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Life (Basel). 2025 Aug 25;15(9):1342. doi: 10.3390/life15091342.

ABSTRACT

Long-term physical training is known to induce brain plasticity, yet how these neural adaptations evolve across different stages of training remains underexplored. This two-year longitudinal study investigated the stage-dependent effects of endurance running on brain structure and resting-state function in healthy college students. Thirty participants were recruited into three groups based on their endurance training level: high-level runners, moderate-level runners, and sedentary controls. All participants underwent baseline and two-year follow-up MRI scans, including T1-weighted structural imaging and resting-state fMRI. The results revealed that the high-level runners exhibited a significant increase in degree centrality (DC) in the left dorsolateral prefrontal cortex (DLPFC). In the moderate-level group, more widespread changes were observed, including increased gray matter volume (GMV) in bilateral prefrontal cortices, medial frontal regions, the right insula, the right putamen, and the right temporo-parieto-occipital junction, along with decreased GMV in the posterior cerebellum. Additionally, DC decreased in the left thalamus and increased in the right temporal lobe and bilateral DLPFC; the fractional amplitude of low-frequency fluctuations (fALFF) in the right precentral gyrus was also elevated. These brain regions are involved in executive control, sensorimotor integration, and motor coordination, which may suggest potential functional implications for cognitive and motor performance; however, such interpretations should be viewed cautiously given the modest sample size and study duration. No significant changes were found in the control group. These findings demonstrate that long-term endurance training induces distinct patterns of brain plasticity at different training stages, with more prominent and widespread changes occurring during earlier phases of training.

PMID:41010285 | PMC:PMC12471654 | DOI:10.3390/life15091342

Brain Sci. 2025 Sep 4;15(9):964. doi: 10.3390/brainsci15090964.

ABSTRACT

Background/Objectives: Neural compensation, in which healthy brain regions take over functions lost due to lesions, is a potential biomarker for functional recovery after stroke. However, previous neuroimaging studies often speculated on neural compensation simply based on greater measures in patients (compared to healthy controls) without demonstrating a more direct link between these measures and the functional recovery. Because taking over the function of a lesion region means taking on a similar role as that lesion region in its functional network, the present study attempted to explore neural compensation based on the similarity of functional connectivity (FC) patterns between a healthy regions and lesion regions. Methods: Seventeen stroke patients (13M4F, 63.2 ± 9.1 y.o.) underwent three resting-state functional MRI (rs-fMRI) sessions during rehabilitation. FC patterns of their lesion regions were derived by lesion network analysis; and these patterns were correlated with healthy FC patterns derived from each brain voxel of 51 healthy subjects (32M19F, 61.0 ± 14.3 y.o.) for the assessment of pattern similarity. Results: We identified five healthy regions showing decreasing FC similarity (29-54%, all corrected p < 0.05, effect size η2: 0.10-0.20) to the lesion network over time. These decreasing similarities were associated with increasing behavioral scores on activities of daily living (ADL, p < 0.001, η2 = 0.90), suggesting greater neural compensation at early-stage post-stroke and reduced compensation toward the end of effective rehabilitation. Conclusions: Besides direct FC measures, the present results propose an alternative biomarker of neural compensation in functional recovery from stroke. For sensorimotor recoveries like ADL, this biomarker could be more sensitive than direct measures of lesion connectivity in the motor network.

PMID:41008324 | PMC:PMC12468017 | DOI:10.3390/brainsci15090964

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Sep 11:111498. doi: 10.1016/j.pnpbp.2025.111498. Online ahead of print.

ABSTRACT

BACKGROUND: Pathologic schizophrenia processes originate early in brain development, leading to detectable brain alterations via structural and functional magnetic resonance imaging (MRI). Recent MRI studies have sought to characterize disease effects from a brain age perspective, but developmental deviations from the typical brain age trajectory in youths with schizophrenia remain unestablished. This study investigated brain development deviations in early-onset schizophrenia (EOS) patients by applying machine learning algorithms to structural and functional MRI data.

METHODS: Multimodal MRI data, including T1-weighted MRI (T1w-MRI), diffusion MRI, and resting-state functional MRI (rs-fMRI) data, were collected from 80 antipsychotic-naive first-episode EOS patients and 91 typically developing (TD) controls. The morphometric similarity connectome (MSC), structural connectome (SC), and functional connectome (FC) were separately constructed by using these three modalities. According to these connectivity features, eight brain age estimation models were first trained with the TD group, the best of which was then used to predict brain ages in patients. Individual brain age gaps were assessed as brain ages minus chronological ages.

RESULTS: Both the SC and MSC features performed well in brain age estimation, whereas the FC features did not. Compared with the TD controls, the EOS patients showed increased absolute brain age gaps when using the SC or MSC features, with opposite trends between childhood and adolescence. These increased brain age gaps for EOS patients were positively correlated with the severity of their clinical symptoms.

CONCLUSION: These findings from a multimodal brain age perspective suggest that advanced brain age gaps exist early in youths with schizophrenia.

PMID:40945816 | DOI:10.1016/j.pnpbp.2025.111498

Drug Alcohol Depend. 2025 Sep 3;276:112863. doi: 10.1016/j.drugalcdep.2025.112863. Online ahead of print.

ABSTRACT

BACKGROUND: Opioid use disorder (OUD) is associated with altered brain network connectivity, particularly in the fronto-parietal, default mode, and salience networks. Brain efficiency is maximized when networks are distinct ('segregated') yet maintain partial connectivity with other networks ('integrated'). 'Brain network segregation' quantifies this balance by comparing the functional connectivity of nodes within and between networks. Previous research found lower brain network segregation in people with cognitive impairment, alcohol use disorder, and older age. We hypothesized that recent drug use severity in people with OUD would relate to reduced brain network segregation.

METHOD: Forty treatment-seeking adults with OUD (72.5 % male) completed resting-state functional magnetic resonance imaging. We grouped 264 brain regions into 10 networks, categorized as "association" (higher-order cognition) or "sensorimotor" (sensory and motor) networks. Regression analysis tested the relation between drug use severity and brain network segregation of association and sensorimotor categories and specific networks. Partial correlations explored the effects of cognition (IQ and working memory), mood, and affect.

RESULTS: Drug use severity predicted lower brain network segregation of the association networks, particularly the fronto-parietal and salience networks, but not the default mode network. The relation between drug use severity and lower segregation of the sensorimotor networks depended on age. In exploratory analyses, positive affect related to greater salience network segregation.

CONCLUSIONS: An altered balance of connectivity within and between brain networks may correspond with drug use severity, particularly in cognitive and salience-detection networks. Lower brain network segregation may indicate accelerated brain aging and be a target for OUD treatment.

PMID:40945409 | DOI:10.1016/j.drugalcdep.2025.112863

Int J Mol Sci. 2025 Aug 28;26(17):8379. doi: 10.3390/ijms26178379.

ABSTRACT

Occupational burnout is ubiquitous yet still debated as a disease entity. Previous reviews surveyed multiple biomarkers but left their neural substrate unclear. We therefore asked: What, if any, reproducible magnetic-resonance signature characterises burnout? Following PRISMA principles adapted for mechanistic synthesis, two reviewers searched PubMed, Scopus, Google Scholar, ResearchGate and Cochrane from January 2000 to May 2025 using "MRI/fMRI" AND "burnout". After duplicate removal and multi-stage screening, 17 clinical studies met predefined inclusion criteria (English language, MRI outcomes, validated burnout diagnosis). In total, ≈1365 participants were scanned, 880 with clinically significant burnout and 470 controls. Uniform Maslach Burnout Inventory thresholds defined cases; most studies matched age and sex, and all excluded primary neurological disease. Structural morphometry (8/17 studies) revealed consistent amygdala enlargement-predominantly in women-and grey-matter loss in dorsolateral/ventromedial prefrontal cortex and striatal caudate-putamen, while hippocampal volume remained unaffected, distinguishing burnout from PTSD or depression. Resting-state and task fMRI (9/17 studies) showed fronto-cortical hyper-activation, weakened amygdala-ACC coupling, and progressive fragmentation of rich-club networks, collectively indicating compensatory executive overdrive and global inefficiency. Two longitudinal cohorts and several intervention sub-studies demonstrated partial reversal of cortical thinning and limbic hyper-reactivity after mindfulness, exercise, cognitive-behavioural therapy, neurofeedback, or rTMS, underscoring plasticity. Across heterogeneous paradigms and populations, MRI converges on a coherent, sex-modulated but reversible brain-networkopathy that satisfies objective disease criteria. These findings justify early neuro-imaging-based triage, circuit-targeted therapy, and formal nosological recognition of burnout as a mental disorder, with policy ramifications for occupational health and insurance parity.

PMID:40943301 | DOI:10.3390/ijms26178379

Int J Mol Sci. 2025 Aug 25;26(17):8230. doi: 10.3390/ijms26178230.

ABSTRACT

Borderline Personality Disorder (BPD) is marked by emotional dysregulation, instability in self-image and relationships, and high impulsivity. While functional magnetic resonance imaging (fMRI) studies have provided valuable insights into the disorder's neural correlates, electroencephalography (EEG) may capture real-time brain activity changes relevant to BPD's rapid emotional shifts. This review summarizes findings from studies investigating resting state and task-based EEG in individuals with BPD, highlighting common neurophysiological markers and their clinical implications. A targeted literature search (1980-2025) was conducted across databases, including PubMed, Google Scholar, and Cochrane. The search terms combined "EEG" or "electroencephalography" with "borderline personality disorder" or "BPD". Clinical trials and case reports published in English were included if they recorded and analyzed EEG activity in BPD. A total of 24 studies met the inclusion criteria. Findings indicate that individuals with BPD often show patterns consistent with chronic hyperarousal (e.g., reduced alpha power and increased slow-wave activity) and difficulties shifting between vigilance states. Studies examining frontal EEG asymmetry reported varying results-some linked left-frontal activity to heightened hostility, while others found correlations between right-frontal shifts and dissociation. Childhood trauma, mentalization deficits, and dissociative symptoms were frequently predicted or correlated with EEG anomalies, underscoring the impact of adverse experiences on neural regulation-however, substantial heterogeneity in methods, small sample sizes, and comorbid conditions limited study comparability. Overall, EEG research supports the notion of altered arousal and emotion regulation circuits in BPD. While no single EEG marker uniformly defines the disorder, patterns such as reduced alpha power, increased theta/delta activity, and shifting frontal asymmetries converge with core BPD features of emotional lability and interpersonal hypersensitivity. More extensive, standardized, and multimodal investigations are needed to establish more reliable EEG biomarkers and elucidate how early trauma and dissociation shape BPD's neurophysiological profile.

PMID:40943155 | DOI:10.3390/ijms26178230

Neurophysiol Clin. 2025 Sep 11;55(5):103102. doi: 10.1016/j.neucli.2025.103102. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess changes in brain functional connectivity associated with the painful nature of peripheral polyneuropathy.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 26 patients with painful or painless polyneuropathy. According to previously published results, connectivity was studied regarding the default mode network (DMN), intrathalamic and thalamocortical connections, and, the different brain networks (pain matrices) involved in the "nociceptive", "attentional" and "emotional" aspects of the chronic pain experience.

RESULTS: No change in DMN connectivity was found between groups. Thalamocortical connectivity was reduced in patients with painful polyneuropathy, especially for the thalamic cluster connected to the motor cortex, while intra-thalamic (mediolateral) connectivity was increased in patients with painless polyneuropathy. Intra-connectivity was increased within the pain matrices, especially the "nociceptive" matrix, in patients with painful polyneuropathy, while inter-connectivity was increased between the "attentional" and "emotional" pain matrices in patients with painless polyneuropathy. Increased connectivity between the posterior insula and parietal operculum positively correlated with the neuropathic pain symptom score and impact of pain on daily functioning.

CONCLUSIONS: Painful polyneuropathy was characterized by increased intra-connectivity within each pain matrix and reduced thalamocortical connectivity of certain thalamic clusters, notably linked to the motor cortex. Conversely, painless polyneuropathy was characterized by increased connectivity within the thalamus and between the different pain matrices. Although various methodological limitations must be acknowledged (small sample size, lack of a control group of healthy subjects or measurement of pain intensity during neuroimaging examination), these results provide new information on the changes in brain connectivity associated with painful polyneuropathies. This study also brings new arguments to explain the efficacy of motor cortex stimulation in the treatment of chronic neuropathic pain.

PMID:40939478 | DOI:10.1016/j.neucli.2025.103102

EBioMedicine. 2025 Sep 11;120:105917. doi: 10.1016/j.ebiom.2025.105917. Online ahead of print.

ABSTRACT

BACKGROUND: The hierarchical organisation of functional networks is crucial for integration and segregation, and its dysregulation is implicated in neurodegenerative progression. The basal ganglia (BG) is the communication hub of the BG-thalamo-cortical circuit, and its dysfunction drives the progression of clinical symptoms in Parkinson's disease (PD). However, network-level functional reorganisation of BG disruption remains unclear.

METHODS: In this cross-sectional study, we applied functional gradient analysis based on a diffusion map embedding algorithm to delineate macroscale functional architectures of BG in 102 PD patients and 88 healthy controls (HCs). Partial least squares correlation analysis was employed to investigate the relationship between gradient alterations and clinical symptoms in PD.

FINDINGS: The first functional gradient of BG extended from caudate nucleus to putamen, whereas second gradient was anchored at dorsal and ventral caudate nucleus. In PD patients, the first gradient showed significant global compression with the progression from unilateral to bilateral motor symptoms. The second gradient exhibited local disruptions in nucleus accumbens and putamen, driven by reduced connectivity with multiple functional systems. Gradient scores of these two nuclei displayed significant lateralisation in PD, regardless of motor deficit dominance. Dysfunctional gradients in PD are associated with motor deficits and emotional symptoms.

INTERPRETATION: Our findings reveal that functional gradient reorganisation is a network-level signature of early-stage PD, linking functional reorganisation in BG circuit to clinical symptoms. The hemispheric asymmetry of gradient patterns may inform ongoing research on PD lateralisation.

FUNDING: National Natural Science Foundation of China (Nos. 82071423 and 82021004) and Beijing Natural Science Foundation (No. JQ23033).

PMID:40939293 | DOI:10.1016/j.ebiom.2025.105917

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