupoint locations using placebo needles. Cognitive function will be assessed at baseline and 4 and 8 weeks using the Montreal Cognitive Assessment and Mini-Mental State Examination. Secondary outcomes include activities of daily living, quality of life, and neuroimaging data acquired through resting-state functional magnetic resonance imaging and diffusion tensor imaging.

RESULTS: This study is currently in the recruitment phase. All results, including clinical and imaging data, will be reported upon trial completion and publication.

CONCLUSIONS: This protocol is designed to investigate the efficacy of acupuncture and its underlying mechanisms in treating PSCI, with a particular focus on functional brain networks. By integrating clinical cognitive assessments and neuroimaging analysis of DMN connectivity, this study seeks to establish objective correlates of cognitive improvement. Findings from this research may advance the understanding of how acupuncture modulates large-scale brain networks and contribute to the development of imaging-based biomarkers for treatment evaluation. If successful, this approach could support the inclusion of acupuncture as a personalized nonpharmacological strategy in the neurorehabilitation of cognitive deficits following stroke.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/74981.

PMID:40939166 | DOI:10.2196/74981

PLoS Comput Biol. 2025 Sep 12;21(9):e1013485. doi: 10.1371/journal.pcbi.1013485. Online ahead of print.

ABSTRACT

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy (r = .78) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure.

PMID:40938947 | DOI:10.1371/journal.pcbi.1013485

Brain Connect. 2025 Sep 12. doi: 10.1177/21580014251376733. Online ahead of print.

ABSTRACT

Background: Time-resolved functional network connectivity (trFNC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFNC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchrony (PS), a phase-based technique. Methods: To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project with 827 subjects [repetition time (TR): 0.7 sec] and the Function Biomedical Informatics Research Network with 311 subjects (TR: 2 sec), which included 151 schizophrenia (SZ) patients and 160 controls. Results: Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, whereas PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (∼30 sec), but larger windows (∼88 sec) sacrifice clinically relevant information. Both methods identify a SZ-associated brain network state but show different patterns: SWPC highlights low anticorrelations between visual, subcortical, auditory, and sensory-motor networks, whereas PS shows reduced positive synchronization among these networks. Conclusion: In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

PMID:40938734 | DOI:10.1177/21580014251376733

Neuroradiology. 2025 Sep 12. doi: 10.1007/s00234-025-03756-1. Online ahead of print.

ABSTRACT

PURPOSE: Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), primarily affects the gastrointestinal tract but can also present with systemic manifestations, including those affecting the central nervous system (CNS). Resting-state functional MRI (rs-fMRI) provides insights into alterations in brain activity and connectivity. This review aims to evaluate rs-fMRI findings in IBD patients compared to healthy controls and to explore potential differences between CD and UC.

METHODS: A systematic search of PubMed/MEDLINE and SCOPUS identified rs-fMRI studies in neurologically asymptomatic IBD patients. Observational rs-fMRI studies assessing local neural activity and/or functional connectivity, were included.

RESULTS: Twenty-seven studies met eligibility criteria and findings were summarized descriptively based on rs-fMRI analysis technique, given the methodological variability. UC patients showed reduced neural activity in the hippocampus and altered functional connectivity in the visual and cerebellar networks, highlighting memory and motor control disruptions. CD patients exhibited increased neural activity in the anterior cingulate cortex and frontal regions, alongside altered connectivity in multiple sensory and higher-order cognitive networks. Both IBD types displayed disruptions in key networks, including the default mode, salience, and cerebellar networks, associated with emotional processing, pain perception and stress response regulation.

CONCLUSION: Despite shared rs-fMRI disruptions, UC is primarily associated with decreased neural activity in areas linked to memory and motor coordination, whereas CD exhibits increased activity in regions regulating emotion and cognition. Connectivity disruptions underscore the broader impact of IBD on brain function, emphasizing the role of the brain-gut axis in emotional and sensory impairments.

PMID:40938374 | DOI:10.1007/s00234-025-03756-1

Imaging Neurosci (Camb). 2025 Sep 9;3:IMAG.a.138. doi: 10.1162/IMAG.a.138. eCollection 2025.

ABSTRACT

The human brain is a large-scale network, containing multiple segregated, functionally specialized systems. With increasing age, these systems become less segregated, but the reasons and consequences of this age-related reorganization are largely unknown. Thus, after characterizing age- and sex-specific differences in the segregation of global, sensorimotor, and association systems using resting-state functional MRI data, we analyzed how segregation relates to cognitive performance in both classical and eye movement tasks across age strata and whether this is influenced by the degree of neuropathology. Our analyses included 6,455 participants (30-95 years) of the community-based Rhineland Study. System segregation indices were based on functional connectivity within and between 12 brain systems. We assessed cognitive performance with tests for memory, processing speed, executive function, and crystallized intelligence and oculomotor tasks. Multivariable regression models confirmed that brain systems become less segregated with age (e.g., global segregation: standardized regression coefficient (ß) = -0.298; 95% confidence interval [-0.299, -0.297], p < 0.001) and that in older age this effect is stronger in women compared to men. Higher segregation benefited memory (especially in young individuals) and processing speed in individuals with mild neuropathology (not significant after multiple testing correction). Lower segregation benefited crystallized intelligence in 46- to 55-year-olds. Associations between segregation indices and cognition were generally weak (ß ~ 0.01-0.06). This suggests that optimal brain organization may depend on the degree of brain pathology. Age-related brain reorganization could serve as a compensatory mechanism and partly explain improvements in crystallized intelligence and the decline in fluid cognitive domains from adolescence to (late) adulthood.

PMID:40937157 | PMC:PMC12421694 | DOI:10.1162/IMAG.a.138

Gastroenterol Hepatol Bed Bench. 2025;18(2):164-176. doi: 10.22037/ghfbb.v18i2.2987.

ABSTRACT

Functional gastrointestinal disorders (FGID) are prevalent illnesses associated with diminished quality of life and increased healthcare utilization. These conditions influence gut sensitivity, motility, microbiota, immunological function, and nervous processing in the brain. Chronic symptoms, including pain and dyspepsia, are exacerbated by maladaptive patient behaviors, stress, and co-morbidity. Studies of functional neuroimaging reveal increased brain responses in regions associated with gut sensory processing and salient cues, altered central regulation of endocrine and autonomic nerve responses, and aberrant connections in pain processing and the default mode network. This neuroimaging helps us understand the pathophysiology and outcomes of patients better. From the standpoint of brain connection, research in this area can further our understanding of the central pathophysiology of FGID and pave the way for the objective diagnosis and development of novel therapeutics for FGID. Prospective Neuroimaging research may change from brain mapping to clinical prognosis prediction due to technological advances in machine learning algorithms used in imaging. The usefulness and revelations of functional brain imaging are highlighted in this review, along with the areas that require development and, lastly, recommendations for future research.

PMID:40936788 | PMC:PMC12421933 | DOI:10.22037/ghfbb.v18i2.2987

Psychol Med. 2025 Sep 12;55:e268. doi: 10.1017/S0033291725100925.

ABSTRACT

BACKGROUND: Repetitive negative thinking (RNT) in major depressive disorder (MDD) involves a persistent focus on negative self-related experiences. Resting-state fMRI shows that the functional connectivity (FC) between the anterior insula and the superior temporal sulcus is associated with RNT intensity. This study examines how insular FC patterns differ between resting state and RNT induction in MDD and healthy control (HC) participants.

METHODS: Forty-one individuals with MDD and 28 HCs (total n = 69) underwent resting-state and RNT-induction fMRI scans. Seed-to-whole brain analysis using insular subregions as seeds was performed.

RESULTS: No diagnosis-by-run interaction effects were observed across insular subregions. MDD participants showed greater FC between the bilateral anterior, middle, and posterior insular regions and the cerebellum (z = 4.31-6.15). During RNT induction, both MDD and HC participants demonstrated increased FC between bilateral anterior/middle insula and prefrontal cortices, parietal lobes, posterior cingulate cortex (PCC), and medial temporal gyrus, encompassing the STS (z = 4.47-8.31). In exploratory correlation analyses, higher trait RNT was associated with increased FC between the right dorsal anterior/middle insula and the PCC, middle temporal gyrus, and orbital frontal gyrus in MDD participants (z = 4.31-6.15). Greater state RNT was linked to increased FC in similar insular regions, as well as the bilateral angular gyrus and right middle temporal gyrus (z = 4.47-8.31).

CONCLUSIONS: Hyperconnectivity in insula subregions during active rumination, especially involving the default mode network and salience network, supports theories of heightened self-focused and negative emotional processing in depression. These findings emphasize the neural basis of RNT when actively elicited in MDD.

PMID:40936343 | DOI:10.1017/S0033291725100925

Front Aging Neurosci. 2025 Aug 26;17:1640378. doi: 10.3389/fnagi.2025.1640378. eCollection 2025.

ABSTRACT

BACKGROUND: Current research is predominantly focused on the single dysfunction after stroke, but the potential changes in brain dynamics of post-stroke cognitive and motor dysfunction (PSCMD) remain unclear, which hinders a deep understanding of its rehabilitation effects. Therefore, the objective is to explore the dynamic brain network characteristics of PSCMD.

METHODS: The clinical features and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 75 patients with post-stroke motor dysfunction (PSMD), 33 patients with PSCMD, and 35 healthy controls (HCs). Hidden markov model (HMM) was employed for the rs-fMRI data, aiming to identify the repetitive states of brain activity while further assessing the temporal properties and activation patterns in PSCMD. Additionally, the correlation between the HMM state characteristics and clinical scale scores was systematically evaluated.

RESULTS: Five HMM states were ultimately identified. According to the results, PSMD and PSCMD groups showed significant changes in the dynamics of spatiotemporal attributes versus HCs, including fractional occupancy (FO), Lifetime (LT), and transition probability (TP). Furthermore, PSCMD patients exhibited greater FO than PSMD (p = 0.006) in state 3. State 3 was mainly characterized by low activation of sensorimotor and higher-order cognitive networks, as well as the high activation of the right prefrontal-parietal network, which may reflect adaptive changes in the brain after PSCMD. Besides, the FO of HMM state 3 exhibited a negative connection with the MoCa score (r = -0.389, p = 0.025).

CONCLUSION: An abnormal dynamic brain reorganization pattern could be observed in PSCMD patients. Neuromodulation strategies can be optimized by HMM-derived brain states in the future.

PMID:40933825 | PMC:PMC12417414 | DOI:10.3389/fnagi.2025.1640378

Psychoradiology. 2025 Aug 8;5:kkaf021. doi: 10.1093/psyrad/kkaf021. eCollection 2025.

ABSTRACT

BACKGROUND: Altered connectivity patterns in socio-emotional brain networks are characteristic of individuals with autism spectrum disorder. Despite recent research on intranasal oxytocin's modulation effects of network topology in autism, its specific effects on the functional connectivity network topology remain underexplored.

METHODS: To address this gap, we conducted an exploratory data-driven study employing a dimensional approach using data from a large cohort of 250 neurotypical adult male subjects with either high or low autistic traits and who had administered 24 IU of intranasal oxytocin or placebo in a randomized, controlled, double-blind design. Resting-state functional connectivity data were analyzed using network-based statistical methods and graph theoretical approaches.

RESULTS: The findings from treatment × autistic trait group interactions revealed significantly different effects of oxytocin in local (cluster coefficient, efficiency, nodal path length, degree and betweenness centrality) but not global graph metrics in individuals with higher autistic traits compared to those with lower ones, across multiple brain regions. Changes across multiple measures were found in the motor, auditory/language, visual, default mode and socio-emotional processing networks, all of which are influenced in autism spectrum disorder.

CONCLUSION: Overall, findings from this dimensional approach demonstrate that oxytocin particularly targets widespread enhancement of local but not global neural network processing parameters in neurotypical individuals with higher autistic traits. This suggests that intranasal oxytocin may represent a therapeutic option for social, emotional and sensorimotor symptoms in individuals with autism spectrum disorder by modulating local integration within brain regions involved in their regulation.

PMID:40933770 | PMC:PMC12418929 | DOI:10.1093/psyrad/kkaf021

Brain Commun. 2025 Sep 9;7(5):fcaf308. doi: 10.1093/braincomms/fcaf308. eCollection 2025.

ABSTRACT

Dopamine and serotonin are two major monoamine neurotransmitters associated with Parkinson's disease (PD), but their spatial distribution and relationship to underlying functional brain architecture are not fully understood. We assessed 30 patients with PD at baseline using structural MRI, resting-state functional MRI (rs-fMRI), 11C-PE2I and 11C-DASB PET, along with comprehensive clinical evaluations of motor and non-motor symptoms. Of these, 15 patients with PD who completed the same assessments after 19 months were included in the longitudinal analysis. rs-fMRI was used to assess functional connectivity, while 11C-PE2I and 11C-DASB PET were used to evaluate interregional homogeneity of dopamine and serotonin levels, referred to as PET covariance. Functional connectivity and PET covariance were estimated using a region-of-interest (ROI)-based approach with 138 ROIs from the Automated Anatomical Labelling 3 atlas, excluding cerebellar regions. These ROIs were further grouped into eight networks: visual, sensorimotor, attention, limbic, frontoparietal, default mode, subcortical and brainstem. At baseline, linear regression revealed that functional connectivity was positively associated with both 11C-PE2I PET covariance (β-values ranging from 0.575 to 0.790, P < 0.001) and 11C-DASB PET covariance (β-values ranging from 0.356 to 0.773, P < 0.001) across all networks. Longitudinally, we found positive correlations between baseline functional connectivity and both 11C-PE2I PET change covariance and 11C-DASB PET change covariance (β-values ranging from 0.166 to 0.576 and 0.312 to 0.671, respectively, P < 0.001) across all networks. These correlations remained significant after controlling for the Euclidean distance between ROIs, indicating that the association is independent of spatial proximity. For both tracers, absolute PET uptake across seed ROIs was positively associated with correspondent regression-derived functional connectivity-PET β-weights, which represent the relationship between PET uptake in target ROIs and their functional connectivity to the seed. This association between target functional connectivity and PET uptake was correlated with PD motor and non-motor severity across different brain regions in a manner that was dependent on the neurotransmitter system evaluated. Our findings suggest that in patients with PD, dopamine and serotonin levels covary among brain regions that are highly functionally connected. This implies that the spatial distribution of these neurotransmitters follows the organizational principles of the brain's functional connectomes, which are associated with features of the disease.

PMID:40933286 | PMC:PMC12418387 | DOI:10.1093/braincomms/fcaf308

Front Neurol. 2025 Aug 26;16:1603396. doi: 10.3389/fneur.2025.1603396. eCollection 2025.

ABSTRACT

BACKGROUND: Minimal hepatic encephalopathy (MHE) is a common cognitive impairment in patients with end-stage liver cirrhosis. However, the selection of sensitive biomarkers and the establishment of reliable diagnostic methods are currently challenging. We aimed to explore the abnormal spontaneous brain activity in patients with MHE and evaluate the clinical diagnostic value of four indicators for MHE using the support vector machine (SVM) method.

METHODS: A total of 45 MHE patients and 40 healthy controls were enrolled. Amplitude of low frequency fluctuation (ALFF), fractional amplitude of low frequency fluctuation (fALFF), percentage amplitude of low frequency fluctuation (PerAF), and regional homogeneity (ReHo) were used to evaluate local spontaneous brain activity. SVM analysis was used to construct the classification model and evaluate the diagnostic value.

RESULTS: Two-sample t-test and SVM analysis showed that, compared with the healthy control group, MHE patients had decreased ALFF values in the left angular gyrus, right inferior temporal gyrus, left postcentral gyrus, precentral gyrus, and right supplementary motor area. These regions indicated moderate classification efficacy (AUC = 0.75). Decreased ReHo metrics in the right anterior cingulate and paracingulate gyri also showed general discriminative power (AUC = 0.72). fALFF metrics, whether analyzed independently or combined with other indicators, exhibited limited classification performance (AUC < 0.70). Decreased PerAF metrics in the right superior parietal lobule, right dorsolateral prefrontal cortex, and right middle frontal gyrus achieved a good classification accuracy rate (AUC value 0.83; accuracy 81.18%; sensitivity 75.56%; specificity 87.50%), outperforming other functional metrics.

CONCLUSION: We found that decreased mean PerAF in the right supramarginal gyrus, right dorsolateral superior frontal gyrus, and right middle frontal gyrus may serve as potential neuroimaging indicators for early identification of cognitive impairment in MHE patients, providing critical evidence for clinical screening protocols.

PMID:40933050 | PMC:PMC12418831 | DOI:10.3389/fneur.2025.1603396

Biol Pharm Bull. 2025;48(9):1358-1374. doi: 10.1248/bpb.b25-00045.

ABSTRACT

Neuroimaging in rodents holds promise for advancing our understanding of the central nervous system (CNS) mechanisms that underlie chronic pain. Employing two established, but pathophysiologically distinct rodent models of chronic pain, the aim of the present study was to characterize chronic pain-related functional changes with resting-state functional magnetic resonance imaging (fMRI). In Experiment 1, we report findings from Lewis rats 3 weeks after Complete Freund's adjuvant (CFA) injection into the knee joint (n = 16) compared with the controls (n = 14). In Experiment 2, Sprague-Dawley rats were scanned 2 weeks after partial sciatic nerve ligation (PSNL) (n = 25) or sham surgery (n = 19). CFA and PSNL induced typical behavioral patterns consistent with inflammatory and neuropathic pain, respectively. Functional magnetic resonance imaging analyses comprised (1) independent component analysis (ICA) decompositions, (2) assessment of graph measures, (3) seed-based functional connectivities, and (4) predictions of chronic pain based on supervised machine learning. In both models, we detected changes in default mode network (DMN) activity. Local and global graph measures were generally similar across groups. However, regardless of the pain model, we observed a significant reduction in the betweenness centrality hub disruption index (HDI) in chronic pain compared with the controls. Finally, employing supervised machine learning in combination with a deep learning approach, chronic pain became predictable based on the functional connectivity patterns. The results indicate changes in DMN activity and betweenness centrality HDI in chronic pain. The predictability of chronic pain using machine learning points to an information content in the connectivity patterns that has not yet been captured in conventional network analyses.

PMID:40930793 | DOI:10.1248/bpb.b25-00045

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Sep 8:111486. doi: 10.1016/j.pnpbp.2025.111486. Online ahead of print.

ABSTRACT

BACKGROUND: Sexual dimorphism in human brain has garnered significant attention in neuroscience research. Although multiple investigations have examined sexual dimorphism in gray matter (GM) functional connectivity (FC), the research of white matter (WM) FC remains relatively limited.

METHODS: Utilizing resting-state fMRI data from 569 healthy young adults, we investigated sexual dimorphism in the WM functional connectome. We constructed both WM-WM and GM-WM FC networks and subsequently analyzed their FC strength, functional connectivity density, and network topological properties. Based on identified dimorphic features, a radial basis function support vector machine model was employed for sex prediction and classification. Validation analyses confirmed the reproducibility of our findings.

RESULTS: Our analyses revealed significant sexual dimorphism in FC within both the WM-WM and GM-WM networks. Notably, females generally exhibited stronger connection strengths across numerous pathways compared to males. Topologically, females displayed greater global system aggregation (higher clustering coefficient) in the WM-WM network. Similarly, within the GM-WM connectome, females showed enhanced network integration, specifically higher global and local efficiency in the frontoparietal network and increased clustering coefficient in the attention network. Critically, these dimorphic WM features proved effective for sex classification using machine learning; an integrated model combining WM-WM and GM-WM FCs achieved superior predictive performance over models using individual feature sets, highlighting the unique information encoded in WM functional dynamics.

CONCLUSION: This finding extends our understanding of brain sex differences beyond gray matter and provides novel insights into the neurological mechanisms potentially underlying sex-specific patterns in cognition, behavior, and susceptibility to brain disorders.

PMID:40930485 | DOI:10.1016/j.pnpbp.2025.111486

Neurobiol Dis. 2025 Sep 8:107089. doi: 10.1016/j.nbd.2025.107089. Online ahead of print.

ABSTRACT

Temporal lobe epilepsy (TLE) patients experience shifts between non-seizing and seizing brain states, but the structural networks underlying these transitions remain undefined and poorly characterized. We detected dynamic brain states in resting-state fMRI and constructed linked structural networks utilizing multi-shell diffusion-weighted MR data. Leveraging network control theory, we interrogated the structural data for all possible brain state transitions, identifying those requiring abnormal levels of transition energy (low or high) in TLE compared to matched healthy participants (n's = 25). Results revealed three transitions requiring significantly higher energy in TLE; no abnormally low-energy transitions were observed. In HPs, transitions relied on mediator regions that did not belong to the initial source or final target brain areas. TLE transitions involved a more restricted set of source/target regions, predominantly outside the epileptogenic temporal lobe. Our findings highlight the abnormal and inefficient network mechanisms that accrue from the network entrainment inherent to TLE seizure activity. We argue these findings clarify the pathologic effects and help explain the well-known cognitive inefficiencies and other deficits found in the TLE disorder.

PMID:40930431 | DOI:10.1016/j.nbd.2025.107089

Diabetes Obes Metab. 2025 Sep 10. doi: 10.1111/dom.70109. Online ahead of print.

ABSTRACT

AIMS: Obesity is associated with increased insulin-stimulated brain glucose uptake (BGU) which is opposite to decreased GU observed in peripheral tissues. Increased BGU was shown to be reversed by weight loss and exercise training, but the mechanisms remain unknown. We investigated whether neuroinflammation (TSPO availability) and brain activity drive the obesity-associated increase in BGU and whether this increase is reversed by exercise training.

MATERIALS AND METHODS: Twelve monozygotic twin pairs mean age 40.4 (SD) years discordant for BMI (leaner mean 29.1 (SD) 6.3, heavier 36.7 (SD) 7.0 kg·m-2) performed 6-month long exercise intervention. Insulin-stimulated BGU during euglycaemic-hyperinsulinaemic clamp, brain inflammation (translocator protein (TSPO) availability) and brain resting state activity were studied by [18F]FDG-PET, [11C]PK11195-PET, and fMRI, respectively. Cognitive function was assessed by an online survey.

RESULTS: Exercise training had no effect on insulin-stimulated BGU, brain neuroinflammation (TSPO availability), or BMI. Exercise improved VO2peak, whole-body insulin sensitivity, and cognitive function similarly in both groups (all, p <0.05) as well as decreased resting state brain activity in heavier co-twins (p <0.05). At baseline, heavier co-twins had worse whole-body insulin sensitivity (p <0.01), increased BGU in the parietal cortex and caudatus, as well as increased resting state brain activity (both, p <0.05) and no difference in cognitive function. Leaner co-twins had higher TSPO availability in white matter and the hippocampus (p <0.05).

CONCLUSIONS: Exercise training had no effect on insulin-stimulated BGU or neuroinflammation (TSPO availability) but it reversed increased resting state brain activity in heavier co-twins. At baseline, obesity was associated with increased insulin-stimulated BGU and resting state brain activity, independent of genetics.

PMID:40926735 | DOI:10.1111/dom.70109

Comput Med Imaging Graph. 2025 Aug 28;125:102632. doi: 10.1016/j.compmedimag.2025.102632. Online ahead of print.

ABSTRACT

Bipolar disorder (BD) is a debilitating mental illness characterized by significant mood swings, posing a substantial challenge for accurate diagnosis due to its clinical complexity. This paper presents CS2former, a novel approach leveraging a dual channel-spatial feature extraction module within a Transformer model to diagnose BD from resting-state functional MRI (Rs-fMRI) and T1-weighted MRI (T1w-MRI) data. CS2former employs a Channel-2D Spatial Feature Aggregation Module to decouple channel and spatial information from Rs-fMRI, while a Channel-3D Spatial Attention Module with Synchronized Attention Module (SAM) concurrently computes attention for T1w-MRI feature maps. This dual extraction strategy is coupled with a Transformer, enhancing feature integration across modalities. Our experimental results on two datasets, including the OpenfMRI and our collected datasets, demonstrate CS2former's superior performance. Notably, the model achieves a 10.8% higher Balanced Accuracy on our dataset and a 5.7% improvement on the OpenfMRI dataset compared to the baseline models. These results underscore CS2former's innovation in multimodal feature fusion and its potential to elevate the efficiency and accuracy of BD diagnosis.

PMID:40926451 | DOI:10.1016/j.compmedimag.2025.102632

Am J Audiol. 2025 Sep 9:1-17. doi: 10.1044/2025_AJA-25-00025. Online ahead of print.

ABSTRACT

PURPOSE: This study investigated the effects of age-related hearing decline on functional networks using resting-state functional magnetic resonance imaging (rs-fMRI). The main objective of the present study was to examine resting-state functional connectivity (RSFC) and graph theory-based network efficiency metrics in 49 adults categorized by age and hearing thresholds to identify the neural mechanisms of age-related hearing decline.

METHOD: Forty-nine adults with self-reported normal hearing underwent pure-tone audiometry and rs-fMRI. RSFC within key brain networks and auditory-related brain regions, including the default mode network, salience network, dorsal attention network, and primary auditory cortices, was assessed using region-of-interest-based and graph theory approaches. Functional metrics, such as RSFC strength and global and local efficiency, were compared across age groups (middle age vs. older age) and hearing profile groups (negative screening vs. positive screening and negative high-frequency [HF] screening vs. positive HF screening).

RESULTS: Older adults demonstrated significantly weaker RSFC between the left primary auditory cortex and the left rostrolateral prefrontal cortex/anterior cingulate cortex within the salience network than middle-aged adults. Participants without age-related hearing decline exhibited weaker internetwork connectivity within the dorsal attention network and bilateral auditory regions, highlighting the impact of hearing sensitivity on network functionality. Graph theory metrics indicated greater local efficiency in nodes within the salience network among individuals without age-related hearing decline, suggesting preserved cognitive control and attentional processing.

CONCLUSIONS: Age and hearing thresholds significantly affected the functional connectivity and network efficiency of the brain. These results emphasize the importance of neuroimaging techniques like rs-fMRI in studying neural mechanisms of age-related hearing loss.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29945021.

PMID:40924510 | DOI:10.1044/2025_AJA-25-00025

JAACAP Open. 2024 Sep 26;3(3):725-735. doi: 10.1016/j.jaacop.2024.09.003. eCollection 2025 Sep.

ABSTRACT

OBJECTIVE: Despite rapid advancements in understanding of cognitive disengagement syndrome (CDS) in children, less is known about the neural correlates of CDS. The aim of this study was to examine associations between CDS symptom severity and connectivity within and between specific brain networks.

METHOD: The study recruited 65 right-handed children (ages 8-13 years; 36 boys) with the full continuum of CDS symptom severity from the community. As part of a cross-sectional descriptive study investigating CDS, children underwent 10-minute resting-state functional magnetic resonance imaging. Connectivity metrics were extracted from the default mode network and ventral and dorsal attention networks. Parents and teachers completed measures of CDS and attention-deficit/hyperactivity disorder inattention symptoms. Multivariate parametric cluster analyses were performed on within- and between-network connections of the specified networks, with age and sex included as covariates. Separate models were conducted with and without controlling for attention-deficit/hyperactivity disorder inattention symptom severity.

RESULTS: Parent-rated CDS symptom severity was not significantly associated with any between- or within-network associations of interest. When attention-deficit/hyperactivity disorder inattention symptom severity was included in the model, teacher-rated CDS symptom severity was associated with greater functional connectivity between several regions of the default mode network and ventral attention network.

CONCLUSION: This study builds on theoretical and empirical evidence suggesting atypical connectivity between task-positive and task-negative networks as potentially key for understanding the neural correlates of CDS. These findings are important for building a neuroscience-based understanding of CDS and support emerging theoretical models linking CDS to mind wandering as well as DMN-related dysfunction.

PMID:40922795 | PMC:PMC12414302 | DOI:10.1016/j.jaacop.2024.09.003

JAACAP Open. 2024 Sep 27;3(3):758-767. doi: 10.1016/j.jaacop.2024.09.008. eCollection 2025 Sep.

ABSTRACT

OBJECTIVE: Psychological distress (eg, anxiety and depression) during pregnancy can disrupt fetal brain development and negatively affect infant behavior. Prenatal distress rose substantially during the COVID-19 pandemic according to most, but not all, studies, raising concerns about its potential effects on brain connectivity and behavior in infants.

METHOD: We investigated 63 mother-infant pairs as part of the Pregnancy during the COVID-19 Pandemic study. Mothers reported depression and anxiety symptoms prospectively during pregnancy; these were combined into one measure of prenatal maternal distress. Infant brain resting state functional magnetic resonance imaging (rs-fMRI) scans were obtained at 3 months of age, and mothers assessed infant behavior at 6 and 12 months using the Infant Behavior Questionnaire-Revised (IBQ-R) and the Ages and Stages Questionnaire (ASQ-3), respectively. The rs-fMRI was processed to measure functional connectivity within auditory, left frontoparietal, and default mode networks, and connectivity was tested for relationships to prenatal maternal distress. Prenatal distress and brain connectivity were also tested for relationships with infant behavior.

RESULTS: Higher prenatal maternal distress was related to stronger functional connectivity in the infant auditory network (T = 2.5, p = 0.01, q = 0.04, df = 59) and higher infant ASQ-3 personal-social scores (T = 2.9, p = 0.006, q = 0.03, df = 48). No significant associations were found between brain connectivity and infant behavior.

CONCLUSION: The impact of exposure to maternal prenatal distress on infant brain networks may be more apparent in networks that develop early, such as the auditory network, compared to later-developing networks, the effects of which may emerge later in childhood. The link between prenatal maternal distress and higher infant behavior scores may suggest compensatory changes, although further study is needed to determine how behavior manifests in the longer term.

DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. We actively worked to promote sex and gender balance in our author group.

PMID:40922793 | PMC:PMC12414312 | DOI:10.1016/j.jaacop.2024.09.008

J Affect Disord. 2025 Sep 6:120230. doi: 10.1016/j.jad.2025.120230. Online ahead of print.

ABSTRACT

Major Depressive Disorder (MDD) poses a significant global health threat, impairing individual functioning and increasing socioeconomic burden. Accurate diagnosis is crucial for improving treatment outcomes. This study proposes Time-Frequency Text-Attributed DeepWalk (TF-TADW), a framework for MDD classification using resting-state functional MRI data. TF-TADW integrates time-frequency dynamics and brain network topology. A key aspect is the adaptive weighting of time-frequency features via an attention mechanism, enabling personalized representation learning to address MDD heterogeneity and mitigate site-specific biases in multi-site datasets. Matrix factorization simultaneously learns network topology and node attributes, creating a comprehensive brain network embedding. Evaluated on REST-meta-MDD and SRPBS-MDD, TF-TADW achieved accuracies of 80.13% and 91.97%, respectively. The attention mechanism also identified key MDD-related brain regions, enhancing interpretability. These results demonstrate TF-TADW's effectiveness and potential for clinical application.

PMID:40921213 | DOI:10.1016/j.jad.2025.120230