The quality of human brain magnetic resonance imaging (MRI) is often compromised by noise. Deep learning-based image denoising methods using convolutional neural networks (CNNs) have proven to be more effective compared to conventional denoising methods. Nonetheless, most CNN-based denoising methods are not accessible in practice because they require high signal-to-noise ratio (SNR) reference data acquired on many subjects for supervising the training of CNNs and may be further hampered by long training time and lack of access to adequate computing resources. This study seeks to address these challenges using transfer learning and/or self-supervised learning. Here, we demonstrate the efficacy of the proposed approaches on denoising highly accelerated (R=3 × 3) three-dimensional T1-weighted MRI data used for brain morphometry. Specifically, an extension of the “Self2Self” denoising method for volumetric brain data with “average masking” (entitled “Self2Self-AM”) is proposed to remove the need for additional high-SNR data by training the CNN on the noisy image volume itself. Transfer learning implemented by fine-tuning parameter values of the pre-trained CNN enables supervised denoising using high SNR data from only a single subject with short training time, and substantially reduces the training time of Self2Self-AM. The denoising performance is systematically and quantitatively evaluated and compared in terms of image quality and morphometric quantification accuracy by comparing to reference images acquired with 9-fold longer scan time. Supervised denoising with fine-tuning and Self2Self-AM with subject-specific training achieved the best denoising performance, as quantified by measures of image similarity (structural similarity index measures of 0.943 and 0.934), gray-white boundary sharpness (13.32% and 12.92%), cortical thickness estimation (whole-brain average discrepancy of 0.16 mm and 0.17 mm) and spatial overlap in brain segmentation (Dice coefficients of 0.986 and 0.985, respectively), significantly improving upon the raw images and outperforming conventional benchmark denoising methods. We present these methods as examples of techniques that may help to promote the wider adoption and practical application of CNN-based denoising methods for brain MRI, thereby benefiting a broader range of clinical and neuroscientific applications.
OBJECTIVE:Chronic ankle instability (CAI) involves force control deficits that may impair postural stability, yet their neural basis remains unclear. This study examined alterations in sensorimotor network connectivity and their relationship with force sense deficits in CAI. METHODS:Thirty individuals with CAI and 27 healthy controls performed ankle plantarflexion and dorsiflexion force-matching tasks while electroencephalography and electromyography were recorded. Source-level cortical signals were reconstructed, and partial directed coherence was used to quantify cortico-cortical connectivity within the sensorimotor network, including the primary motor cortex (M1), primary somatosensory cortex (S1), premotor cortex (PMC), supplementary motor area (SMA), and inferior parietal lobule (IPL), as well as cortico-muscular connectivity between these cortical regions and the lower-limb muscles, including the soleus (SOL) and tibialis anterior (TA), in the beta and gamma bands. Force-matching performance was quantified using absolute error (AE%). Group differences in task performance and connectivity measures were examined using t-tests or Mann-Whitney U tests, and their associations were assessed using Pearson or Spearman correlations. RESULTS:Individuals with CAI exhibited higher AE% than healthy controls during both plantarflexion and dorsiflexion. These deficits were characterized by significant reductions in beta-band cortico-cortical connectivity (e.g., S1 → M1 and IPL → SMA during both plantarflexion and dorsiflexion) and in descending cortico-muscular connectivity (M1 → SOL during plantarflexion; PMC/SMA → TA during dorsiflexion). An additional increase in ascending connectivity (e.g., SOL → IPL) was observed during plantarflexion, and it was negatively correlated with AE% in the CAI group (all corrected p < 0.05). CONCLUSION:Force sense deficits in CAI are associated with disrupted sensorimotor network connectivity rather than altered regional cortical activity, highlighting the importance of network-level integration for rehabilitation.
BACKGROUND:Posterior α suppression, frontal θ enhancement and fronto-posterior α-band coupling are routinely interpreted together as an 'EEG-DMN-like' rest-to-task marker. Whether they replicate as a unitary pattern or dissociate across metric families, task windows and cognitive paradigms remains insufficiently characterised within a single harmonised pipeline. METHODS:ds004148 (n = 60; 61-channel EEG; three internally oriented tasks: MATH, MEMORY and MUSIC) was the primary cohort, and EEGMAT (n = 36; 19-channel EEG; mental arithmetic) the independent external validation cohort. Spectral, topographic and connectivity metrics were computed under a per-subject θ-localised 60-s primary window with a fixed central 60-s sensitivity window. Six connectivity estimators covering phase-lag-tolerant, zero-lag-suppressing and amplitude-envelope families were computed. Replication required the expected direction, Cohen's dz ≥ 0.2 and a 95 % bootstrap CI excluding zero. Reference-scheme sensitivity analyses (original online reference and REST) were also performed. RESULTS:Posterior α suppression and frontal θ enhancement were directionally consistent across the three ds004148 tasks (dz = 0.21-0.47) with topographically coherent posterior α clusters. Under the central window, MATH α/α₁ fell to near zero and MEMORY frontal θ dropped below threshold. Lag-tolerant connectivity reached small-to-moderate effects in MEMORY and MUSIC (dz ≈ 0.20-0.43) and attenuated in MATH; zero-lag-suppressing estimators failed the replication criterion across cohort/task cells. EEGMAT confirmed direction rather than magnitude. Direction was preserved under montage harmonisation and both alternative reference schemes (0/16 sign changes under REST), with near-threshold per-metric attenuations. CONCLUSIONS:The sensor-level EEG-DMN-like pattern is best interpreted, within the limits of the present evidence, as a proxy of the rest-to-internally-oriented-cognition state transition rather than as a unitary biomarker of default-mode network activity. Reproducibility is component-, window- and estimator-specific: the spectral signature is directionally robust across cohorts, references and montage densities, whereas connectivity depends critically on estimator family and should be reported as such.
Directed forgetting (DF) refers to the goal-directed modulation of memory, typically manifested as superior memory for information cued to be remembered (TBR) compared to information cued to be forgotten (TBF). Although prior studies have emphasized task-evoked neural mechanisms of DF, it remains unclear whether intrinsic brain network organization contributes to individual differences in memory control. The present study examined whether resting-state functional connectivity within and between the default mode network (DMN) and frontoparietal network (FPN) predicts individual differences in DF performance. Forty-seven healthy college students (31 females; aged 18-28 years) underwent resting-state fMRI scanning and completed an item-method DF task. Behavioral results showed significantly better recognition for TBR than TBF items, confirming a DF effect. Connectivity analyses revealed that, 1) stronger within-DMN connectivity centered on the right precuneus/posterior cingulate cortex (pCun/PCC) was associated with better memory for TBR items and a larger DF effect. 2) Stronger within-FPN connectivity, especially between the right dorsolateral prefrontal cortex and bilateral inferior parietal lobule, predicted poorer memory for TBF items. 3) Moreover, stronger anticorrelation between the FPN and mnemonic regions was associated with better memory for TBR items, poorer memory for TBF items, and a larger DF effect. These findings suggest that DF depends on dissociable but coordinated intrinsic network properties supporting selective remembering, active forgetting, and functional segregation between mnemonic and control systems.
Selection of behaviorally relevant visual information relies on interactions between stimulus-driven and top-down controlled attention, yet their concurrent cortical dynamics remain poorly understood. Using EEG and a feature-based attentional shifting paradigm, we tracked early sensory processing of frequency-tagged flickering stimuli and behavior while participants simultaneously selected a cue-matching color (stimulus-driven selection) and a learned associate color (top-down selection). Steady-State Visual Evoked Potentials (SSVEPs) showed an initial amplitude enhancement of the stimulus-cued color at the expense of the top-down matched and unattended colors, which was mirrored in behavioral costs. From ∼500 ms after cue onset onwards, SSVEP amplitudes for the top-down selected color increased while stimulus-driven amplitudes decreased towards comparable levels and suppression of the uncued color persisted. Cross-correlation analyses of SSVEP amplitude and behavioral response time courses revealed closely matching attentional selection profiles. Together, these findings are consistent with a sequential attentional cascade in which early stimulus-driven selection predominates and is only later resolved by top-down guided stimulus selection that redistributes attentional resources between both to-be-attended colors.
Selective visuo-spatial attention is a complex and dynamic process that prioritizes relevant visual information while suppressing irrelevant distractors. Recent studies have demonstrated that incorporating computational visual attention models with functional magnetic resonance imaging (fMRI) in naturalistic paradigms (e.g., movie-watching) offers valuable insights into the neural mechanisms underlying selective visual attention. However, prior research has typically treated these models as “black boxes”, overlooking the heterogeneity of their elementary hidden units, such as the convolutional filters in a convolutional neural network (CNN). In this study, we investigate whether decomposing a CNN-based computational visual attention model into its constituent filters can advance our understanding of the neural mechanisms of selective visual attention. Specifically, we treated each filter as a distinct saliency-related feature channel and derived its temporal responses during movie-clip processing. These temporal responses served as filter-specific visual attention regressors in general linear model (GLM) analysis applied to movie-watching fMRI data to localize associated brain regions. Our results demonstrate that these filters can be grouped into three clusters with distinct cortical activation patterns. Notably, the primary cluster reliably mapped onto canonical fronto-parietal attention networks alongside higher-order visual cortices, showing remarkable consistency with the global attention regressor and suggesting that decomposed filters successfully capture core visuospatial components. Crucially, the filter-specific analysis revealed distinct neural response patterns beyond the global saliency representation, involving auditory regions and the default mode network (DMN) during naturalistic viewing. Furthermore, eye-tracking analysis confirmed that the filter units effectively capture an auditory bias in visual attention. These findings offer valuable insights into the neural basis of fine-grained visual attention representations, particularly under naturalistic viewing conditions.
The neural mechanisms underlying the brain's representation of event gist remain poorly understood. In this study, we investigate the neural architecture and computational principles governing the hierarchical representation of event gist in the human brain. Using an integrative approach that combines natural language processing with fMRI data from narrative listening, we extracted semantic event gist vectors from texts and aligned them with neural responses through representational similarity analysis. This analysis revealed that the brain's representation of event gist follows a functional hierarchy. Specifically, brain regions can be categorized into four functional levels based on their temporal preference for narrative information processing: from the Fine-grained level representing sentence-level event gist, to the Medium-Fine and Medium-Coarse levels integrating cross-sentence event gist, ultimately reaching the Coarse level constructing long-range narrative gist. Using lagged inter-subject functional connectivity (lag-ISFC), we further characterized the temporal lag structure across this hierarchical organization, revealing a systematic offset pattern consistent with the temporal hierarchy. To explore the computational principles underlying these hierarchical patterns, we implemented long short-term memory (LSTM) and convolutional neural network (CNN) models to map fine-scale to coarse-scale regional dynamics. The results revealed that LSTM models outperformed CNNs in capturing the hierarchical processing of narrative events across gist-representing brain regions, with deeper LSTM hidden layers corresponding more closely with higher-order cortical activity. Perturbation of LSTM hidden states further revealed that lower-layer representations are computationally upstream of higher-layer ones, and that deep hidden states are specifically required for correspondence with Coarse-grained regions. These findings, obtained within a brain-to-brain modeling framework that characterizes the fine-to-coarse cortical transformation directly, identify sustained recurrent state integration as a candidate computational property of intra-cortical hierarchical transformation during naturalistic narrative processing.
Learning to read is one of the most important missions in child development. Understanding the neural basis of this process supports the early diagnosis and intervention of reading difficulties in children. Most neuroscience studies on children's reading development are based on alphabetic languages. Chinese is a logographic language with different linguistic features. However, it remains unclear how structural and intrinsic functional network measures jointly characterize Chinese children's reading development and its change over time. In this study, we addressed this question using the Chinese Color Nest Project dataset, which included 184 children's cross-sectional and longitudinal reading tests and multimodal MRI images between 6 and 14 years old. We found that cortical thickness and functional connectivity in the bilateral frontal and occipital lobes were associated with Chinese children's reading development. Among these relations, only the reading-related functional connectivity between frontal lobes was moderated by age. In the prediction model, both the frontal and occipital lobes' structure and function predicted children's reading performance cross-sectionally, while mainly the occipital lobe contributed to reading performance longitudinally. In summary, using a large and multimodal dataset, this study systematically revealed the unique neural substrates of Chinese children's reading development. These findings contribute to an enhanced understanding of neural differences shaped by culture and language, providing insights for new targeted interventions for reading difficulties among Chinese children.
Neural activity arises from interactions between bottom-up sensory inputs and top-down information process, and thus may not be strictly time-locked to external task stimuli in function MRI (fMRI). Such temporal decoupling between stimuli and neural responses challenges the generalized linear model (GLM) that assumes fMRI signals as a convolution of a prior hemodynamic response function (HRF) with task timings, potentially leading to incomplete detections of task-evoked brain activations. To address this issue, we developed an HRF-model-free framework that detects task-evoked brain activations by capturing spatiotemporal pattern of fMRI signals within functional topography in the brain. Using this framework, we investigated neural dynamics of participants engaged in Human Connectome Project (HCP) emotion tasks. We identified neural activations in prefrontal subregions that exhibit partial temporal decoupling from task timing, rendering them undetectable by the GLM. These activations are likely driven by implicit events emerging from top-down information processes following the reception of external stimuli. Together, these findings suggest that task-evoked neural dynamics are shaped not only by externally events but also by autonomous cognitive process, underscoring the complex and interactive nature of brain function and highlighting the need for new analytical frameworks for task-based fMRI.
Objective Epilepsy-aphasia spectrum (EAS) represents the most common group of childhood epilepsy syndromes, however, the precise source localization of epileptiform discharges remains undetermined. This study aimed to localize and compare the sources of epileptiform discharges across different EAS subtypes using magnetoencephalography (MEG). Methods In this prospective MEG-based study, we recruited 70 patients with EAS, including 52 with self-limited epilepsy with centrotemporal spikes (SeLECTS), 12 with atypical benign partial epilepsy (ABPE), 3 with Landau-Kleffner syndrome (LKS), and 3 with Epileptic Encephalopathy with continuous Spike-and-Waves during sleep (EE-SWAS). Ten independent epileptiform discharges per patient were analyzed using distributed source modeling with standardized low-resolution electromagnetic brain tomography (sLORETA). The spike source density was quantified as current amplitude, and source locations were mapped according to the Desikan-Killiany atlas. Results The source locations of epileptiform discharges differed significantly across EAS subtypes. EE-SWAS exhibited the highest overall current source density, followed by ABPE, whereas SeLECTS and LKS showed the lowest values. Lobar analysis revealed frontal-predominant discharges in EE-SWAS and ABPE, contrasting with temporal-predominant discharges in SeLECTS and LKS. Moreover, patients with secondary generalized tonic‑clonic seizures exhibited higher current source densities than those with only focal seizures, consistent with broader EEG discharge spread. Conclusions This first systematic MEG source analysis across the full EAS reveals a hierarchical gradient from temporo-centric to fronto-centric distribution, paralleling the clinical severity gradient. Our findings highlight the potential of MEG source imaging for phenotyping and pathophysiological stratification in EAS.
Sense of Agency (SoA), the subjective experience of initiating and controlling one's actions, is a critical component of self-perception and consciousness, and is often degraded in different psychopathologies. Individuals with dissociation seem to have diminished SoA, but it is yet unknown whether this affects communication with their surroundings. This study employs Natural Language Processing (NLP) to examine linguistic markers of SoA in dissociation, focusing on passive voice usage as an indicator of diminished agency. A dataset of 26 017 posts was extracted from 'Reddit' forums concerning dissociation and maladaptive daydreaming (a suggested dissociative disorder), and for comparison, depression, anxiety, and social anxiety. Passive voice usage was measured using spaCy's NLP framework, and verb tense distributions were analysed to explore additional potential linguistic markers of agency. Negative binomial regression models were used to compare passive voice usage across groups, controlling for word count. The dissociation group, but not the maladaptive daydreaming group, exhibited significantly greater use of passive voice compared to the general psychopathology control group (IRR = 1.10, P < .001). Additionally, when examining the three psychopathology control subgroups separately (depression, anxiety, and social anxiety), no significant difference was found between the depression and dissociation groups in passive voice usage. These findings suggest that diminished SoA is a core feature of dissociation, reflected in linguistic patterns. The results highlight the usefulness of language-based big data investigations in understanding the self-experience in psychopathology research.
Visualizations are fundamental to neuroimaging research, facilitating tasks ranging from exploratory data analysis to the communication and interpretation of findings. Despite their necessity, visualizations can potentially compromise the confidentiality of individual participants. In this paper, we discuss how visualizations may inadvertently lead to privacy leakage and explore methods to mitigate such risks. Our work investigates ways to securely share visualizations that faithfully preserve the patterns supporting the derived insights from data analysis, rather than deriving conclusions from the visualizations themselves. We address privacy-preserving visualization within a differential privacy (DP) framework, focusing on commonly used visualization methods for functional network connectivity. Various perturbation-based strategies are investigated for protecting correlation-related measures, with analyses of their privacy costs and the effects of pre-processing and/or post-processing. To achieve a better balance between privacy and visual utility, this paper introduces novel workflows tailored for connectogram and seed-based connectivity visualizations that maintain the qualitative patterns observed in non-private results. Overall, this work illustrates how DP can be effectively applied to neuroimaging visualization, demonstrating its efficacy as a robust methodology for securing sensitive biomedical data.
Video gaming is highly prevalent in adolescence, yet its relationship with cortical morphology in early adolescence remains unclear. Data from the large, diverse nationwide Adolescent Brain and Cognitive Development Study cohort (N=5,686) were analyzed to investigate the association between video gaming behaviors and cortical morphology in adolescents. Adolescents (mean: 14.2 years old) reported time spent playing single and multi-player games each day and their frequency of playing mature-rated games. Measures of cortical volume, surface area, and thickness of regions defined by the Desikan-Killiany atlas were obtained from 3T MRI scanners. Generalized additive mixed effects models were used to evaluate the association between gaming behaviors and cortical morphology, adjusting for demographics, genetic ancestry, socioeconomic status, non-gaming screen time, pubertal stage, body mass index percentile, scanner characteristics, and familial relationships. In addition to regional analysis, vertexwise analysis was performed using the Fast and Efficient Mixed Effects Algorithm, which provides higher spatial resolution. Higher total gaming time was associated with lower cortical surface area and thickness across multiple regions. Single-player but not multi-player gaming was associated with altered morphology regions involved in language processing and social cognition, which may be due to the different processes engaged by each game type. Mature-rated gaming frequency was also associated with lower cortical thickness in several regions and lower surface area in the right parahippocampal gyrus. Our results demonstrate that different gaming behaviors are associated with distinct patterns of cortical morphology. Decreases in cortical surface area and thickness are a normative feature of adolescent development, and future research on long-term outcomes is required to determine whether these differences reflect variation outside of normative developmental trajectories and the directionality of this association.
Existing neural studies have predominantly focused on threat processing during fear acquisition and generalization, whereas the spatiotemporal dynamics of safety learning remain poorly understood. Here, we examined safety learning across early acquisition, late acquisition, and fear generalization, while characterizing early and late event-related potential (ERP) responses using a fear conditioning and generalization paradigm with multiple conditioning sets (N = 29). Behavioral ratings and reaction times indicated enhanced safety learning from early to late acquisition, together with a generalization gradient from threat to safety stimuli. Non-parametric cluster-based permutation tests (CBPT) revealed larger frontal N2 amplitudes to the safety cue than the threat cue across all experimental phases. During fear generalization, both the safety cue and the generalization stimuli (GS1-GS4) elicited larger occipital P2 amplitudes than the threat cue. The safety cue further elicited larger right frontocentral late positive potential (LPP) amplitudes than the threat cue and the generalization stimuli, as well as larger sustained anterior negativity (SAN) than the generalization stimuli. Descriptive source reconstruction suggested shifts in the estimated sources of the N2 from early to late acquisition, whereas source estimates of the P2, LPP, and SAN showed spatial overlap with medial prefrontal, limbic subcortical, occipitotemporal, medial temporal, medial parietal, and cerebellar regions. Together, these findings provide a preliminary characterization of the spatiotemporal dynamics of safety learning across fear acquisition and generalization and establish a normative framework for future investigations of altered safety learning in anxiety disorders.
Autism spectrum disorder (ASD) is a neurodevelopmental condition with core diagnostic domains of social communication impairments, restricted interests and repetitive behaviors. Idiosyncratic brain organization is a potential hallmark of ASD. Previous transcranial direct current stimulation (tDCS) studies often targeted dorsolateral prefrontal cortex, with changes oin brain dynamics averaged across the cohort. We utilized a magnetoencephalographic (MEG) array to characterize individual differences in brain dynamics induced by cortico-cerebellar tDCS across nodes of a social cognition network. A randomized, sham-controlled, double-blind, within-subject clinical trial was conducted in a cohort of 24 young adults with ASD or high autistic traits. Two separate sessions of computerized social learning activities were combined with verum/sham tDCS, with anodal electrode over right temporoparietal junction (TPJ) and cathode on right deltoid. Following stimulation, theta- and alpha-band activity were evaluated within nodes of a social cognition network: bilateral TPJ, fusiform, medial prefrontal cortex and Crus I/II of cerebellum. Idiosyncratic participant-specific up- and down-regulation of theta- and alpha-band activity occurred across the network. Activity in right Crus I/II, a region inundated by the stimulation current, strongly correlated with the change of activity summed across all cerebral cortical nodes in theta- but not alpha-band. Intrinsic theta-band activity is believed to mediate input/output relationships in cerebellar cortex and to drive synaptic plasticity. These results suggest that theta-band stimulation of cerebellar cortex might be an effective therapy for individuals on the autism spectrum who present with cerebellar hyperactivity.
Professional Go (Baduk) is a rare model of lifelong cognitive expertise that taxes strategic planning and working memory. Prior Go studies examined younger or amateur players and isolated regions (Jung et al., 2013); whether professional practice leaves a network-level structural signature in older experts is unknown. To our knowledge, this is the first characterization in an older professional cohort, extending beyond regional measures to network morphometry and inter-hemispheric covariance. We used T1-weighted structural MRI to compare 29 older male professional Go players (PGP; mean career 54.6 years; dan 5-9) with 50 male controls (CTRL) recruited at baseline of a Go-training trial (KCT0010704). Groups were comparable in age and vascular risk, although CTRL had more education (15.9 vs 12.4 years). Regional gray-matter volume, thickness, and texture were aggregated into six Yeo functional-network regions, and inter-hemispheric structural covariance was computed. PGP showed larger executive control network (ECN) volume (η²p = 0.153; PFDR = 0.006), driven by the right middle frontal gyrus (MFG; η²p = 0.392; PFDR < 0.001). The right MFG effect held across education-adjusted ANCOVA, propensity-score matching, and a cognitively-normal subgroup (n = 62); ECN persisted in the subgroup but did not survive strict matching in the full sample. Covariate-adjusted inter-hemispheric covariance was higher in PGP in five of six networks (all PFDR < 0.05). Cognition was domain-specific rather than globally superior: PGP excelled at working memory (+0.71 SD) but underperformed at naming (-0.82 SD; Group × Domain P < 0.001). The enlarged right MFG, robust to education adjustment, is the principal, association-level finding, motivating longitudinal follow-up.
Intergroup emotions regulate intergroup interactions, often leading to attitudes and behaviors that favor the ingroup. Theoretical frameworks in social psychology suggest that group identities, perceived threats, stereotypes, and context shape intergroup outcomes, including intergroup emotions. Hence, studying emotions in intergroup dynamics can enhance our understanding of the mechanisms underlying prejudice and emotional bias. In this research, we aimed to investigate how emotional expressions and physical proximity of group members modulate brain patterns of emotions during intra- and intergroup encounters. We presented a series of 360-degree videos showing ingroup and outgroup members either looking at the Finnish White majority participants (N = 40) from a distance or approaching them while their BOLD activity was measured with fMRI. The protagonists in the videos expressed neutral, happy, or angry facial expressions. Evoked emotions were measured by decoding emotion-related neural patterns while watching the videos. The results suggest that the valence of emotional expressions modified the perception of warmth of ingroup and outgroup members, as happy-looking outgroup members evoked emotions associated with the paternalistic stereotype, whereas angry-looking ingroup members elicited emotions associated with the envious stereotype. Moreover, physical proximity enhanced intergroup emotional bias, with close-proximity ingroup members evoking neural patterns of anger and fear, while close-proximity outgroup members evoked patterns of contempt and discomfort. The results are discussed in terms of their contribution to neuroscientifically informed models of intergroup emotions.
BACKGROUND:While it is established that motor training induces structural changes in the brains of young adults, structural adaptations in aging brains are less studied. METHODS:This randomized controlled study investigated the impact of long-term strength and balance training on the structural plasticity in 60 elderly adults (64 - 82 years old, 70.6 ± 4.7) using multi-modal neuroimaging. We compared the effects of three months of strength training to balance training of the same duration and to a passive control group. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to assess grey matter (GM) and white matter (WM) plasticity. White matter tract integrity (WMTI) modelling was employed to explore the microstructural underpinnings of white matter alterations. RESULTS:We found that strength training was associated with changes in diffusion metrics consistent with white matter microstructural remodeling, specifically increased extra-axonal axial diffusivity in the bilateral inferior fronto-occipital and longitudinal fasciculi. Additionally, both balance and strength training mitigated reductions in axonal water fraction in the splenium of the corpus callosum and the right posterior corona radiata observed in the control group. CONCLUSION:These results underscore the potential relevance of strength and balance training to induce beneficial neural plasticity by counteracting aging-related demyelination in the corpus callosum and highlight the specific role of strength training in facilitating white matter reorganization in key transmission fiber pathways.
The ability to imagine scenarios decoupled from the immediate environment supports a wide range of cognitive functions, such as planning, memory, and decision making. However, this increase in cognitive sophistication comes with a potential cost of mistaking simulation for reality. Here, we provide a functional analysis of the imagination-reality problem and argue that the specific way that imagination is used to guide behaviour puts unique constraints on the ability to distinguish it from reality. Our analysis illuminates the space of possible solutions supporting the ability to separate imagination from reality and offers a roadmap for future empirical research.
Theoretical models and empirical evidence suggest that psychedelics alter contextual influences on perception. To test this, we examined contrast surround suppression, a visual illusion in which a grating appears lower in contrast when embedded in a high-contrast surround. While psilocybin has been reported to enhance this and related illusions at moderate-to-high doses, it is unknown whether low-dose Lysergic Acid Diethylamide (LSD) produces similar effects. In a randomized, double-blind, within-subjects crossover study, N = 30 healthy participants received placebo, 10, and 20 μg of LSD (base-equivalent). In each session, participants performed a contrast discrimination task and rated subjective drug effects. LSD did not measurably influence contrast surround suppression. However, participants reported subjective visual changes, including blurrier, more saturated, and more dynamical visual perception. These subjective alterations did not correlate with individual differences in contrast-surround suppression. Our findings indicate a dissociation between subjective experience and early visual contextual processing, suggesting that subjective visual effects of low-dose LSD may arise through mechanisms distinct from those mediating contrast surround suppression. Future research should test whether higher doses of LSD affect contrast surround suppression.
