Search Results (53)

Objective: Depressive symptoms exist on a continuum in the general population, yet the underlying neurobiological mechanisms, particularly the interplay between resting-state networks and task-evoked social cognitive responses, remain elusive. Methods: Leveraging the Human Connectome Project (HCP) dataset, we included 867 participants. With depression scores as the independent variable and age/sex as covariates, we systematically examined associations with sleep quality, negative emotions, sensory scores, gray matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), multi-seed resting-state functional connectivity (rsFC), as well as brain activation and behavioral performance during working memory, emotion recognition, social cognition, relational reasoning, language comprehension, and gambling tasks. The statistical threshold was set at voxel-level p < 0.001 (uncorrected) combined with cluster-level FWE correction at p < 0.05. Results: (1) Depression scores were positively correlated with sleep disturbances, negative emotions (anger/fear), and pain. (2) In resting-state, depression scores negatively correlated with ventral striatum (VS)–cerebellum/parahippocampal gyrus/fusiform rsFC, yet positively correlated with pregenual anterior cingulate cortex (preACC)–supplementary motor area (SMA) rsFC. (3) In task-fMRI, only the social task showed a positive association with task accuracy and regional activation in bilateral pre/postcentral gyri, superior temporal gyri, left middle frontal gyrus, and SMA/paracentral lobule. Conclusions: Elevated depression scores are linked to a pattern that may reflect relative decoupling between reward and perceptual systems, along with enhanced connectivity in cognitive control circuits. Socially, high scorers exhibit a pattern suggestive of compensatory hypervigilance, accompanied by enhanced behavioral performance. This study provides multidimensional evidence for the dimensional neural representation of depressive symptoms. Full article

Objectives: To explore the brain imaging correlates of the association between chess skill level and visual spatial working memory (VSWM) in school-aged children. Methods: This study analyzed a final sample of 20 school-aged children (aged 10–15 years, mean age of 13.48 ± 1.23 years; including 9 experts and 11 novices) selected from an initial pool of 40 recruited participants from primary and secondary schools and chess clubs in Beijing. They were divided into an experimental group (expert-level school-aged children from a Beijing chess club) and a control group (novice-level children from a Beijing primary and secondary school who had not systematically participated in chess courses but understood basic chess knowledge). The N-back task was used to assess behavioral differences in VSWM. Voxel-based morphometry (VBM) and surface-based morphometry (SBM) techniques were employed to analyze differences in gray matter volume and cortical thickness in the brain structure of expert- and novice-level children. Differences in resting-state functional imaging were also analyzed. Results: The results showed the following: (1) The expert-level children demonstrated significantly higher accuracy and shorter reaction times in VSWM tasks compared to novices. (2) Differences in gray matter volume and cortical thickness were observed between the expert and novice groups. (3) Expert-level children showed significantly higher resting-state ALFF/fALFF values in the dorsolateral superior frontal gyrus, cingulate gyrus, and orbitofrontal cortex compared to novices. Conclusions: In conclusion, expert-level school-aged children exhibit distinctive regional structural and intrinsic functional profiles. These non-causal neural variations co-vary with selective cognitive baselines characterizing specialized low-load visual spatial processing rather than a generalized working memory expansion, reflecting brain signatures associated with varying tiers of chess expertise. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

Introduction: Transcranial photobiomodulation (t-PBM) is a non-invasive metabolic neuromodulation technique intended to enhance cerebral bioenergetics by stimulating mitochondrial activity. To characterize both baseline metabolic vulnerability and real-time metabolic engagement during stimulation, this preliminary study integrated phosphorus magnetic resonance spectroscopy (³¹P-MRS) with resting-state fMRI. Methods: Eleven individuals with mild cognitive impairment (MCI) or early Alzheimer’s disease underwent ³¹P-MRS to quantify baseline cerebral metabolism (PCr/Pi, pH), followed by MRI sessions during which t-PBM was applied over bilateral frontal sites. Fractional amplitude of low-frequency fluctuations (fALFF), a resting-state index strongly associated with cerebral glucose metabolism, was used as a real-time proxy of metabolic change during stimulation. Results: Linear regression analyses indicated that lower baseline PCr/Pi and lower pH, markers of impaired oxidative metabolism, predicted greater increases in fALFF during t-PBM, most prominently in the right frontal pole (FP2) and, to a lesser extent, right dorsolateral prefrontal cortex (F4). While greater dementia severity also predicted larger fALFF responses in select regions, our findings suggest that t-PBM can boost metabolism in some brain regions where it is compromised, but that this may be independent of cognitive function in early AD/MCI. These findings suggest that t-PBM may preferentially engage brain regions with reduced metabolic capacity to exhibit stronger acute responses. Discussion: Overall, these hypothesis-generating results support the combined use of ³¹P-MRS and fALFF as complementary biomarkers to quantify baseline metabolic status and real-time target engagement. A single session of t-PBM produced neural activity changes consistent with partial metabolic normalization in vulnerable cortical regions. As these results are preliminary, ongoing longitudinal work with a larger cohort will determine whether baseline metabolic profiles and acute fALFF responses predict clinical outcomes after repeated t-PBM treatment. Full article

Background: Lumbar disc herniation (LDH) is the most common etiological cause of low back pain (LBP). Objective and precise pain evaluation is of significant clinical value. Functional near-infrared spectroscopy (fNIRS) as a noninvasive neuroimaging modality, has been increasingly validated to reflect subjective pain perception through hemodynamic correlates. This study aimed to analyze the fNIRS changes in patients with LDH about to receive Unilateral Biportal Endoscopy and to further explore the feasibility of fNIRS as an objective biomarkers for clinical assessment of LDH. Methods: Resting-state fNIRS data were acquired from 67 preoperative LDH patients and 20 healthy controls (HC). Brain functional maps—including z-standardized fractional amplitude of low-frequency fluctuations (zfALFF) and seed-based functional connectivity (FC)—were extracted and quantified. Group-level comparisons were performed between LDH and HC groups across four predefined regions of interest; additionally, correlation analyses were conducted between fNIRS metrics and clinical assessment scores within the LDH cohort. Results: Compared with HC, LDH patients exhibited significantly altered zfALFF in the medial prefrontal cortex (mPFC): decreased amplitude at channel CH12 (t = −2.031, p = 0.045) and increased amplitude at CH21 (t = 2.462, p = 0.016). Whole-brain FC analysis further revealed widespread changes—particularly between the parietal somatosensory cortex and prefrontal regions. Among all tested FC–clinical indicator associations, 56 reached statistical significance after FDR correction (q < 0.05). VAS_ lumbar and SF-36_SF exhibited the highest number of significant connections. Conclusions: LDH patients with LBP exhibit notable alterations in prefrontal resting-state ALFF and FC between the parietal somatosensory cortex and prefrontal cortex relative to HC. Importantly, these neural alterations exhibit significant associations with both pain severity (VAS) and long-term health-related quality of life (SF-36), thereby strengthening their candidacy as neural correlates meriting prospective validation as objective, mechanism-informed biomarkers for clinical evaluation of lumbar disc herniation (LDH). Moreover, these findings highlight candidate neural targets for future longitudinal studies investigating early prognostic prediction and treatment response monitoring in LDH. Full article

Background: Frequent nocturnal arousals are a core feature of comorbid insomnia and obstructive sleep apnea (COMISA), yet the underlying central mechanisms remain unclear. Identifying brain functional correlates of nocturnal awakenings may help clarify arousal-related mechanisms and inform potential interventional targets. Methods: A total of 99 participants (COMISA, insomnia alone, OSA alone, and healthy controls) underwent clinical assessments, polysomnography, and brain magnetic resonance imaging (MRI). MRI metrics were compared across groups, followed by correlation and regression analyses with the arousal index, adjusting for respiratory events and insomnia-related factors. Results: Patients with COMISA exhibited more severe insomnia symptoms, greater daytime dysfunction, and more frequent nocturnal awakenings than those with insomnia alone, although their arousal index did not differ from that of the OSA group. Patients with COMISA exhibited altered activity in the right cerebellar lobule VIII (Cerebelum_8_R), left middle temporal gyrus, and right inferior frontal gyrus, opercular part. Lower fractional amplitude of low-frequency fluctuations (fALFF) in the Cerebelum_8_R was associated with a higher arousal index. This association remained significant after controlling for insomnia severity and sleep efficiency but was attenuated after adjustment for AHI. Conclusions: Reduced functional activity in the Cerebelum_8_R was independently associated with sleep fragmentation in COMISA, independent of insomnia severity but potentially mediated by respiratory events. These findings suggest this region may be involved in arousal-related neural regulation and could represent a therapeutic target for the complex symptoms of COMISA. Trial Registration: Chinese Clinical Trial Registry, ChiCTR2500095809. Full article

Memristor-based configuration memory offers an alternative solution to the volatility and large area overhead of conventional Static Random Access Memory (SRAM)-based FPGA configuration memory. Their non-volatile nature and the possibility of stacking them on top of the logic layer in a process called Back-End-Of-Line (BEOL) manufacturing help not only dramatically reduce area consumption but also significantly reduce startup time. However, due to the comparatively high defect probability caused by manufacturing defects, traditional approaches for defect tolerance are not fit to address these defects. This work introduces an approach to defect-aware and tolerant synthesis. Based on this, an investigation into the defect tolerance of different architecture choices regarding the size of LUTs and the fracturability of LUTs is presented. We can show that smaller, non-fracturable LUTs exhibit a higher defect tolerance. Moreover, multiple strategies to improve the mapping result based on the properties of the logic functions are introduced. Notably, reducing the mapping complexity of logic clusters during the packing stage significantly improves the mapping success rate. Full article

Aging is a significant risk factor for several neurodegenerative diseases. Understanding brain aging processes is a fundamental step in identifying the early signs of pathological dysfunction. Nonetheless, regional functional changes are still poorly characterized. In this study, we employed Detrended Fluctuation Analysis (DFA) to investigate age-related changes in the scale-free temporal dynamics of blood oxygen level-dependent (BOLD) signal fluctuations derived from resting-state networks. We compared DFA to fractional amplitude of low-frequency fluctuations (fALFF) to assess their ability to discriminate between young and old adults. Significant decreases (p < 0.01) in fALFF in the visuospatial and dorsal default mode networks and in DFA in the salience network, were identified as key predictors of functional brain aging. Using machine learning, we showed that DFA and fALFF provide complementary information for predicting aging, with an accuracy of approximately 80% achieved only through their combined use. Overall, DFA captures alterations in scale-free temporal organization that complement conventional spectral measures, providing additional insight into network-specific functional aging. Full article

Background: In temporal lobe epilepsy (TLE), locally reduced glucose metabolism (i.e., hypometabolism) is indicative of the epileptogenic onset zone (EZ). Here, we investigate the potential value of resting-state fMRI (rs-fMRI) for localizing the EZ with fluorodeoxyglucose positron emission tomography (FDG-PET) as ground truth. Methods: Twelve PET-positive patients (34.1 ± 13.1 y; 5 females) with unilateral drug-resistant TLE were included. FDG-PET and rs-fMRI were acquired simultaneously at a hybrid 3T PET-MR scanner. Hypometabolic regions were identified on the FDG-PET images by a nuclear medicine expert. The FDG-PET images were compared with a clinical FDG-PET control dataset with normal glucose uptake distribution. The output z-score maps were thresholded at z < −2 to produce a binary mask of the significantly hypometabolic regions. The hypometabolism masks were mirrored onto the contralateral hemisphere for the asymmetry comparison. Regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) were calculated from the rs-fMRI in conventional (0.01–0.1 Hz) and slow-3 (0.073–0.198 Hz) frequency bands. Asymmetry indices (AIs) were calculated using the ipsilateral and contralateral hypometabolic masks in the PET-positive subjects and assessed via the one-sample Wilcoxon test and Spearman correlation coefficients. Results: The AIs of conventional fALFF were significantly lower in the hypometabolic zone (p < 0.05). A significant negative correlation was found between the AIs of FDG-PET and fALFF in the slow-3 band (r = −0.62; p < 0.05). Conclusions: Conventional and slow-3 band fALFF showed a potential to mimic the FDG-PET findings in terms of EZ localization. Further research with extended cohorts and histopathological validation is required to determine the clinical value. Full article

This essay argues that Jeremy Bentham’s experience of the 1780 Gordon Riots and 1787 sojourn to White Russia inspired his conception of several projects for managing unruly populations. Bentham’s devotion to speculative enterprise informs his Defence of Usury, which vindicates schemers and dreamers from the criticism of Adam Smith, whose Wealth of Nations caricatured projectors as “riotous” con-artists who threatened domestic peace. Bentham’s Defence, I show, resuscitated early modern debates over the efficacy of free-lance enterprise to authorize his own efforts to improve society. A projector and theorist of projection, Bentham reveals how residents of the late eighteenth century described riot so that they could suppress it. Full article

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

Background/Objectives: The cerebellum contributes to both motor and cognitive functions. As basketball requires the integration of these abilities, basketball athletes provide an ideal model for exploring cerebellar adaptations. This study aimed to examine multidimensional cerebellar adaptations in basketball athletes and their associations with physical performance. Methods: In this study, 55 high-level basketball athletes and 55 non-athletes matched for age and gender were recruited for multimodal magnetic resonance imaging data collection and physical fitness tests. We compared the structural and functional differences in the brain between the two groups and analyzed the correlations between regional brain indices and physical fitness test outcomes. Results: Basketball athletes exhibited increased gray matter volume in Crus I, alongside heightened ALFF signal in Crus I and improved regional homogeneity in Crus II and VII b compared to non-athletes. Diffusion kurtosis imaging analysis demonstrated that athletes perform elevated kurtosis fractional anisotropy and decreased radial kurtosis within the cerebellar cortex and peduncles, with cortical modifications mainly localized around Crus I and lobule VI. Notably, both kurtosis fractional anisotropy and the amplitude of low-frequency fluctuations displayed positive correlations with vertical jump performance, an indicator specific to basketball ability. Conclusions: Basketball athletes exhibit structural, microstructural, and functional cerebellar adaptations, especially in Crus I. These modifications involve regions associated with motor and cognitive representations within the cerebellum, and part of the indexes are linked to the athletes’ physical performance. This study enhances our understanding of cerebellar adaptive changes in athletes, providing new insights for future research aimed at fully elucidating the role of the cerebellum in these individuals. Full article

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. Full article

Transcranial direct current stimulation (tDCS) is a safe, well-tolerated method of non-invasively eliciting cortical neuromodulation. It has gained recent interest, especially for its positive clinical outcomes in neurodegenerative diseases such as multiple sclerosis (MS). However, its simultaneous (during tDCS) and cumulative effects (following repeated tDCS sessions) on the regional brain activity during rest need further investigation, especially in MS. This study aims to elucidate tDCS’ underpinnings, alongside its therapeutic impact in MS patients, using concurrent tDCS-MRI methods. In total, 20 MS patients (age = 48 ± 12 years; 8 males) and 28 healthy controls (HCs; age = 36 ± 15 years; 12 males) were recruited. They participated in a tDCS-MRI session, during which resting-state functional MRI (rs-fMRI) was used to measure the levels of the fractional amplitude of low-frequency fluctuations (fALFFs), which is an index of regional neuronal activity, before and during left anodal dorsolateral prefrontal cortex (DLPFC) tDCS (2.0 mA for 15 min). MS patients were then asked to return for an identical tDCS-MRI visit (follow-up) after 20 identical at-home tDCS sessions. Simultaneous tDCS-induced changes in fALFF are seen across cortical and subcortical areas in both HC and MS patients, with some regions showing increased and others decreased brain activity. In HCs, fALFF increased in the right pre- and post-central gyrus whilst it decreased in subcortical regions. Conversely, MS patients initially displayed increases in more posterior cortical regions but decreases in the superior and temporal cortical regions. At follow-up, MS patients showed reversed patterns, emphasizing significant cumulative effects of tDCS treatment upon brain excitation. Such long-lasting changes are further supported by greater pre-tDCS fALFFs measured at follow-up compared to baseline, especially around the cuneus. The results were significant after correcting for multiple comparisons (p-FDR < 0.05). Our study shows that tDCS has both simultaneous and cumulative effects on neuronal activity measured with rs-fMRI, especially involving major brain areas distant from the site of stimulation, and it is responsible for fatigue and cognitive and motor skills. Full article

Purpose: Obesity’s metabolic consequences are well documented; however, its neurobiological underpinnings remain elusive. This systematic review addresses a critical gap by synthesizing evidence on obesity-induced neuroplasticity across structural, functional, and molecular domains through advanced neuroimaging. Methods: According to PRISMA guidelines, we systematically searched (2015–2024) across PubMed/Web of Science, employing MeSH terms: (“Obesity” [Majr]) AND (“Neuroimaging” [Mesh] OR “Magnetic Resonance Imaging” [Mesh]). A total of 104 studies met the inclusion criteria. The inclusion criteria required the following: (1) multimodal imaging protocols (structural MRI/diffusion tensor imaging/resting-state functional magnetic resonance imaging (fMRI)/positron emission tomography (PET)); (2) pre-/post-intervention longitudinal design. Risk of bias was assessed via the Newcastle-Ottawa Scale. Key Findings: 1. Structural alterations: 7.2% mean gray matter reduction in prefrontal cortex (Cohen’s d = 0.81). White matter integrity decline (FA reduction β = −0.33, p < 0.001) across 12 major tracts. 2. Functional connectivity: Resting-state hyperactivity in mesolimbic pathways (fALFF + 23%, p-FDR < 0.05). Impaired fronto–striatal connectivity (r = −0.58 with BMI, 95% CI [−0.67, −0.49]). 3. Interventional reversibility: Bariatric surgery restored prefrontal activation (Δ = +18% vs. controls, p = 0.002). Neurostimulation (transcranial direct current stimulation (tDCS) enhanced cognitive control (post-treatment β = 0.42, p = 0.009). Conclusion: 1. Obesity induces multidomain neural reorganization beyond traditional reward circuits. 2. Neuroimaging biomarkers (e.g., striatal PET-dopamine binding potential) predict intervention outcomes (AUC = 0.79). 3. Precision neuromodulation requires tripartite integration of structural guidance, functional monitoring, and molecular profiling. Findings highlight neuroimaging’s pivotal role in developing stage-specific therapeutic strategies. Full article