Brain Connectivity

Independent component analysis (ICA) is widely used in resting-state fMRI to identify large-scale functional networks; however, existing approaches provide limited means of quantifying how network representations are distributed across independent components. We introduce an entropy-based network integration framework that characterizes the organizational architecture of canonical resting-state networks by quantifying the distribution of ICA-derived functional contributions within Yeo atlas networks. Spatial overlap between independent components and network templates is normalized to generate a probability distribution, from which Shannon entropy and a normalized integration index are derived. The resulting metric provides a continuous measure of network representational integration, ranging from specialized configurations dominated by a small number of components to distributed configurations involving multiple functional modes. The framework was evaluated and validated using resting-state fMRI data from healthy controls, Parkinsons disease patients with normal cognition, and Parkinsons disease patients with mild cognitive impairment. Global entropy and integration measures were complemented by network-specific analyses, dominance profiling, principal component analysis (PCA), and multivariate centroid-distance assessments. The proposed framework revealed selective alterations in Ventral Attention and Limbic network organization associated with cognitive-status differences, while preserving overall within-group heterogeneity. Group-wise PCA independently further identified these networks as major contributors to altered network organization, and centroid-distance analyses demonstrated that observed differences reflected coherent shifts in network architecture rather than increased variability. By quantifying the distribution of network representations across ICA-derived functional modes, this framework provides a simple, interpretable, and generalizable measure of large-scale brain organization, offering a complementary approach for studying network reorganization in health and disease.

BACKGROUND: Cognitive flexibility represents a crucial function in adapting to new environments. In this study we examined the ecological validity of a cognitive flexibility task by studying its relationship with individual traits (dogmatism, dependence on routines and perspective taking). Second, we investigated whether global and local structural brain connectivity properties were related to cognitive flexibility as well as associated traits and their possible age-related differences. METHOD: Thirty-eight young (18-35 years) and thirty-seven older (60-85 years) healthy participants took part in an MRI protocol including a Diffusion Weighted Imaging (DWI) sequence. Participants also performed a Rule-Switching task to measure cognitive flexibility performance and filled in questionnaires assessing dogmatism, dependence on routines and perspective taking. RESULTS: A higher cognitive flexibility was related to lower dogmatism and lower dependence on routines only in young adults. In relation to structural connectivity, we found that: a) global and local connectivity properties negatively predicted dogmatism levels in the full sample, b) local connectivity properties of the inferior frontal gyrus (IFG) positively predicted performance in cognitive flexibility performance in the full sample and in older adults, and c) connectivity between left inferior parietal lobule (IPL) and left putamen negatively predicted dogmatism in older adults. DISCUSSION: A deeper understanding of the shaping of structural networks supports a better understanding of cognitive flexibility and dogmatism in a highly dynamic world.

Individual brains are unique in structure and function. Functional differences are captured by neural fingerprints, which reflect individual differences in behavior and cognition as well as group-level changes related to neurodegenerative diseases. Most research efforts so far have focused on fingerprints com-prising full functional connectomes. However, the high dimensionality of the connectomes can increase computational load and impede performance of machine learning methods in potential applications. A low-dimensional alternative that retains individual features of the full connectomes would thus be beneficial. The present study employed latent-noise Bayesian Reduced Rank Regression (lnBRRR) to learn low-dimensional latent spaces that capture individual features in functional connectivity and power spectral density data derived from MEG recordings. LnBRRR performance was assessed with low training set sizes (N=20-44), and against principal component analysis and linear discriminant analysis. Model performance was also assessed with task data, and the solutions were compared across task conditions with cosine similarity to establish whether individual features are altered by different cognitive processes. LnBRRR captured generalizable individual patterns already at N=20 but N=30-35 was needed to reach optimal test accuracies and to prevent potential overfitting. The model also achieved comparable performance to the alternative models. Latent fingerprints derived from task data attained comparable performance to resting-state latent fingerprints, and lnBRRR solutions were shown to generalize across conditions. Additionally, the model solutions for power spectral density data were discovered to be notably similar, yet differently rotated, over task conditions, suggesting that similar patterns of individual features were captured by the model regardless of the task condition. Altogether, the present results highlight lnBRRR as a potential tool for neuroimaging data analysis and demonstrate that individual differences in power spectral density are largely intrinsic and unaffected by varying cognitive processes.

Psychosis as a symptom manifests in schizophenia and bipolar disorder, two highly heterogeneous psychiatric illnesses with overlapping clinical manifestations. Resting-state functional Magnetic Resonance Imaging (rsfMRI), represents a promising tool for identifying objective biomarkers of functional brain alterations to aid differential diagnosis. In this work, we comparatively evaluate multiple rs-fMRI representations for differentiating schizophrenia and bipolar disorder using intrinsic connectivity network (ICN) temporal profiles and several functional network connectivity (FNC) approaches, including static, dynamic, and high-order connectivity analyses. The study was conducted on a cohort of 371 subjects with psychosis, while evaluation was performed using a separate held-out cohort of 315 subjects. We investigated convolutional neural network architectures applied to ICN temporal profiles, spectrograms, and scalograms, alongside classical machine learning models trained on connectivity-derived features. Across the evaluated approaches, ICN temporal profiles provided the most consistent discriminative performance, with a 1D convolutional neural network achieving the strongest overall results under the benchmark protocol. Among connectivity-based methods, static functional connectivity generally outperformed dynamic and high-order representations, suggesting that increased representational complexity did not necessarily translate into improved generalization. Although the obtained classification performance remained modest, the results highlight the challenges of robust psychosis differentiation using rs-fMRI while emphasizing the relative stability of low-order connectivity representations and temporal ICN features. These findings contribute to ongoing efforts toward reproducible and interpretable neuroimaging biomarkers for psychiatric disorders.

Post-stroke semantic aphasia is characterised by multimodal semantic deficits and reflects disruption of a distributed brain network spanning frontal and temporal regions. Connectivity gradients, which capture key dimensions of whole-brain variation in functional connectivity, offer a promising framework for understanding the global impact of stroke on brain function. This study investigated whether changes in connectivity gradients following stroke can explain semantic aphasia deficits. First, we evaluated whether lesion-location and lesion-load information from structural MRI could predict the gradient changes observed in resting-state fMRI, as a proof-of-principle analysis. Second, we tested whether simulated gradient changes predict the severity of semantic impairment. Results show that post-stroke gradient changes simulated from structural MRI are correlated with actual changes in resting-state fMRI, particularly for the principal gradient that separates unimodal and heteromodal regions. Semantic deficits were related to simulated connectivity changes along this gradient: left prefrontal areas involved in controlled semantic retrieval exhibited stronger connectivity to unimodal cortex in patients with more severe deficits. Semantic deficits also correlated with changes in the second gradient, which distinguishes visual and motor cortex. Particularly, the right parahippocampal gyrus, typically visually biased--showed reduced visual connectivity in more impaired patients. These results help explain controlled semantic retrieval deficits in semantic aphasia. More broadly, the findings suggest that functional connectivity gradients capture post-stroke reorganisation of global brain networks linked to cognitive impairment, and that these changes can be estimated from structural MRI alone, enhancing clinical utility of gradient-based approaches. HighlightsO_LIFunctional connectivity gradients explain the multimodal impairments in semantic aphasia from a dimensional perspective, using the unimodal-transmodal and motor-visual axes. C_LIO_LIPost-stroke functional changes are explored through alterations in connectivity gradient patterns. C_LIO_LICortical lesion information from structural MRI can be used to simulate changes in connectivity gradients, offering potential clinical relevance. C_LI

Autism spectrum disorder (ASD) is a heterogeneous developmental condition characterized by repetitive behaviors and social communication difficulties. The reward network has been specifically implicated in the social deficits associated with ASD for several decades. While modern neuroimaging techniques enable investigation of this primarily subcortical network, task-based fMRI studies have yielded inconsistent findings, largely due to insufficient sample sizes and heterogeneous reward paradigms. More recently the brain at rest has been leveraged to examine reward network connectivity in large-scale datasets. While previous studies have identified associations between network organization, diagnostic group, and individual-level clinical variables, these findings have largely remained at the trend level. However, prior research has focused exclusively on static connectivity patterns, neglecting the temporal dynamics inherent in brain activity. In this study, we analyzed a large multi-site fMRI dataset (ABIDE I) to examine reward network dynamics in individuals with ASD, with particular emphasis on individual-level variability and its relationship to clinical phenotypes. Following an initial assessment of static connectivity, we employed a Hidden Markov Model (HMM) as our primary analysis method, alongside a sliding window approach with subsequent clustering as a secondary validation step, to characterize the temporal properties of reward network activity. We observed a consistent association between greater occupancy of the most sparsely connected network state and milder verbal communication symptoms, as measured by the Autism Diagnostic Interview-Revised (ADI-R), across both methods. Notably, traditional group-level comparisons between ASD and control groups revealed limited differences, underscoring the importance of individual-level characterization in this heterogeneous condition. These findings demonstrate that temporal dynamics of reward network connectivity capture clinically meaningful variation in ASD beyond static connectivity measures, supporting the value of dynamic approaches for understanding neurodevelopmental disorders.

INTRODUCTION: Early detection of Alzheimer's disease (AD) remains challenging. EEG offers a scalable, non-invasive tool for patient stratification, but its relationship to ATN-defined staging is poorly understood. METHODS: EEG was recorded in 60 participants (SCD, MCI--, MCI+, N=20 per group) using a battery of tasks (N-back, auditory oddball, 40 Hz ASSR, resting-state). Event-related, spectral, connectivity, and complexity features were extracted, compared across groups, correlated with CSF and plasma biomarkers, and evaluated for classification performance. RESULTS: Multiple EEG features showed discriminatory power among groups and correlated with amyloid and tau biomarkers. MCI-- showed a cortical hyperexcitability profile. EEG added no value for ATN-based discrimination where plasma pTau217 performed near ceiling (AUC=0.96--0.99), but uniquely separated SCD from MCI (EEG AUC {approx} 0.72--0.75) where plasma biomarkers failed (AUC{approx} 0.32--0.33). DISCUSSION: EEG biomarkers capture ATN-stage-dependent neurophysiological signatures, support a non-monotonic model of AD progression, and show promise as a first screening tool where plasma biomarkers are uninformative.

Introduction The superior longitudinal fasciculus (SLF) is a major association fiber bundle implicated in cognition, visuospatial attention, language, and motor control, and its impairment is linked to several neurological and neuropsychiatric disorders. This proof-of-concept study was performed with three main objectives in healthy adults. First, to fuse whole brain spectroscopic (WBSI) and diffusion MRI (dMRI) derived parametric maps along the SLF I and II segments to quantify their spatial concordance, second, to evaluate regional metabolite concentrations and microstructural properties along these trajectories and finally, to determine the relationships between the WBSI and dMRI parameters within these segments. Methods Ten healthy adults (4F, 6M; mean age 31.4 {+/-} 7.53 years) underwent 3T MRI including multi-shell high angular resolution diffusion imaging and WBSI. After preprocessing and non-linear co-registration, WBSI-derived white matter metabolite maps and neurite orientation dispersion and density imaging (NODDI) / diffusion tensor imaging (DTI) derived parametric maps were spatially aligned and projected along the centroid of reconstructed SLF I and II segments divided into 20 discrete, anatomically contiguous sections. Results A strong spatial alignment between WBSI and dMRI imaging modalities was confirmed by mutual information and Pearson's correlation analyses. Intra-subject repeatability, as assessed from a single participant scanned three times, demonstrated high tract reconstruction reliability (mean Dice similarity coefficients >0.79; track density-weighted Dice >0.97) and acceptable intra-subject coefficients of variation. Inter-subject coefficients of variation were within acceptable ranges ({approx}3-17%) for most parameters, with free water fraction (fiso) exhibiting relatively higher variability. Single and multivariate regression analyses revealed significant associations between WBSI and dMRI tract profiles: choline/creatine (Cho/Cr) and choline/ N-acetyl aspartate (Cho/NAA) ratios showed positive linear associations with intra-cellular volume fraction (ficvf) and fractional anisotropy (FA), and negative associations with mean diffusivity (MD) along bilateral SLF I, with ficvf and MD identified as the strongest combined predictors of metabolite ratios. Conclusion Co-localization/fusion of WBSI and NODDI/DTI data into one framework offers a reliable, user-independent way for mapping regional metabolite and microstructural alterations along the path of SLF. Moving forward, this image processing pipeline has the potential to enhance diagnosis and clinical assessment of neurological disorders linked to SLF damage.

This study explores the nonlinear developmental trajectories of brain networks in neurotypically developing infants during their first six months. Using a longitudinal dataset of 137 resting-state functional MRI scans from 74 infants, we analyzed five spatial metrics across 13 intrinsic connectivity networks, including motor, visual, subcortical, and prefrontal networks. A cubic model was specifically employed to capture distinct linear and non-linear trends in the networks developmental patterns, allowing for the identification of significant differences in cubic, quadratic, and linear slope parameters across networks. This model choice was driven by the need to examine how specific non-linear components (e.g., inflection points and acceleration rates) uniquely characterize each networks trajectory, which a generalized approach might smooth out without pinpointing such network-specific features. Notably, the subcortical network exhibited a distinct cubic growth pattern, while secondary motor and visual networks showed pronounced quadratic variations, suggesting network-specific shifts in spatial organization and connectivity. These findings highlight the unique maturation timelines and interactions between functional systems, such as early sensory-motor coordination and later cognitive integration. The results underscore the importance of network-specific growth patterns, providing deeper insights into how infant brain networks evolve and interact to support emerging cognitive and behavioral functions.

Tourette Syndrome (TS) is a neurodevelopmental hyperkinetic disorder characterised by involuntary tics. These tics are thought to arise due to hyperexcitability of the motor cortex due to disinhibition within the cortico-striato-thalamo-cortical pathway. Given the involvement of motor circuits in this disorder, we explored whether there is a difference in the oscillatory dynamics of voluntary movements in people with tic disorders. We recorded optically pumped magnetometer magnetoencephalography during cued voluntary finger abductions in individuals with tic disorders and age- and sex-matched neurotypical controls. We analysed the data both using conventional time frequency analysis and using hidden Markov modelling to explore the difference in beta burst characteristics and dynamics. Whilst no differences were seen between groups during conventional analysis, we demonstrated an increase in beta burst duration during the post-movement beta rebound within the contralateral motor cortex in individuals with TS suggestive of increased inhibition following movement. We also show evidence of increased contralateral sensorimotor functional connectivity and reduced connectivity from and between frontal regions, as measured using coincident beta bursts. This is suggestive of disrupted functional connectivity within frontal control networks in individuals with TS.

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

Moderate-to-severe traumatic brain injury (msTBI) is recognized as a chronic and evolving neurological condition characterized by progressive structural brain changes and persistent cognitive impairment. While prior studies have demonstrated widespread atrophy following msTBI, less is known regarding the longitudinal trajectory of gray matter (GM) changes during recovery and post-rehabilitation. The current study used longitudinal voxel-based morphometry (VBM) to characterize GM volume changes over a period of 9 months, in individuals with msTBI relative to healthy controls (HC). Associations between regional GM volume and neuropsychological functioning were examined. Twenty-eight participants (14 msTBI, 14 HC) completed MRI and neuropsychological assessments across three timepoints spanning outpatient rehabilitation and follow-up. Longitudinal VBM analyses revealed significant group and time interactions within subcortical and limbic regions. Relative to HC, individuals with msTBI showed lower GM volume in these regions at baseline, with trajectories that converged toward HC values (right hippocampus) or increased relative to HC over the rehabilitation period (bilateral pulvinar), whereas the right amygdala and inferior cerebellar vermis remained persistently reduced. Significant longitudinal improvements in memory and psychomotor speed during the rehabilitation period were demonstrated in msTBI. Greater (preserved) GM volume within the right hippocampus, thalamus, and bilateral pulvinar was associated with better performance across measures of verbal memory, processing speed, executive functioning, and cognitive flexibility. These findings suggest that msTBI is associated with dynamic structural brain changes involving subcortical, limbic, and cerebellar networks, and that the rehabilitation period was accompanied by relative volumetric stabilization in these regions and by meaningful cognitive improvement.

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD), yet variability in treatment response remains a significant challenge. Network control theory provides a framework to quantify how brain networks facilitate state transitions, but prior work has focused primarily on node level metrics. Here, I investigate whether edge based controllability of the structural connectome is associated with rTMS outcomes. Twenty five patients with treatment-resistant depression underwent diffusion MRI prior to a five week course of high frequency rTMS targeting the dorsolateral prefrontal cortex. Structural connectomes were constructed using MRtrix3 and the Destrieux atlas, and edge based controllability metrics were computed at baseline. Controllability of specific middle frontal gyrus centered edges showed significant associations with changes in HAMD-24 scores, including connections to the superior frontal gyrus, hippocampus, angular gyrus, and orbital gyrus (r = 0.470-0.597, p < 0.05). These findings suggest that edge based controllability captures circuit level properties relevant to treatment response and may inform personalized neuromodulation strategies.

Background: Neuroimaging studies implicate network alterations in functional motor disorder (FND-motor), yet white matter remains poorly characterized. Objectives: To characterize white matter microstructure in FND-motor relative to healthy (HCs) and psychiatric (PCs) controls and examine symptom associations. Methods: Fifty individuals with FND-motor, 50 age- and sex-matched HCs, and 50 PCs matched on age, sex, depression, anxiety, and post-traumatic stress disorder severity underwent multi-shell diffusion MRI. Voxel-based analyses examined whole-brain white matter using diffusion tensor imaging (fractional anisotropy [FA], mean diffusivity [MD]) and neurite orientation dispersion and density imaging (NODDI) (neurite density index [NDI], orientation dispersion index, and free water fraction [FWF]) metrics. Cross-metric convergence was characterized using atlas-based tract overlap analyses and probabilistic tractography. Associations with FND symptoms and transdiagnostic physical symptoms were also evaluated. Results: Compared with HCs, FND-motor showed higher FA/NDI and lower MD/FWF, predominantly in the middle cerebellar peduncle. Compared with PCs, differences were limited to lower MD/FWF, involving the corpus callosum, middle cerebellar peduncle, and left inferior longitudinal fasciculus. Greater FND symptom severity was associated with a lower FA/NDI and higher MD/FWF in the corpus callosum and right-lateralized association and projection pathways, whereas greater transdiagnostic physical symptom burden across FND-motor and PCs was associated with higher FA and lower MD/FWF in the middle cerebellar peduncle. Conclusions: This study provides a comprehensive multi-metric diffusion-weighted characterization of white matter microstructure in FND-motor relative to both HCs and PCs - highlighting cortico-cerebellar connections via the middle cerebellar peduncle as distinct in FND-motor and associated transdiagnostically with physical symptom burden.

Resting-state functional MRI (rs-fMRI) studies in schizophrenia commonly rely on normalization to volumetric templates and fixed atlas parcellations derived from neurotypical populations. While these approaches enable group-level comparisons, they may obscure individual variation in cortical organization and intrinsic brain dynamics. In this study, we compared four preprocessing and parcellation strategies across two independent schizophrenia cohorts (MRI site 1, n=159; MRI site 2, n=255) to evaluate how analytic choices affect static functional connectivity and dynamic quasi-periodic pattern (QPP) measures, including default mode-dorsal attention network opposition, QPP component rank, explained variance, event rate, and associations with PANSS symptom severity. Across datasets, individualized surface-based parcellation (IndiPar) consistently detected more pronounced QPP dynamics, stronger default mode / dorsal attention network opposition, and greater explained variance of QPPs relative to atlas-based pipelines. IndiPar also produced larger and more reproducible patient-control differences in functional connectivity and QPP event-rate measures, suggesting improved sensitivity through preservation of subject-specific organization. IndiPar additionally detected a significantly increased QPP event rate and more symptom associations in patients in the larger dataset. However, associations between fMRI measures and symptom severity showed limited stability across cohorts. These findings extend previous reports of altered resting-state activity in schizophrenia, and demonstrate that preprocessing and parcellation choices substantially influence both static and dynamic rs-fMRI results. Individualized surface-based parcellation appears to better preserve subject-specific variability and improves detection of intrinsic brain dynamics. At the same time, the limited cross-dataset replication of symptom associations highlights the challenges of deriving stable brain-symptom relationships from heterogeneous psychiatric cohorts.

Functional activity in response to increasing task difficulty (BOLD-modulation) in regions of the default mode network (DMN/task-negative) and multiple demand network (MDN/task-positive) shows age-related decline, which is associated with reduced cognitive performance. While BOLD-modulation in the DMN begins to decline in middle-age, white matter structural connectivity declines earlier in MDN regions. We conjecture that to maintain executive function (EF) performance with aging, altered BOLD-modulation in DMN during task engagement serves as a compensatory reaction to structural decline in MDN. To test this possibility, functionally-guided tractography was applied in 160 healthy adults aged 20-94 to locate white matter connecting MDN and/or DMN regions that were active during a distance judgement paradigm. Specifically, analyses examined the effects of 1) age on white matter tracts (fractional anisotropy; FA); 2) structure-function association between FA and BOLD-modulation; and 3) age, positive/negative BOLD-modulation, and quadratic FA on EF. Age-related decline was found in one MDN tract (U-shaped tract) and a significant structure-function association was found in inferior fronto-occipital fasciculus (IFOF). Additionally, an interaction effect between quadratic frontal-insular (U-shaped) tract FA, age, and positive/negative BOLD-modulation was found for inhibition performance, supporting the hypothesis of compensatory functional activity effects on executive function for older adults with altered white matter microstructure.

The noradrenergic locus coeruleus (LC) and the cholinergic nucleus basalis of Meynert (nBM) are key hubs of ascending neuromodulatory systems that shape large-scale brain dynamics. However, the behavioral relevance of the structural and functional connectivity between these nuclei remains poorly understood. Here, we investigated whether LC-nBM structural or functional connectivity is related to cognitive-motor dual-task performance in healthy younger and older adults. Fifty-four participants (36 older, 18 younger) underwent diffusion MRI, resting-state fMRI, and behavioral assessment using an MRI-compatible cognitive-motor dual-task paradigm. LC-nBM structural connectivity was estimated using tractography, whereas resting-state functional connectivity was quantified as Fisher z-transformed correlations between LC and nBM time series. LC-nBM structural connectivity was better explained by a quadratic rather than a linear or cubic age model, indicating non-linear age-related variation, whereas functional connectivity showed no significant age-related association. Higher LC-nBM structural connectivity was associated with greater cognitive dual-task cost, but not with motor dual-task cost or single- or dual-task reaction times. This association was not moderated by age group and was not statistically explained by attentional and executive performance as measured by the Test of Attentional Performance. These findings suggest that LC-nBM structural connectivity is selectively associated with cognitive-motor interference, potentially reflecting a neuromodulatory pathway that constrains the balance between task-specific stabilization and flexible cross-domain coordination during a cognitive-motor dual-tasking.

Background Chemotherapy-related cognitive impairment is a well-documented concern among cancer survivors, yet the neural mechanisms underlying deficits in cognitive control remain poorly understood. This study examined group differences in brain activation during a flanker task using functional MRI (fMRI) between chemotherapy-exposed participants and healthy controls. Methods Participants (21 survivors (24.9 years old; 71.4 % female; 15 years from diagnosis) and 21 healthy controls (26.7 years old; 61.9 % female) completed a flanker task during fMRI, with congruent and incongruent conditions. Reaction time, accuracy, and Flanker scores were collected. Whole-brain group comparisons were performed for congruent, incongruent, and incongruent > congruent contrasts. Associations between the incongruent > congruent contrast and cognitive performance were examined. Results Compared to controls, the Chemo group had longer reaction times in both congruent and incongruent conditions (p < .001) and lower NIH Flanker scores (p = .01), with no differences in accuracy. They showed reduced activation in the bilateral inferior frontal gyri, supplementary motor area, and bilateral caudate, but greater activation in the right inferior temporal and cerebellar regions. The incongruent > congruent contrast correlated with increased activation in the orbitofrontal cortex, inferior temporal gyri, and fusiform gyrus with cognitive performance. Conclusions Chemotherapy-exposed participants showed cognitive control deficits and altered neural activation during a flanker task, indicating disrupted recruitment of frontoparietal and subcortical regions key for conflict processing. These findings improve understanding of neural causes of chemotherapy-related cognitive impairment and may help identify at-risk survivors and guide personalized rehabilitation.

Neurofeedback (NF) is a promising method for helping individuals overcome choking under pressure. Identifying relevant neural biomarkers is crucial for developing effective NF. In this exploratory study, we aimed to investigate changes in prefrontal hemodynamic signals associated with golf-putting performance under psychological pressure using functional near-infrared spectroscopy. Participants engaged in a one-on-one golf-putting task against an experimenter, with monetary rewards introduced to induce psychological pressure. This manipulation successfully elicited psychological pressure, leading to impaired performance in some participants. Based on performance changes between the practice and competition sessions, participants were categorized into a non-choking group (performance improved) and a choking group (performance declined). Statistical analysis revealed significantly greater increases in prefrontal activation from practice to competition in the non-choking group than in the choking group, especially in the left superior frontal gyrus. Furthermore, moderate but statistically nonsignificant negative correlations were observed between changes in activation in this region and changes in putting error, indicating that greater activation increases tended to accompany less performance deterioration or greater performance improvement. These exploratory findings suggest that the left superior frontal gyrus warrants further investigation as a candidate biomarker for NF interventions aimed at mitigating choking under pressure.

Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional linear frameworks attempting to model synergistic risk. While various loci are identified as contributing to neurocognitive disparities, the emergent phenotypic expression and associated predictive value relative to standard clinical baselines require further investigation. To facilitate dimensional reduction of complex genetic data into identifiable phenotypes, Generalized Low Rank Modeling (GLRM) and K-means clustering are applied to participant data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The utility of these derived archetypes and clusters is assessed, stratifying variance for Mini-Mental State Examination (MMSE) performance. Utilizing generalized additive modeling (GAM) and partial eta squared (p2) effect size, the derived genetic features are compared with other predictors including age, educational attainment, gender, and raw, genetic variant carriage dimensions. Results & Conclusion: In accordance with the hypothesized empirical regularity, age and education persist as primary predictors of MMSE performance. The unsupervised machine learning pipeline successfully identified a composite genetic cluster that emerged as an influential predictor in terms of relative magnitude (p2). Centroid analysis of the GLRM subspace indicated that a particular sub-population (Cluster 2) - defined by a substantial weighting on the EPHA1 target - demonstrated a statistically significant association with MMSE scores, relative to cluster 3. These results suggest that data-driven genetic feature engineering provides an interpretable basis for inference regarding variance in global cognition. By discovering multivariate genetic architecture, this modeling approach captures complexity often missed by individual clinical variable modeling. Such findings implicate the utility of interpretable machine learning for translational dementia research and predictive clinical stratification.