Frontiers in Neuroimaging

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.

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.

One of the prominent methods in neuroimaging data processing is SSM-PCA, which is based on principal component analysis and allows for the identification of diagnostically significant patterns in the form of statistical maps. We developed software, PIE Toolbox, employs SSM-PCA and classification based on the obtained diagnostic patterns revealed from functional and structural tomographic brain imaging. The program supports the entire analysis pipeline including preprocessing of brain images, diagnostic patterns extraction, building classification models, and prediction based on them. The resulting diagnostic patterns are weighted principal components obtained through SSM-PCA, or their linear combinations. PIE Toolbox allows selection of relevant structural and functional brain patterns, computation of their expression values in regions of interest, classification using support vector machines, and evaluation of model performance via cross-validation. This approach enables the use of patterns as features of intergroup differences for individual diagnosis. The software has been validated on both simulated and ADNI datasets.

Much of a typical individuals mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional Magnetic Resonance Imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience-sampling (rt-fMRI-ES). We analyzed blood-oxygenation-level-dependent signals in real time during a wakeful resting state (n=60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external-attention time-locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation compared to other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendationhttps://osf.io/sd4hu (Date of in-principle acceptance: 07/24/2024; under temporary private embargo)

Alzheimers disease (AD) is an irreversible neurodegenerative disorder that gradually impairs memory, cognition, and behavior, making early diagnosis essential for slowing disease progression and improving patients quality of life. Functional Magnetic Resonance Imaging (fMRI) provides a noninvasive tool to study brain activity, yet many existing diagnostic models rely on black-box architectures that lack interpretability. In this study, we introduce a computational framework that models each anatomical brain region as a Shannon information source, thereby quantifying both the intrinsic information content of regions and the interactions among them. We used kernel density estimation to compute the probability density functions (PDFs) of voxel-level BOLD time series. From these PDFs, we derived regional entropy and pairwise Kullback-Leibler (KL) divergence measures. These measures were used to construct feature spaces representing information dynamics across the brain. We applied the framework to the ADNI resting-state fMRI dataset, which includes cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD subjects. Our findings indicate that entropy values increase with disease progression, while KL-based connectivity networks reveal a progressive loss of inter-regional interactions, especially in frontal, temporal, and parietal lobes. For classification, we trained multilayer perceptrons using voxel BOLD signals, entropy vectors, and KL divergence vectors. Models trained on KL features achieved the highest performance, outperforming both entropy-based and voxel-based approaches. These results demonstrate that the Shannon information source model offers an interpretable and statistically grounded approach for characterizing brain dynamics, while achieving superior diagnostic accuracy. Beyond AD, the proposed framework provides a generalizable tool for studying brain network alterations in neurological and psychiatric disorders.

Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be insufficient for capturing fine-grained representational differences. This challenge becomes particularly relevant in experimental settings and interventions such as neurofeedback training, where voxels are not only measured as neural responses but also used as targets for intervention based on their previously observed activity patterns. In this study, we propose a subject-level searchlight optimization framework that integrates voxel-wise general linear model (GLM)-based univariate analysis with representational similarity analysis (RSA)-based multivariate refinement to identify voxels that are both task-relevant and condition-sensitive. To enhance practical applicability, the framework further incorporates a data-driven hyperparameter tuning step based on Bayesian optimization, enabling efficient identification of high-performing configurations from small pilot datasets, with consistent performance when applied to larger samples. The proposed framework was evaluated using an emotion imagery fMRI dataset with four affective conditions. Results demonstrate that the multivariate refinement improves alignment between empirical and target representational structures compared with univariate selection alone. Compared with a classifier-based voxel selection approach, the RSA-based approach better preserves the representational geometry of emotional states while maintaining discriminative capacity. These findings highlight the effectiveness, efficiency, and robustness of the proposed RSA framework, providing a practical solution for identifying condition-sensitive voxels and supporting more precise multivariate investigation of affective brain states in multi-condition fMRI studies.

SignificanceHigh inter-subject variability and limited reproducibility in functional near-infrared spectroscopy (fNIRS) research may partly reflect global systemic physiology and signal quality differences, possibly distorting task-evoked hemodynamic responses. AimWe investigate how signal quality relates to inter-subject variability in motor-task fNIRS responses and introduce a large, open, multi-task, near whole-head fNIRS dataset with extensive peripheral physiology and short-channel recordings. ApproachFifty-seven participants completed resting-state, motor action, motor imagery, emotion recognition, visual, and auditory tasks during fNIRS recording. Peripheral measures included pulse oximetry, heart rate, blood oxygen saturation, respiration, room temperature, galvanic skin response, electrocardiogram, and electromyography. Signal quality was assessed using the scalp coupling index (SCI), coefficient of variation (CV), signal-to-noise ratio (SNR) and a spectral measure here coined the coupling SNR (cSNR). ResultsQuality metrics were weakly to moderately correlated, except SNR and CV, which showed the expected inverse relationship. All quality metrics were significantly related to channel length and associated with task-related activation estimates. Group-level analyses validated activation in expected task-related regions. ConclusionsThe assessed metrics capture complementary features of fNIRS signal quality and may help explain individual activation differences. The dataset provides a comprehensive, open resource enabling future evaluation of physiological correction methods and confound mitigation.

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmit field (B1+) inhomogeneity remains a major source of bias. Although Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) improves the tissue contrast, residual B1+ effects may persist and may be exacerbated in aging or clinical populations, where anatomical and physiological factors further challenge image quality and preprocessing. The impact of B1+ inhomogeneity on automated quality assessment and morphometric statistical inference remains insufficiently understood. Methods: Submillimeter 7 T MP2RAGE brain acquisitions from carriers of a mitochondrial gene mutation (m.3243A>G) and controls were retrieved from previous studies. Image quality before and after B1+ inhomogeneity correction was assessed by multiple automated pipelines. Case-control morphometric studies, including regional volume and mean cortical thickness, were analyzed in both registration based and deep learning based segmentation frameworks. Changes in image quality metrics (IQMs) and morphometric statistical significance were evaluated to determine the impact of B1+ inhomogeneity correction. Results: Overall image quality rating and metrics sensitive to intensity non-uniformity and topological integrity consistently improved after B1+ inhomogeneity correction. However, its impact on morphometric statistical inferences was strongly method-dependent. Some pipelines showed redistribution of significant regions, whereas others predominantly demonstrated increased effects in sensitivity. Across methods, B1+ inhomogeneity correction altered the findings of morphometric analyses, particularly in cortical regions. Conclusion: Residual B1+ inhomogeneity at 7 T substantially influences both image quality control and morphometric evaluations. Current automated quality control approaches can hardly capture these effects reliably. B1+ inhomogeneity correction will not only improve intensity uniformity, but also change sensitivity of morphometric statistical inferences. To establish reliable morphometric biomarkers at UHF strengths, explicit B1+ correction and customized preprocessing are practically necessary and highly recommended.

While Blood Oxygenation Level-Dependent (BOLD) fMRI remains the gold standard for mapping functional brain networks with MRI, its vascular origins inherently conflate haemodynamic effects with neural activity, limiting its sensitivity in white matter (WM) or its interpretation in neurovascular diseases. Apparent Diffusion Coefficient (ADC) fMRI offers an alternative, diffusion-based contrast that is theoretically more sensitive to neuromorphological coupling and therefore more specific to neuronal activation, though investigated primarily during task-based conditions. This study aimed to comprehensively evaluate the efficacy of isotropic ADC-fMRI in detecting established resting-state networks (RSNs) and to extend this methodology to the investigation of grey-to-white matter (GM-WM) functional connectivity. Our analyses revealed a gradient of ADC detectability shaped by the degree of static functional cohesion and structural tethering of each network. The visual and somatomotor networks, being both highly segregated and strongly anchored to underlying structural pathways, yielded the most robust detection. The default mode network (DMN) and dorsal attention network (DAN) reached group-level significance but with lower effect sizes, and their detection proved fragile across analytical approaches. The frontoparietal network (FPN) and salience network (SAN), whose functional identity is defined by dynamic cross-network reconfiguration, did not reach significance. This gradient partially mirrors the established hierarchy of network segregation observed in BOLD, while further suggesting that ADC sensitivity depends on the structural grounding of each network. Furthermore, ADC demonstrated superior sensitivity to GM-WM functional coupling compared to BOLD. GM-WM functional connectivity profiles derived from ADC were significantly more aligned with underlying structural WM architecture across subjects. Taken together, these findings position isotropic ADC-fMRI as a viable complementary modality to BOLD, offering a more direct window into the neural and structural foundations of brain connectivity.

BackgroundMagnetoencephalography (MEG) is a unique technique in human neuroimaging combining high temporal resolution (millisecond or faster) with moderate spatial resolution (several millimeter). While many software packages for MEG data analysis exist, there is no pipeline developed for the specific purpose of enabling the automated analysis of very large, multi-site datasets. ResultsThe ENIGMA consortium was developed to enable large scale collaborations in the fields of neuroimaging and genetics. To facilitate ENIGMA MEG working group data analysis, we developed the ENIGMA MEG pipeline. The first ENIGMA MEG working group project involves spectral analysis of resting state MEG data, thus our current pipeline is designed to carry out that task. The goals of the ENIGMA MEG pipeline include ease of use, automated processing wherever possible, detailed logging and quality assurance (QA) features, the use of the brain imaging data structure (BIDS) format, anonymized output, and consistent processing across vendors. The pipeline is built using the MNE-Python framework and incorporates a re-trained version of the MEGnet deep neural network algorithm for automated artifact detection. QA tools are designed to enable high throughput evaluation of a large number of subject datasets. All software is open source and available on GitHub (https://github.com/nih-megcore/enigma_MEG). We used our pipeline to process data from three publicly available MEG cohorts, demonstrating its functionality and compatibility with large-scale processing. ConclusionsWhile the current ENIGMA pipeline is limited to resting state data and spectral analysis for the current working group project, the software is highly modularized, allowing straightforward extension to other analysis questions. Further development of the tool to enable connectivity and task-based MEG analysis are planned. The ENIGMA MEG pipeline represents an important first step to augment the existing arsenal of analysis tools, enabling multi-site, high throughput data analysis.

Early detection of schizophrenia (SZ) remains challenging due to the subtlety of early-stage brain alterations and reliance on subjective clinical assessment. We propose a frequency-aware 3D convolutional neural network (CNN) pipeline that integrates NeuroMark-HiFi high-pass spatial filtering with a modified VGGNet3D architecture featuring 3D Laplacian kernel initialization and dilated convolutions. Using the FBIRN dataset (N=311; 150 healthy controls, 161 SZ) with all 53 intrinsic connectivity networks (ICNs) per subject, we evaluate four experimental conditions across two hyperparameter configurations to isolate the contributions of enhanced input representations and frequency-aware model design. Under the optimized configuration, Condition 3 (HiFi + Laplacian initialization) achieved the best mean test accuracy of 75.54% with a peak single-fold accuracy of 87.10%, representing a 5.44% absolute gain over the optimized baseline. These results demonstrate that high-frequency spatial features are more discriminative for SZ classification than raw intensities, and that aligning Laplacian-initialized kernels with HiFi-filtered input creates a beneficial inductive bias--even with a compact model of approximately 1.4M parameters.

There is growing interest in identifying brain function underlying adolescent cognition, personality, and psychopathology. One promising approach is Precision Functional Mapping (PFM) of MRI functional connectivity, a data-intensive method for characterizing individualized brain networks. Foundational studies suggest that PFM can detect stable, task-responsive, and clinically relevant networks. Studies demonstrate that both functional connectivity reliability and network stability improve with increasing data quantity, although benchmark estimates vary across populations, preprocessing pipelines, and MRI acquisition approaches. Accordingly, it is important to understand how PFM performs in adolescent populations and with multi-echo fMRI acquisition. In a case study of eight youth (ages 10-17), we applied PFM to 80-minutes of combined resting-state and task-based fMRI. The resulting networks were highly modular, consistent with adult templates, and without evidence of structural registration artifacts. Functional connectivity reliability compared favorably to prior single-echo studies, with multivariate similarity and ICC estimates showing early stabilization around 10-15 minutes despite continued improvement with additional data. Trait-like stability increased gradually with acquisition time and a Bayesian algorithm (MS-HBM) demonstrated higher stability than Infomap. Across algorithms, stability was greatest in sensory networks (somatomotor, auditory, visual). Furthermore, when evaluating task-based responses to threat and attention paradigms, only the auditory network consistently benefited from individualized mapping over group template networks. These findings suggest that, with constrained scanning time, PFM is especially effective for characterizing sensory and perceptual networks in adolescents. Bridging the methodological divide between deeply sampled individual cases and large-scale developmental studies will require further innovation and validation.

NeuroMark is a fully automated hybrid independent component analysis (ICA) framework designed to extract functional network features that are individually resolved and comparable across different cohorts. By integrating a reliable spatial template with spatially constrained ICA that adapts to each scan, NeuroMark retains the advantages of data-driven decomposition while avoiding limitations of fixed region-of-interest approaches. NeuroMark typically employs direct spatial normalization of fMRI data to a standardized adult EPI template; it remains unclear whether this approach is optimal for populations whose anatomy differs substantially from that of adults. We evaluated two normalization strategies in three large datasets spanning infancy, development, and aging: (1) direct normalization to the adult EPI template (EPInorm), and (2) normalization using an age-specific anatomical T1 template followed by transformation to the adult EPI template (T1toEPInorm). Across all cohorts, average intrinsic connectivity networks derived from EPInorm and T1toEPInorm exhibited very high spatial correspondence (mean {+/-} SD: 0.9966 {+/-} 0.0012 in infants; 0.9947 {+/-} 0.0019 in development; 0.9963 {+/-} 0.0012 in aging). The individual level also showed high similarity, though time courses showed slightly higher consistency than spatial maps (average correlations for time courses: 0.7990-0.9931; average correlations for spatial maps: 0.6879-0.9131). Functional network connectivity (FNC) measures were extremely well preserved across scans (95% of FNC with r > 0.9374 in infants; r > 0.8670 in developmental cohorts; r > 0.9219 in aging), demonstrating the robustness of NeuroMark features to different normalization strategies. Together, these results indicate that NeuroMark yields highly stable functional network features irrespective of whether an age-specific intermediate registration step is incorporated. NeuroMark, along with direct normalization to the adult EPI template, thus provides a robust, efficient, and harmonizable approach for large-scale, multisite, and lifespan neuroimaging studies, facilitating broad comparability across datasets while avoiding potential biases introduced by using multiple age-specific templates within a single study.

Hypoxia-targeted BOLD MRI is a novel technique, which probes oxygenation physiology in response to a controlled transient hypoxia stimulus. In glioblastoma, the signal response is spatially and temporally heterogeneous. We developed a voxel-wise temporal decomposition framework for hypoxia-targeted BOLD MRI that separates the arrival of responses, transition phases, and steady state during controlled isocapnic hypoxia. Twenty healthy controls underwent 3-T BOLD MRI during a double hypoxic step challenge to establish a normative reference. Three patients with newly diagnosed glioblastoma were included as proof-of-concept cases. For each voxel, we estimated response arrival delay (Delaycorr), delay to plateau, delay to return and an O2-normalized steady-state response (HypoxiaSS). Healthy-control maps were used to construct a voxel-wise normative atlas and, for HypoxiaSS, a global-response-adjusted model for patient deviation mapping. In healthy controls, HypoxiaSS showed lower supratentorial between-subject variabilitythan both whole-stimulus comparators (coefficient of variation: 1.77 versus 2.36 for Hypoxiaavg) and higher voxel-level step-to-step agreement (ICC(2,1): median 0.951 versus 0.792 for Hypoxiaavg). Whole-stimulus averaging exhibited a systematic step-2 signal amplification present in 19 of 20 subjects, which was absent from HypoxiaSS. Asingle global response scalar explained a median 72.5% of voxel-wise between-subject variance in HypoxiaSS. In proof-of-concept patient analyses, G-adjusted HypoxiaSS deviation maps and timing maps identified spatially coherentabnormalities that were partly complementary and extended beyond conventional MRI-defined lesion margins.Temporal decomposition improves the stability and interpretability of hypoxia-targeted BOLD MRI and provides a practical framework for population-referenced physiological mapping and atlas-based deviation mapping in glioblastoma.

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.

A common approach for investigating high-level visual cortex with functional magnetic resonance imaging (fMRI) is to define regions of interest (ROIs) in individual participants using functional activation clusters and anatomical landmarks. Although highly productive, this approach requires manual decisions about which clusters correspond to specific canonical regions, limiting reproducibility and posing challenges in populations with lower signal-to-noise ratios, such as children. The Group-Constrained Subject-Specific (GSS) approach reduces this subjectivity by using group-level parcels to constrain subject-specific functional ROI definition. However, the original GSS parcel set provides limited coverage of the occipital place area (OPA) and does not include more recently characterized scene-selective regions. Here, we introduce an updated and expanded set of GSS parcels for scene-selective cortex. Using a larger adult sample and dynamic scene stimuli, we generated updated parcels for OPA, parahippocampal place area (PPA), and retrosplenial complex (RSC), and for the first time, delineated a parcel for a newly discovered scene-selective region in the superior parietal lobule (superior place area; SPA). We evaluated these parcels in independent adult and pediatric datasets by testing whether they improve cross-subject coverage while preserving functional selectivity. The updated OPA parcel increased cross-subject coverage relative to the original parcel by Julian and colleagues. Moreover, ROIs defined using the updated parcels showed equal or greater scene selectivity across OPA, PPA, and RSC, indicating improved functional ROI definition without sacrificing specificity. Across scene-selective regions, the updated parcels reliably identified scene-selective cortex and reproduced canonical response profiles and in pediatric data. These parcels provide more complete and reliable coverage of the scene-processing network, supporting objective and reproducible ROI definition across adult and pediatric fMRI datasets. HighlightsO_LIExpanded group-constrained parcels improve coverage of scene-selective cortex C_LIO_LIDynamic stimuli yield improved cross-subject overlap for OPA C_LIO_LINew parcel introduced for the scene-selective region in the superior parietal lobule, now called superior place area (SPA) C_LIO_LIUpdated parcels reproduce canonical response profiles in adult data C_LIO_LIParcels reliably identify scene-selective voxels in pediatric datasets C_LI

Clinical adoption of new biomedical techniques depends on establishing reference values against which individual patients can be compared. In resting-state functional MRI (rsfMRI), most biomarker research has relied on the case-control paradigm, whose underlying assumptions are often invalid as diseases are frequently heterogeneous, limiting biomarker generalizability. Normative modeling offers a complementary alternative by characterizing individual deviations against a reference population. However, in rsfMRI, normative modeling has been applied almost exclusively to functional connectivity, with limited attention to age trajectories and sex effects. We address these gaps by developing a spatial normative model of four rsfMRI metrics that capture complementary features of the blood-oxygen-level-dependent (BOLD) signal across age and sex. Five publicly available datasets were aggregated to form a sample of 1,978 participants aged 10-30 years. Four metrics were computed for each of 110 grey matter regions: amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and Hurst exponent. A machine-learning model based on hierarchical Bayesian regression with a non-Gaussian likelihood was fitted per metric, modeling non-linear age effects, sex, and multi-site acquisition. Models were well calibrated across all four metrics, with fALFF showing the strongest predictive performance and Hurst exponent the weakest. Normative trajectories varied across brain regions for each metric, but on average, the median of each distribution remained bounded across regions, while the spread was more regionally variable. All four metrics showed predominantly negative slopes with age, indicating a decrease in each metric over the age window. This work provides a normative reference across four rsfMRI metrics that capture distinct features of the BOLD signal, complementing the case-control paradigm and supporting individual-level inference.

Machine-learning models are increasingly used to predict cognitive and clinical outcomes from neuroimaging data, yet challenges in fairness and generalizability remain. Large-scale datasets are often racially and ethnically imbalanced, leading to systematic performance disparities, with models typically achieving higher accuracy for majority populations represented in the training data. In this study, we evaluated whether supervised domain adaptation methods--including balanced weighting, two-stage TrAdaBoost, feature augmentation with SrcOnly prediction, and linear interpolation--can mitigate these biases. Using the ABCD dataset, we assessed whether models trained on 80 MRI measures from White American participants could generalize more effectively to African American participants. All domain adaptation methods reduced prediction error for African American participants, particularly for MRI modalities with large baseline disparities (e.g., structural MRI), while offering limited improvements where initial gaps were smaller (e.g., functional connectivity). Among the approaches, balanced weighting performed best and remained stable and beneficial even when only 10 African American participants were used to adapt the original model trained exclusively on White American participants. These findings suggest that simple, low-cost strategies can effectively reduce cross-ethnic performance gaps and improve equity in predictive neuroimaging, offering a practical path forward for future neuroimaging predictive biomarkers. Significant StatementLarge-scale neuroimaging datasets increasingly enable machine-learning models to predict cognitive and clinical outcomes; however, these datasets are often ethnically/racially imbalanced. As a result, predictive models tend to generalize poorly to underrepresented populations. We demonstrate that, across 80 MRI phenotypes, a class of machine-learning approaches collectively known as supervised domain adaptation can substantially reduce cross-ethnicity disparities in neuroimaging-based cognitive prediction, even when only limited data from underrepresented groups are available. Among the methods evaluated, balanced weighting achieved the best performance while maintaining low computational cost. Together, these findings provide a practical and scalable framework for improving fairness and generalizability in neuroimaging-based machine learning under realistic conditions of ethnic/racial imbalance.

Emerging evidence suggests that saliva cell-free mitochondrial DNA (cf-mtDNA) increases in response to psychosocial and physical stress. Here, we quantified saliva cf-mtDNA changes in response to acute physical and cognitive stressors as well as identifying potential predictors of these responses, while also exploring the potential modulatory effects of transcranial infrared laser stimulation (TILS). In a crossover design, a total of 47 participants (53% female, ages 18-30) underwent up to three experimental sessions, including an exercise stress task and two cognitive stress tasks. Repeated saliva samples were collected for cf-mtDNA and cell-free nuclear DNA (cf-nDNA) quantification, alongside continuous measurement of heart rate, oxygen consumption, and blood pressure. Our results show that average cf-mtDNA levels increased by 90% after baseline during exercise experiments, and in cognitive stress experiments peaked 160% above average baseline levels during the stress task. Inter-individual differences in response trajectories were associated with differences in factors such as fitness, sleep quality, and stress perception. Notably, participants with higher cf-mtDNA elevations during the exercise experiment reported fewer recent stressful incidents, drank alcohol less frequently, had higher maximum VO2 during exercise, and had lower BMI. More dynamic responses to cognitive stress were observed in participants with poorer sleep quality and greater blood pressure reactivity. These findings provide a foundation for larger studies by highlighting the dynamic behavior of saliva cf-mtDNA following physical and cognitive stressors, and by suggesting potential drivers of individual differences in saliva cf-mtDNA stress reactivity.

PURPOSE: The UK7T travelling head dataset was used to characterise the reproducibility of 7T measurements of the susceptibility of the hippocampal subfields, focusing on the Cornu Ammonis (CA1, CA2 and CA3), dentate gyrus (DG), subiculum (SUB), tail of the hippocampus (TAIL) and entorhinal cortex (ERC). METHODS: Susceptibility maps were created from whole-brain 3D single-echo GRE data (TE=20 ms; 0.7 mm isotropic resolution) using Multi-Scale Dipole Inversion. Automatic Segmentation of Hippocampal Subfields (ASHS) was applied to high resolution T1- and T2-weighted images for segmentation. The mean magnetic susceptibility and volume of hippocampal subfields was evaluated in 50 data sets, comprising 5 repeat acquisitions on 10 healthy participants (age 32 + or -6 years; 3 female). RESULTS: Averaging over subjects, susceptibility values spanned an 18ppb range over the hippocampus (ranging from -13.3ppb in DG to 4.7ppb in ERC). Susceptibility values in the larger hippocampal subfields showed a consistent pattern of variation across subjects, being generally more positive in ERC and SUB than in CA1 and more positive in CA1 than in DG and TAIL. The standard deviation of subfield susceptibilities over subjects ranged from 8.2ppb in the TAIL to 1.7ppb in CA1, and the average standard deviation across repeated measurements, which ranges from 1.7 to 4 ppb, was less than half of the inter-participant standard deviation in all subfields. Susceptibility values in the smaller subfields (CA2 and CA3) were more variable, but ICC(2,k) values for all subfields were >0.82. CONCLUSION: The reported data characterises the variation and reproducibility of hippocampal subfield susceptibility measurements at 7T.