To examine how three dimensions of the Multidimensional Attitude toward Ambiguity Scale (MAAS)-Discomfort with Ambiguity, Absolutism, and Need for Complexity-relate to resting-state functional connectivity, conceptually replicating and extending the work of Liu et al. (2023) in a Japanese sample. Liu et al. (2023) reported that higher ambiguity tolerance was associated with stronger connectivity in integration and control networks, whereas lower ambiguity tolerance was associated with stronger connectivity in threat- and error-monitoring circuits. Of the three MAAS dimensions, Need for Complexity was the one most closely aligned with their measure. Thirty-nine participants underwent resting-state MRI and completed the MAAS. Region-of-interest (ROI)-to-ROI analyses were used to test the associations between each MAAS dimension and its hypothesized connectivity pair, controlling for age, sex, and head motion. No MAAS dimension was significantly associated with its corresponding connectivity pair. Effect sizes were negligible, although the Need for Complexity showed a small zero-order correlation with inferior parietal lobule-middle cingulate cortex connectivity. These findings contrast with earlier reports using unidimensional measures, suggesting that previously observed neural correlates may not map directly onto specific MAAS dimensions. Larger, well-powered, cross-cultural studies are needed.
This paper reports a genetic identification task using 3D convolutional neural network (3D-CNN) models applied to a proprietary 3D magnetic resonance imaging (MRI) dataset of patients with lissencephaly. Lissencephaly is a neuronal migration disorder caused by genetic mutations or deletions in which specific causative genes result in distinct morphological alterations in brain structure. The objective of this study was to identify causative genes through image classification by analysing three-dimensional structural features of brain MRI using deep learning. In our experiments, we extended representative CNN architectures to handle three-dimensional inputs and performed three-class classification targeting the primary causative genes, LIS1 and DCX, along with a category for other genetic variations. Our results demonstrated that the 3D-ResNet18 model achieved a mean classification accuracy of over 78%. Furthermore, to enhance the precision for primary genes, we introduced a decision-making process based on prediction probability thresholds. This approach yielded an average precision improvement of 4.67% for DCX and 4.84% for LIS1 across all the evaluated models.
Complex congenital heart disease (CHD) is associated with reduced brain volumes, but little is known about the brain developmental trajectory in CHD beyond childhood, which is a critical period for brain maturation. This study reports alterations in brain volumes from a large cross-sectional dataset of patients with CHD and controls, with an age range from childhood to young adulthood. Patients and controls underwent 3 T cerebral MRI and overlapping cognitive assessments. Images were processed using Freesurfer 5.3. The dataset comprised 311 participants, 128 CHD and 183 controls aged between 9 and 32 years (male: 52.1%). Associations between the total brain and grey matter, white matter, and cerebrospinal fluid (CSF) volumes and age, sex, group (CHD vs. controls) and maternal education were analysed using linear mixed models. Global and total grey/white matter volumes were smaller in patients with CHD compared to controls (p < 0.001), whereas CSF volumes did not differ significantly between groups (p = 0.23). No significant interaction between the group, sex and age was found. Larger global brain volume was associated with higher maternal education (p < 0.001) and higher IQ (p < 0.001). Consistent lower brain volume in CHD than in controls throughout childhood and young adulthood suggests that there is no convergence towards healthy peers in CHD brain volumes over time. Functional correlates of smaller brain volumes underscore the importance of longitudinal studies in better understanding the evolution and determinants of impaired brain development in CHD populations.
Exposure to trauma and mild traumatic brain injury (mTBI), which often co-occur and can both trigger acute and chronic pathophysiological processes, have been linked to brain changes indicative of accelerated neurological aging. However, few longitudinal studies have examined the impact of these factors over time or characterized how the chronicity and severity of associated symptoms affect aging trajectories. This study included individuals (N = 79) who were exposed to psychological trauma, with most of the cohort additionally having sustained a mTBI. Participants completed structural MRI scans and behavioral assessments during three study visits over 18 months at nine-month intervals. Utilizing a deep learning method (DeepBrainNet), brain age was estimated using a large, published, normative sample and compared against chronological age. Associations between resultant brain age and neurobehavioral symptoms over time were also examined. Findings revealed that predicted brain age was significantly older (i.e., 4.8 years) than chronological age at baseline (p < 0.0001). This brain age gap progressively widened over the course of evaluation, with a difference of 5.4 and 6.5 years at 9 and 18 months post-baseline, respectively. Participants reported mild to moderate posttraumatic stress disorder, depressive, neurobehavioral, and postconcussive symptoms, which remained stable and were not correlated with the accelerated rate of brain aging. The duration since trauma exposure did not significantly influence the rate of neurological aging. Results suggest that the experience of trauma, and the confluence of factors that frequently co-occur with trauma, have a profound impact on brain health.
As one of the world's most widely spoken languages, Arabic holds immense cultural and geopolitical importance, but traditional teaching methods often fall short in preparing learners for real-life interaction. Virtual reality (VR) and task-based language teaching (TBLT) offer promising, immersive alternatives, but the neurobiological mechanisms underlying their effectiveness remain unclear. Understanding these mechanisms can inform second language pedagogy. This study investigated whether immersive, task-based Arabic vocabulary learning in VR elicits stronger brain activity and superior learning outcomes than traditional lecture-style instruction. Twelve English-dominant adults with no prior Arabic knowledge were randomly assigned to VR or traditional learning groups. Participants completed receptive and productive vocabulary pre- and posttests while cortical activity was recorded using functional near-infrared spectroscopy (fNIRS). fNIRS signals were preprocessed and compared across groups. Both groups showed significant pre-to-post gains in receptive vocabulary scores. However, the VR group exhibited significantly greater improvements in productive performance (p = 0.002). fNIRS analyses revealed consistently higher hemodynamic variability in prefrontal (Broca's area), parietal and superior temporal (Wernicke's area) regions for The VR group during learning and subsequent tests, indicating greater brain activity. VR-mediated TBLT enhances cortical activation and productive Arabic performance compared with traditional instruction, offering neurobiological evidence for immersive technologies in language learning.
Visuomotor adaptation, the ability to recalibrate movements using visual feedback, continues to mature throughout childhood, yet the neural mechanisms driving this development are not well characterized. We recorded magnetoencephalography (MEG) in children (6-12 years) and adults as they reached visual targets under a 45° cursor rotation and examined cortical oscillations at two movement stages, before peak velocity (predominantly feedforward) and after it (predominantly feedback-based). Both child and adult groups showed adaptation in their cursor movements, and older participants were faster, more accurate, and less variable in their movement timing. During feedforward planning, children showed reduced pre-movement beta synchronization and weaker post-stimulus beta desynchronization in the motor, parietal, and occipital cortices, consistent with a less mature feedforward process. Children also showed more prominent, sustained prefrontal theta activity following target onset, consistent with greater reliance on executive control. During the deceleration period after peak velocity, group differences were reduced, suggesting that predominantly feedback-based movement correction is closer to adult-like function than feedforward planning, although not yet fully mature. Brain-behavior relationships also differed by age: neural oscillations were more tightly coupled to task performance in children than in adults, suggesting that maturation is still ongoing in this age range. Together, these findings suggest that the development of visuomotor adaptation is not a uniform process. Rather than developing as a single capacity, feedforward execution and feedback-based error correction follow graded maturation timelines.
The eye is often described as a window to the brain and may offer a non-invasive opportunity for investigating neurodegenerative changes. Although cerebral amyloid-β (Aβ) accumulation is a well-established hallmark of Alzheimer's disease (AD), the potential of ocular regions to capture aspects of this pathology in positron emission tomography (PET) remains underexplored. Our primary objective is to conduct a radiomics-SUVR analysis to investigate the relationship between ocular regions of interest (ROI) extracted from PET imaging and regional SUVR values obtained from six brain PET regions. Our secondary objective is to address domain shifts arising from heterogeneous data distributions across imaging sites during ocular segmentation. This study included 228 participants from Chonnam National University Hospital (CNUH) and an external validation cohort of 50 participants from ADNI-4, all with paired 18F-florbetaben PET and 3D T2-weighted MRI. For the segmentation task, we propose HCA-Net, a Hierarchical Cross-Attention Network that integrates co-registered PET and MRI using a multi-scale fusion strategy, allowing the model to better handle domain shifts in cross-site imaging conditions. For the radiomics-SUVR analysis, the segmented ocular ROI was classified into Aβ⁺ and Aβ⁻ groups using a CNN model. Radiomic features were then extracted and compared with corresponding regional brain SUVRs using statistical methods. While several features showed group differences at an uncorrected threshold, only one feature remained significant after correction for multiple comparisons highlighting the need for further validation. Correlation analyses identified modest associations between ocular radiomics and regional brain SUVRs, highlighting the influence of global amyloid burden on these relationships. Overall, our study presents a multimodal cross-site segmentation framework and provides preliminary evidence of neuro-ocular associations in AD through 18F-FBB PET imaging, highlighting the need for further validation.
We introduce a quantitative pipeline for region-level explanations of an Alzheimer's prediction model using four post hoc explainable AI (XAI) methods (DeepLIFT, Layer Gradient × Activation, Occlusion sensitivity analysis, and XGrad-CAM). Explanations are mapped to anatomical regions of interest using Freesurfer and summarized as the percentage of volume per region that is deemed relevant by the XAI method. We validate the pipeline in a cross-sectional ADNI cohort, where the explanations (except XGrad-CAM) converged on hippocampus/amygdala as most discriminative for CN, whereas relevant regions for AD were method-dependent. In longitudinal data from subjects converting from cognitively normal to Alzheimer's disease, the relevance of caudate/putamen/pallidum increased across most XAI methods, with FDR-corrected significant trends for three of four methods. In amyloid-positive MCI subjects, attribution profiles were intermediate between AD and CN groups. We additionally evaluated XAI fidelity using perturbation analyses and quantified cross-modal correspondence by correlating regional MRI relevance with amyloid and tau positron emission tomography (PET) standardized uptake value ratios. We observed weak correlations between XAI maps and PET across cortical and subcortical regions, suggesting that MRI and PET provide complementary information. Our findings show that XAI results are method- and class-dependent for AD classification. Still, all methods might convey relevant complementary information that can be used by end users to better understand the biological processes behind the patterns used to classify AD subjects. Thus, we suggest applying different XAI methods for AD classification.
Habituation reduces the salience of repeated socio-emotional cues like facial expressions once they become familiar, predictable, or no longer relevant. To clarify whether variation in amygdala habituation to repeated emotional faces is associated with psychological symptoms and persistent antisocial behavior, the current study examined whether internalizing and externalizing symptoms, and their interaction, were related to amygdala habituation in treatment-referred young adults with antisocial histories, and whether habituation predicted future recidivism. For this purpose, ninety-eight treatment-referred young adults (18-27) with a history of antisocial behavior performed an emotional face-matching fMRI task presenting fearful, angry, sad, happy and neutral faces. Habituation was operationalized as the change in amygdala activation from the first to the second half of the task for each emotional face condition. Internalizing and externalizing symptoms were measured with the Adult Self-Report. Recidivism was obtained from Dutch national judicial records (median follow-up 2.5 years post-scan). Amygdala habituation differed by emotional expression in the treatment-referred youth, with the strongest habituation for happy faces and relatively weaker habituation for negative and neutral expressions. However, within this group, individual differences in habituation were not associated with internalizing symptoms, externalizing symptoms, their interaction or recidivism. Additional analyses comparing treatment-referred youth with healthy controls indicated that treatment-referred young adults showed reduced habituation to fearful faces compared to controls. Taken together, these findings suggest that amygdala habituation may help identify emotion-specific neural processing differences between treatment-referred young adults and controls, but is not associated with dimensional symptom heterogeneity or future recidivism within treatment-referred young adults.
Transcranial direct current stimulation (tDCS) enhances cognitive abilities yet has highly inconsistent outcomes, highlighting the need to clarify its neurophysiological mechanisms. Herein, we integrated EEG with machine learning to assess 24 participants performing unpredictable magnitude/parity switching tasks under anodal tDCS (a-tDCS) and sham conditions using a double-blind design. Behavioral results showed that a-tDCS over the right dorsolateral prefrontal cortex (rDLPFC) markedly improved switch cost accuracy while reducing mixing cost accuracy. EEG analyses revealed that a-tDCS induced a significantly larger N1 in repeat trials, eliminated switch-repeat differences in N2 latency, frontal/central alpha (1200-1500 ms) and parietal beta power (600-800 ms), while enhancing central theta power (100-400 ms) in females. Moreover, participants were divided into high-gain and low-gain groups by median split of switch cost improvement score (a-tDCS minus sham accuracy). Decoding analyses further revealed a significantly higher trial-type classification accuracy in the low-gain group under a-tDCS condition during stimulus-response interval. Conversely, the high-gain group exhibited lower trial-type classification accuracy, albeit with a higher response-hand classification accuracy during pre-stimulus period. Additionally, model interpretability analyses showed a frequency band main effect on trial-type classification with significantly higher alpha and/or beta weights compared to theta and/or delta weights across regions. Furthermore, a-tDCS significantly reduced left frontal delta weights, enhanced left centrofrontal alpha weights, and increased right central alpha weights in high-gain group. These findings suggest that a-tDCS over the rDLPFC regulates attention, conflict detection, and inhibition to modulate unpredictable task switching, highlighting the dominant role of high-frequency bands in trial-type classification, and indicating that a-tDCS impacts the low- and high-gain groups via reactive conflict resolution and broad proactive preparation, respectively.
In Parkinson's disease (PD), increased amplitude of high-frequency oscillations (HFO) has been confirmed to be coupled with the beta oscillations phase, resulting in increased phase-amplitude coupling (PAC) in the subthalamic nucleus (STN). This pathological coupling correlates with the severity of motor symptoms and is known to be modulated by therapeutic interventions. For example, dopaminergic medications and deep brain stimulation (DBS) were previously shown to reduce PAC, which changes in magnitude between states of rest and movement. However, PAC alterations during kinetic and static movements in the presence and absence of medications remain to be determined. Furthermore, there is little evidence on the relationship between PAC and clinical symptoms of PD. In this study, we investigated these two issues. We analyzed a publicly available dataset, which contained STN local field potential registrations (n = 20) and concurrent limb electromyography during resting, static, and kinetic movements with and without levodopa intake. We calculated the PAC within the STN during different conditions, and the PAC between the limb tremor and the ipsilateral LFP oscillations. Beta-HFO STN PAC was increased during the medication off period, especially during kinetic movement and was highly correlated with bradykinesia and negatively correlated with tremor. We observed no interaction between tremor frequency oscillation phase on electromyography and beta or HFO oscillation amplitude within the STN in either of the conditions. These results point out that beta-HFO PAC provides complementary information to beta power for electrophysiological localization during DBS implantation and during adaptive DBS.
Multiple Sclerosis (MS) disrupts white matter (WM) tract organization, affecting brain networks and neurological function. Current methods lack the capacity to directly quantify tract- and network specific myelin-sensitive measures with clinical outcomes. This cross-sectional study (122 MS patients, 72 females, median age 48.5 years, median EDSS 3.0) applied multi-compartment Myelin Streamline Decomposition (MySD), an advanced MRI technique for assessing myelin-sensitive measures of WM bundles and networks in the presence of focal lesions. Patients underwent 3T MRI with magnetization transfer (MT)-weighted and multi-shell diffusion imaging. These data enabled multi-compartment MySD reconstruction to generate myelin-sensitive tract measures for the corticospinal tract (CST) and cingulum bundle (CB) and MVF-derived myelin-sensitive network properties. Associations were tested between tract-specific measures and five network measures with disability (Expanded Disability Status Scale (EDSS)), information processing speed (Symbol Digit Modalities Test) and neuroaxonal damage (serum neurofilament light chain), adjusting for covariables. Primary FDR-corrected analyses were considered for the left/right CST with EDSS and left/right CB with SDMT. All remaining analyses were considered exploratory. While primary CST-EDSS associations did not reach significance, a subgroup analysis revealed an association between myelin-sensitive tract measure of the left CST with SDMT z-scores (β = 0.57, p = 0.001, R2 Adj = 0.21). Sensitivity analysis demonstrated correlations between myelin-sensitive tract measure of the left CST with mean strength (ρ = 0.38, p < 0.001) and efficiency (ρ = 0.33, p < 0.001). Lower myelin-sensitive tract measures of the CST related to poorer cognitive performance. Multi-compartment MySD enables exploration of myelin-sensitive proxies and clinical outcomes in MS.
Adjuvant therapies in aphasia rehabilitation may help reduce the cost and clinical resources required for intensive speech-language interventions. We conducted a proof-of-principle pilot study to evaluate whether transcranial direct current stimulation (tDCS) paired with a shortened course of phonomotor treatment (sPMT)-targeting sounds and nonwords but not real words-enhances phonological production in chronic post-stroke aphasia, and to examine the neural mechanisms underlying this approach. Using a double-blind, parallel-group design, participants received 30 h of sPMT combined with 1 mA tDCS (anode/cathode over left/right inferior frontal gyrus) delivered at the start of each intervention session. Here, we report data from six older male participants matched on aphasia severity (active: n = 3; sham: n = 3). Phonological production and confrontation naming were assessed at baseline, immediately post-intervention, and at 3-month follow-up. Structural and resting-state functional MRI (rs-fMRI) were acquired at baseline and post-intervention. Repeated-measures ANOVA revealed a significant group-by-time interaction for phonological production, with the active tDCS + sPMT group showing gains from baseline to post-intervention that were maintained at 3 months, whereas the sham group showed no significant improvement. Confrontation naming showed no significant effects of time or group. MRI-based estimates of current density (J) indicated that J varied systematically with lesion volume and inter-electrode distance, underscoring the importance of individualized electrode placement. rs-fMRI analyses demonstrated significant group-by-time interactions, with greater connectivity increases in the active versus sham group across domain-specific and domain-general networks. These preliminary findings suggest that active tDCS may enhance the effects of sPMT on phonological production and provide mechanistic support for individualized, network-informed neuromodulatory approaches in post-stroke aphasia rehabilitation.
Irritability is a prevalent and impairing feature associated with autism, yet remains poorly understood, particularly in adults. Drawing heavily on insights translated from pediatric and transdiagnostic literatures, we propose that irritability in autistic individuals often reflects a psychophysiological stress or threat response, rooted in a vulnerable neurobiology (e.g., sensory sensitivities, intolerance of uncertainty), but may also stem from intrinsic neurobiological dysregulation independent of environmental triggers. The current treatment paradigm for autistic adults, largely extrapolated from pediatric antipsychotic trials, leaves these adults critically underserved due to a lack of evidence-based treatments, clinical trials, or validated tools to measure their internal experience. This viewpoint deconstructs irritability, differentiating its affective nature from aggression, and highlights heterogeneity across the lifespan and support needs. We critique the limitations of current assessment methods and recommend a shift toward a multi-modal strategy integrating self-report (when feasible) with objective, physiologically-informed tools (e.g., wearable biosensors) and nuanced observer reports. We argue that the field is poised for neuroscience-informed treatment innovation-including novel pharmacological agents and adapted psychosocial interventions-but is hampered by a lack of rigorous clinical trials in adults. Finally, we call for mechanistically driven trials to address the large unmet burden of inadequately treated irritability, ultimately improving the quality of life for autistic adults and their families/caregivers. Irritability in autistic adults is a major crisis, but most treatments are decades old and were only tested on children. Drawing on evidence primarily from youth and related conditions, our review explains that irritability is often a physical and emotional stress response to things like sensory overload or trauma, not just a “behavior problem.” We call for urgent research into new, adult‐focused treatments and tools, like wearable sensors, to directly address the root causes of this distress.
The underlying mechanisms of increasing prosocial behavior to charity organizations during adolescence remain poorly understood. Here we investigated adolescents' neural sensitivity to equity and inequity outcome distributions for self and charity. In three longitudinal waves including participants between ages 11-24-years (wave 1: n = 160, 86 females; wave 2: n = 165, 84 females; wave 3: n = 174, 90 females), we studied neural responses in dorsolateral prefrontal cortex (DLPFC), temporal parietal junction (TPJ), and ventral striatum (VS). To this end, we used a fMRI paradigm in which participants received rewards either only for self, only for charity, for both, or for neither. Similar as in previous research, activity in the VS correlated with self-serving outcomes. In contrast, the DLPFC and TPJ showed lowest activity to equity outcomes (mutual benefit) and higher activity to both self-serving and charity-serving inequity, suggesting a general role in processing inequity. Finally, TPJ activity was highest when neither party received a reward, possibly signaling additional demands on social attention switching. Developmental comparisons revealed region-specific developmental trajectories, with stronger activity in VS for self-serving gains in early adolescence and stronger activity in DLPFC and TPJ for inequity gains in late adolescence. Together, these findings suggest a qualitative shift from personal reward-driven processing to more socially evaluative equity processing during adolescent development.
Growing evidence implicates self-blame-related neural networks in the pathophysiology of major depressive disorder (MDD). fMRI neurofeedback is an emerging technology with the potential to deliver interventions targeting the neural substrates of self-blame, but its feasibility and potential mechanism in current MDD remain unresolved. The current pilot study employed a single-session, exploratory neurofeedback trial harnessing the subgenual cingulate cortex (SCC) BOLD activity as the sole training target. Two active interventions were compared. In Intervention A (n = 10), participants were encouraged during neurofeedback to upregulate their SCC activity during a 'guilt' task and downregulate it during an 'indignation' task. In Intervention B (n = 10), participants were encouraged to do the opposite. Clinical scores improved significantly across interventions, although no significant intervention differences were detected. Neurofeedback performance was variable across participants and conditions, with significant group-level regulation in the intended direction observed only for the indignation condition in Intervention B. A whole-brain analysis using a uniform preprocessing pipeline revealed no clusters surviving correction for multiple comparisons. The results support the feasibility of the protocol and suggest that engagement with negative autobiographical memories during SCC-oriented neurofeedback can be delivered safely in this small sample. Group-level target engagement was inconsistent, but the observation that SCC regulation was most achievable during indignation tentatively suggests this region may be more functionally heterogeneous for causal agency representations than commonly assumed. The absence of significant whole-brain effects under a uniform pipeline indicates that any neural changes were not robust at the group level in this small sample. In summary, the study provides preliminary feasibility and safety data, alongside estimates of variance, to inform more adequately powered investigations into SCC-oriented neurofeedback for depression.
Narratives are central to human cognition, yet the brain mechanisms supporting coherent story construction remain incompletely understood. In this study, we used a well-controlled naturalistic paradigm to isolate the neural basis of narrative coherence. Participants watched a foreign-language film with either intact or scrambled English subtitles, disrupting narrative understanding while preserving auditory and visual input. Whole-brain functional connectivity multivariate pattern analysis (fc-MVPA) revealed significant differences between conditions in the ventromedial prefrontal cortex (vmPFC), posterior cingulate cortex (PCC), lateral prefrontal cortex, and right angular gyrus-regions spanning the default mode and frontoparietal control networks. Seed-based correlation analyses showed stronger vmPFC and PCC coupling with lateral temporal and frontoparietal regions during coherent narratives, supporting semantic and contextual integration. In contrast, incoherent narratives increased connectivity between the default mode and attention networks, consistent with elevated cognitive demands. Finally, we observed a functional dissociation within the DMN: vmPFC-PCC coupling was enhanced during incoherent viewing, while PCC-precuneus coupling was stronger during coherent narratives. These results highlight dynamic reconfiguration across large-scale brain networks in support of real-world narrative comprehension.
Resting-state functional connectivity (rsFC) has shown widespread changes in intrinsic functional brain networks in individuals with chronic low back pain (CLBP). However, less is known about rsFC between task-evoked brain networks (i.e., networks formed by regions that are activated to tasks) implicated in cognitive reappraisal and attention regulation in this population. This cross-sectional study analyzed resting-state fMRI and behavioral data from a separate pain regulation task in 184 individuals with CLBP to investigate whether rsFC among ROIs defined from task-based meta-analyses of cognitive reappraisal and attention regulation networks was associated with pain regulation success. We hypothesized that increased rsFC within and between task-based networks would be associated with greater pain regulation success, and that trait cognitive reappraisal and mindfulness would mediate these relationships. Results indicated that increased rsFC between ROIs in the task-based cognitive reappraisal network, including the left ventrolateral prefrontal cortex (vlPFC) and left middle temporal gyrus (MTG), was associated with reduced pain regulation success using either cognitive reappraisal or attention regulation. Conversely, increased rsFC between ROIs in the task-based cognitive reappraisal network left vlFPC and attention regulation network right middle frontal gyrus (MFG) was associated with increased pain regulation success using attention regulation. Examining the relationship between rsFC and self-reported measures of pain, cognition, and emotion, we found that increased rsFC between the task-based cognitive reappraisal network left MTG and attention regulation network right inferior frontal gyrus (IFG) was associated with increased self-reported cognitive distortions. In contrast, increased rsFC between the task-based attention regulation network bilateral vlPFC and left posterior cingulate cortex (PCC) was associated with more habitual use of cognitive reappraisal, lower pain catastrophizing, and lower depression. In a formal mediation analysis, neither trait cognitive reappraisal nor trait mindfulness mediated the relationship between rsFC and pain regulation success. Together, our findings suggest that rsFC between brain regions in task-based cognitive reappraisal and attention regulation networks is related to behavioral outcomes in a separate evoked pain regulation task and may underlie cognition and emotion regulation success in individuals with CLBP.
Neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial biological and clinical heterogeneity, complicating diagnosis, subtype characterization, and prediction of disease progression. We introduce PatientSpace, a multimodal graph-based latent representation framework designed to model neurodegenerative disease heterogeneity using T1-weighted MRI and FDG-PET. PatientSpace is built upon a structured variational autoencoder that integrates multimodal neuroimaging features while organizing patients within a latent space constrained by age, diagnosis, and a consistency regularization term encouraging similarity between neuroimaging phenotypes. This design enables the construction of an interpretable patient graph in which neighborhood relationships reflect biological similarity. Applied to cohorts of cognitively normal individuals, AD, and FTD patients, PatientSpace revealed multiple disease clusters associated with distinct neuroimaging patterns and clinical severity. Diagnostic classification achieved performance comparable to state-of-the-art deep learning models, while graph-based neighborhood inference enabled prediction of structural volumes, metabolic activity, and cognitive severity. Projection of mild cognitive impairment (MCI) subjects from an independent cohort further showed that cluster membership was associated with differential risks of dementia conversion and distinct longitudinal trajectories. Together, these results demonstrate that PatientSpace provides an interpretable framework linking multimodal neuroimaging representations to disease subtypes, patient-level characterization, and progression modeling in neurodegenerative disorders.
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