Cross-disorder research and replication of neuroimaging findings remains scarce. Social dysfunction is an early manifestation across diverse neuropsychiatric disorders that may relate to altered default mode network (DMN) integrity. This study aimed to replicate previous findings linking social dysfunction with diminished resting-state DMN functional connectivity and altered task-based DMN functional activation in response to emotional faces across schizophrenia (SZ), Alzheimer’s disease (AD), and healthy controls (HC), and to extend these findings to major depressive disorder (MDD). Resting-state fMRI and task-based fMRI data on implicit facial emotional processing were acquired in an overlapping cohort (resting-state fMRI: N=167; SZ=32, MDD=44, AD=29, HC=62. Task-based fMRI: N=152; SZ=30, MDD=42, AD=26, HC=54). Additionally, mega-analyses (N=317 for resting-state fMRI; N=291 for task-based fMRI) of the current and a prior independent sample were conducted. Social dysfunction was indexed with the Social Functioning Scale (SFS) and the De Jong-Gierveld Loneliness (LON) scale. The association between higher mean SFS+LON social dysfunction scores and diminished DMN connectivity within the dorsomedial prefrontal cortex across SZ/AD/HC participants was replicated, and extended to MDD patients. Similar observations within the dorsomedial and rostromedial prefrontal cortex were found in the mega-analysis. Associations between social dysfunction and DMN activation in response to sad and happy faces were not replicated or found in the mega-analysis. To conclude, diminished dorsomedial prefrontal cortex DMN connectivity emerged as a transdiagnostic neurobiological marker for social dysfunction, suggesting a potential treatment target for precision medicine approaches. DMN functional responses to emotional faces may not be a sensitive biomarker for social dysfunction.

Background Autism is characterized by social-communicative difficulties, with sex differences in symptom presentation. Social functioning is inherently dynamic, however, many neuroimaging studies rely on static, time-averaged approaches that obscure time-varying network interactions, potentially limiting our ability to capture the dynamic processes underlying social cognition. The fusiform gyrus (FFG), central to face and social perception, shows differences in functional connectivity in autism, yet is rarely examined dynamically or as a spatially heterogeneous structure. Here, we investigate the dynamic functional connectivity of FFG subregions in terms of their large-scale network configurations as a function of diagnosis and sex. Methods We applied micro co-activation patterns analysis (μCAPs) to resting-state fMRI data from 286 autistic individuals (208:78 males:females) and 228 non-autistic individuals (146:82 males:females), aged 6-30 years, from the EU-AIMS LEAP dataset. μCAPs were identified using k-means clustering with FFG as the seed, and connectopic mapping positioned each μCAP along the principal connectivity gradient. We quantified μCAPs occurrence and further examined dwell time, transition probabilities, and spatial extent, along with associations with social functioning. Results Six μCAPs mapped onto distinct FFG subregions along a posterior-anterior axis. A significant sex-by-diagnosis interaction emerged for a default mode network (DMN)-related μCAP. Non-autistic females exhibited significantly more frequent occurrences, longer dwell times and distinct transition dynamics compared to males, while no sex difference was observed in autism. The spatial extent of this μCAP showed a reversal of typical sex effects. Conclusions Autism is associated with an attenuation and reversal of typical sex differences in the functional configuration and spatial extent of FFG-DMN coupling, indicating that neural signatures of social-cognitive functions are sex-specific and dynamic. These findings suggest that sex is a neurobiologically meaningful dimension of heterogeneity in autism, expressed in dynamic network organization.

The excitatory/inhibitory (E/I) imbalance theory suggests that excitatory and inhibitory alterations underlies autism characteristics. However, genetic underpinnings of this imbalance and its impact on brain function and behavior remains unclear. We explored causal links between glutamate and GABA gene-set polygenic scores (PGS) for autism and core autism characteristics, putting particular attention on the restricted- and repetitive behaviors (RRBs) domain by including functional activity (fMRI) during inhibitory control (in the anterior cingulate cortex (ACC) and striatum). Causal links between genes, brain and behavior was evaluated using Bayesian Constraint-based Causal Discovery (BCCD) algorithms, to build causal models of these relationships in a discovery sample (LEAP cohort: autistic = 343, neurotypical = 253) and two generalization cohorts with partially overlapping measures (TACTICS cohort: autistic = 60, neurotypical = 100, Simon Simplex Collection: autistic = 2756). In the discovery sample, we found a causal link between glutamate PGS and core clinical characteristics of autism, particularly the communication domain (Autism Diagnostic Interview-Revised) in autistic participants, with 95% reliability. We did not find links between functional activity during inhibitory control and other measures. For one generalization cohort, we further report on the impact of 1H-MRS measures of glutamate, identifying a causal link between GABA autism PGS on ACC glutamate concentrations. Not all links were identified in the generalization cohorts, which may be due to clinical and genetic differences between the cohorts. While our results reinforce the previously found association between glutamate genes and core clinical autism behaviors, task-based functional activity may not be causally related to RRBs.

Realigning structural MR images to stereotactic AC-PC space is a standard procedure that enhances anatomical consistency both within and across neuroimaging studies, allowing for precise spatial localization and reliable cross-subject comparisons in both clinical and research contexts. However, different versions of AC-PC spaces, stemming from varying definitions of the AC-PC axis as outlined in common stereotactic atlases like Talairach and Schaltenbrand, often lead to discrepancies between the neurosurgical and neuroimaging communities. Manual realignment of structural MR images to the clinically used version of the AC-PC plane is often necessary to validate the results of automated methods or to realign the image when these methods fail. Furthermore, manual realignment provides a critical ‘ground truth’ for the development of new automated tools. However, such manual interventions are typically performed in a non-standardised manner by domain experts who possess the specialized knowledge of neuroanatomy and neuroimaging required to ensure accurate alignment. To address these challenges, we have developed and validated a standardized protocol for manually realigning structural MR images to the clinically used Schaltenbrand AC-PC plane, using a set of visual criteria to ensure accurate realignment. This protocol can be used to manually align images, verify results, validate existing automated methods, or generate ground truth data for developing new automated techniques.

Group-mean comparisons often identify atypical functional connectivity in autism, but it remains unclear whether these findings consistently manifest at the individual level. Here we use normative modeling to quantify the interindividual heterogeneity of atypical functional connectivity across multiple brain scales using multicenter resting-state functional magnetic resonance imaging data from 1,824 participants (796 autistic individuals and 1,028 neurotypical controls) in a cross-sectional study across 32 sites. We find that no single functional connectivity estimate showed extreme deviation from normative expectations in more than 4% of people in either group. However, these deviations converged on common regions and networks in autistic people, who showed up to double the level of overlap compared with controls. Specifically, autistic participants demonstrated convergent hypoconnectivity in sensorimotor and attention regions and convergent hyperconnectivity between frontoparietal and default mode networks. Functional connectivity deviation patterns significantly predicted social and cognitive abilities. These findings demonstrate that autism exhibits scale-dependent heterogeneity, characterized by normative variability at the connection level but significant convergence at regional and network scales. These convergent regions and networks may be used to identify targets for individualized therapeutic development.

The hippocampus and amygdala play essential roles in human cognition and emotion, through their extensive connectivity with other brain regions and close interaction between them. Uncovering the functional organization of the hippocampus–amygdala complex and its relationship with neurotransmitter distribution can enhance our understanding of their biological functionality, and provide a basis for further exploration of the clinical relevance. An emerging functional connectivity analysis method termed connectopic mapping, may offer a novel approach to characterize this functional organization. In this study, we applied connectopic mapping to the hippocampus-amygdala complex, testing its utility with resting-state functional magnetic resonance imaging (fMRI) scans of two independent datasets: one comprising healthy individuals (N = 410) and another comprising a psychiatric cohort (N = 367). The spatial organization of derived gradient maps was compared to 18 positron emission tomography (PET) or single photon emission computed tomography (SPECT) scan templates for different neurotransmitter systems. Individual gradient–neurotransmitter similarity indices were correlated with mental health outcomes. Our analyses identified six distinct gradient maps in both datasets. The third-order gradients showed stable similarity with 5-HT1A receptor maps across various resting-state scans. Similarities were also observed between gradient maps and the distribution patterns of neurotransmitters within the dopaminergic system. Individual gradient-to-5-HT1A similarity was positively correlated with depressive severity and anxiety sensitivity, highlighting the psychopathological relevance. These findings demonstrate that across the psychiatric continuum, connectopic mapping is a powerful tool for exploring the relationship between functional connectivity organization and neurotransmitter distribution, showing potential as a comprehensive transdiagnostic biomarker.

Diffusion MRI (dMRI) is the primary tool for in vivo investigation of brain microstructure. However, dominant analytical methods rely on biophysical models with specific assumptions and large computational requirements, limiting their biological accuracy and generalisability. Here, we introduce SSDiff, a self-supervised transformer-based foundation model that summarises the entire dMRI scan into a compact set of biologically interpretable latent features, without a priori model assumptions. Trained on ~80,000 scans from diverse populations, protocols, and ages, these features capture individual variability in both local microstructural properties (reflecting aspects of conventional metrics like fractional anisotropy and neurite density) and tract-level architecture. Across three large population datasets (N=65,316), these features significantly improved phenome-wide prediction of over 3,000 non-imaging phenotypes compared to standard dMRI biomarkers, such as diffusion tensor imaging, NODDI and structural connectomes. Crucially, SSDiff demonstrated robust zero-shot generalizability, accurately predicting clinical phenotypes across 7 independent cohorts (N=3,671) with different diseases, including data acquired with simplified clinical-grade protocols. A genome-wide association study on SSDiff features in 53,276 participants revealed 363 independent novel genetic loci for brain microstructure. Mendelian randomization analyses further revealed causal relationships between these features and brain disorders; for instance, linking the microstructural properties of the cerebellum to Parkinson's disease and of the hippocampus to Alzheimer's disease. SSDiff is a powerful, biologically interpretable foundation model for dMRI analysis, enabling more accurate clinical prediction and revealing novel genetic insights into brain health and disease.

Brain charts have emerged as a highly useful approach for understanding brain development and aging on the basis of brain imaging and have shown substantial utility in describing typical and atypical brain development with respect to a given reference model. However, all existing models are fundamentally cross-sectional and cannot capture change over time at the individual level. We address this using velocity centiles, which directly map change over time and can be overlaid onto cross-sectionally derived population centiles. We demonstrate this by modelling rates of change for 24,062 scans from 10,795 healthy individuals with up to 8 longitudinal measurements across the lifespan. We provide a method to detect individual deviations from a stable trajectory, generalising the notion of 'thrive lines', which are used in pediatric medicine to declare 'failure to thrive'. Using this approach, we predict transition from mild cognitive impairment to dementia more accurately than by using either time point alone, replicated across two datasets. Last, by taking into account multiple time points, we improve the sensitivity of velocity models for predicting the future trajectory of brain change. This highlights the value of predicting change over time and makes a fundamental step towards precision medicine.

Abstract INTRODUCTION Alzheimer's disease (AD) heterogeneity complicates diagnosis and prognosis. Uncovering amyloid–tau–neurodegeneration (A–T–N) patterns may improve diagnostic prediction. METHODS We applied SuperBigFLICA (SBF), a semi‐supervised multimodal fusion method, to gray matter density, cortical thickness (CT), pial surface area, amyloid and tau positron emission tomography maps from 274 Alzheimer's Disease Neuroimaging Initiative 3 participants to derive 50 latent components predictive of cognitive decline. Subject loadings were then used to predict diagnosis (cognitively normal, mild cognitive impairment, dementia) and apolipoprotein E (APOE) ε4 status via least absolute shrinkage and selection operator logistic regression, compared to demographic, single‐modality, and naïve fusion comparator models. RESULTS SBF modestly predicted out‐of‐sample concurrent clinical severity (Clinical Dementia Rating Sum of Boxes; r = 0.21), yet models using SBF‐derived loadings were among the strongest comparator models (area under the receiver operating characteristic curve; = 0.80 for diagnosis; 0.83 for APOE ε4). Amyloid alterations in sensory areas best separated dementia, while a tri‐modal tau–neurodegeneration pattern related to disease progression. Loadings were validated through cerebrospinal fluid correlations. DISCUSSION SBF improves prediction and reveals interpretable patterns that better classify clinical diagnoses and APOE ε4 than traditional approaches.

Abstract Digital phenotyping, which is defined as quantifying someone’s behaviour with digital devices, provides unprecedented opportunities for understanding human mental health but is hampered by high levels of inter-individual variability. Here, we propose a new method to address this, parsing inter-individual variability by decomposing the digital phenotype dynamics into latent trajectories and using each individual’s trajectory membership as a moderator when modelling psychopathology over the same timeframe. We applied our method in the context of mood symptom exacerbation across the menstrual cycle, where symptom severity and timing are inconsistent between individuals. Using the BiAffect platform to collect smartphone typing dynamics, we found stable trajectories in smartphone movement rate: one group of participants showed substantial movement rate fluctuations across the menstrual cycle, whilst the others did not. Participants with movement fluctuations displayed increased fluctuations across the cycle in prospective anhedonia and depression ratings, but not in anxiety, irritability, and suicidal ideation.

Background and objectives: Understanding postanoxic encephalopathy following cardiac arrest is important to provide optimal treatment and predict chances of recovery. Both functional cerebral disturbances (changes in electrophysiological activity or functional connectivity) and structural tissue changes (signs of cytotoxic or vasogenic oedema) have been associated with neurological outcome. We hypothesize that functional and structural changes follow separate pathways towards persistent coma or recovery after a cardiac arrest. We aim to identify common and separate pathways between structural and functional changes using an advanced integration method. Methods: We performed a prospective multicentre cohort study in comatose cardiac arrest patients in three Dutch hospitals. T1-weighted, diffusion-weighted, and resting-state functional MRI scans were collected on day 2-9 after cardiac arrest. Primary outcome measure was neurological outcome at six months defined by the Cerebral Performance Categories (CPC), dichotomized as CPC 1-2 = good, CPC 3-5 = poor. Maps of grey matter volume, Jacobian deformation, mean diffusivity, skeletonized fractional anisotropy, and eight resting-state networks were used as input for a linked independent component analysis. Group differences in subject loadings on the components between individuals with good and poor outcome were calculated. Relationships between functional and structural components were studied descriptively. Results: We included 80 patients (31 with poor outcome). Of twenty identified components, six consisted of functional, seven of structural, and five of both structural and functional information. Four components related independently to outcome: two functional and two structural components. None of the components containing both functional and structural information related to outcome. Patients with good outcome showed higher connectivity in multiple resting-state networks (mainly visual, default-mode, and frontoparietal network) and smaller volumes of and higher diffusivity in the cortex and deep grey nuclei. Signs of functional injury were seen in both outcome groups, while only patients with poor outcome showed signs of structural damage. Forty-three patients showed functional without structural, but no showed structural without functional injury. Discussion: Functional network and structural tissue injury exist independently in postanoxic encephalopathy after cardiac arrest. Our results point towards distinct cerebral mechanisms relating to postanoxic coma outcome, likely including cell swelling, tissue edema, and isolated synaptic failure. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Jeannette Hofmeijer: Dutch Heart Foundation (2018T070) Rick C. Helmich: NOW (0915017201004) Cracking coma and this analysis: institutional grants from University of Twente, Radboudumc, and Rijnstate hospital ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Medical ethics committee Arnhem-Nijmegen gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data that support the findings of this study are available from the corresponding author upon reasonable request

Anhedonia, a core symptom of major depressive disorder (MDD), is linked to dysfunction in reward-related brain regions and impaired dopamine (DA) signaling. This study used arterial spin labeling (ASL) to investigate the effects of a single low dose of amisulpride, a selective DA antagonist, on cerebral blood flow (CBF) in MDD patients and healthy volunteers (HVs). In a double-blind, placebo-controlled, randomized, parallel-group design, participants received either 100 mg amisulpride or placebo. N = 111 participants (MDD: n = 57; HV: n = 54) were included in the final analysis and group differences were assessed in core regions of the reward network, particularly the ventral striatum. Individual CBF values were analyzed in relation to self-reported anhedonia and depression severity. Amisulpride was associated with higher perfusion in the ventral striatum compared to placebo in both HVs and MDD patients, consistent with enhanced dopaminergic activity in this region. No significant CBF changes were observed in other areas of the reward network. Ventral striatal perfusion was significantly associated with self-reported anhedonia. Exploratory whole-brain analyses revealed higher CBF in the right frontal operculum in MDD patients compared to HVs under placebo, but not under amisulpride. In the placebo group, both anhedonia and depression severity were associated with CBF in the right frontal operculum; these associations were absent following amisulpride administration. In conclusion, we provide initial evidence of region-specific effects of amisulpride administration on CBF that are consistent with modulation of dopaminergic signaling. Increased perfusion in the ventral striatum suggests a potential dopaminergic mechanism relevant to anhedonia. These findings underscore the ventral striatum’s role in reward processing and support its involvement in the pathophysiology of anhedonia across clinical and non-clinical populations.

Reward processing deficits are a well-established mechanism underlying anhedonia, a core symptom of Major Depressive Disorder (MDD). Given the role of dopamine in mesocorticolimbic reward pathways, this study examined whether a single low dose of amisulpride (100 mg), which is thought to enhance dopaminergic transmission through presynaptic D2/3 autoreceptor blockade, modulates brain activation during the anticipation of social rewards. A total of 58 participants with MDD and 57 healthy controls (HCs) participated in a double-blind, placebo-controlled, randomized trial. They received either amisulpride or placebo before completing a Social Incentive Delay task during functional magnetic resonance imaging (fMRI). Neural activation was examined in the ventral and dorsal striatum, pallidum, ventral tegmental area (VTA), anterior insula, and ventromedial prefrontal cortex. Anhedonia was assessed using self-report measures. Behaviorally, participants responded faster to reward than no-reward cues, with no significant differences between groups (MDD vs. HCs) or treatment conditions (amisulpride vs. placebo). On a neural level, amisulpride enhanced VTA activation during the anticipation of social rewards compared to no-reward cues. Relative to HCs, participants with MDD showed reduced right putamen activity when anticipating highly rewarding cues, which was not significantly affected by amisulpride. Within the MDD-amisulpride group, exploratory analyses revealed positive associations between anhedonia severity and activity in the putamen, VTA, and anterior insula, and higher striatal activity correlated with faster responses to highly rewarding cues. These findings suggest that while amisulpride enhances VTA activity during the anticipation of social rewards, core striatal deficits in MDD remain, underscoring both the potential and limitations of dopaminergic modulation in addressing social anhedonia.

For many problems in neuroimaging, the most informative features occur in the tail of the distribution. For example, when considering psychiatric disorders as deviations from a 'norm', the tails of the distribution are considerably more informative than the bulk of the distribution for understanding risk, stratifying and predicting such disorders, and for anomaly detection. Yet, most statistical methods used in neuroimaging focus on modeling the bulk and fail to adequately capture extreme values occurring in the tails. To address this, we propose a framework that combines normative models with multivariate extreme value statistics to accurately model extreme deviations of a reference cohort for individual participants. Normative models are now widely used in clinical neuroscience and similar to the employment of normative growth charts in pediatric medicine to track a child's weight in relation to their age; normative models can be used with neuroimaging measurements to quantify individual neurophenotypic deviations from a reference cohort. However, formal statistical treatment of how to model the extreme deviations from these models has been lacking until now. In this article, we provide such an approach inspired by applications of extreme value statistics in meteorology. Since the presentation of extreme value statistics is quite technical, we begin with a non-technical introduction to the fundamental principles of extreme value statistics to accurately map the tails of the normative distribution for biological markers, including mapping multivariate tail dependence across multiple markers. Next, we give a demonstration of this approach to the UK Biobank dataset and demonstrate how extreme values can be used to accurately estimate risk and detect atypicality. This framework provides a valuable tool for the statistical modeling of extreme deviations in neurobiological data, which could provide us with more accurate and effective diagnostic tools for neurological and psychiatric disorders.

Background The field of biomedical research is entering a new era, in which public data sharing is increasingly the norm. There are many advantages of embracing data sharing initiatives, including tackling the replication crisis through enhanced transparency and publication of null findings, facilitating global collaborations to accelerate research progress, enhancing cost-effectiveness by reducing duplication of efforts, and making scientific advances more accessible to the public. However, there are also several crucial ethical and logistical challenges that must be addressed to maximise the benefits of data sharing and minimise risks. The potential, and increasingly recognised, risks of unregulated data sharing (e.g., data reidentification, misuse, and lack of representativeness due to variability in who agrees to share data) have also been exemplified by high profile data breaches and directly clash with efforts to make research more robust, accessible, and global. Methods/Results Here, we narratively outline current challenges for data sharing from the perspective of child and adolescent psychiatry, one area where they may be particularly acute. For example, child and early adolescent research often requires caregivers to consent on behalf of a minor – increasing the responsibility of researchers to consider how the science of today may evolve into the future (when those individuals are no longer minors). We use data from our research consortium Autism Innovative Medicines Study - 2 - Trials (AIMS-2-TRIALS; https://www.aims-2-trials.eu/) to illustrate the points raised in this perspective piece. Conclusions We also propose some potential solutions to begin to address current challenges for data sharing, focusing on key priorities, including shared control of data curation between researcher and participant communities and equity of access by research groups to the tools and resources needed to conduct responsible and sustainable data sharing.

Most current autism research focuses on categorical comparisons (e.g., autistic vs. neurotypical people) and usually examines only one biological domain (e.g., cognition, genetics, or brain imaging). Here, we present a comprehensive resource integrating quantitative phenotypic data, whole genome sequencing, brain magnetic resonance imaging, and electroencephalography. A total of 5,549 people were recruited in Europe through LEAP and InovAND, including 2,061 autistic people, 62 people with intellectual developmental disability who do not meet diagnostic criteria for autism, 2,551 undiagnosed relatives and 875 neurotypical people. Among these people, 2,531 have both clinical and genetic data, and 875 people additionally have neuroimaging data (EEG and/or MRI). We stratified people based on autistic traits and cognitive skills, revealing clusters with distinct genetic and brain signatures. Differences were observed in both rare and common variants, particularly in synaptic and chromatin remodeling genes pathways, and suggesting distinct trajectories of cortical maturation at early stages of development. This resource is available to support research into the complex links between genes, brain structures/functions, and autism. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by Institut Pasteur, Universite Paris Cite, the Simons Foundation Autism Research Initiative (SFARI award #240059), the Bettencourt-Schueller Foundation, the GenMed Labex, and AIMS-2-TRIALS, which received support from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA and AUTISM SPEAKS, Autistica, SFARI, and the Inception program (Investissement d'Avenir grant ANR-16-CONV-0005). This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under grant 847818 (CANDY), and from Horizon Europe under grant 101057385 (R2D2-MH). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work benefited from the DNA & cell bank core facility, at the ICM-Paris Brain Institute. This work received support from the French government, managed by the National Research Agency (Agence Nationale de la Recherche), under the France 2030 program, reference ANR-23-IAHU-0010. Part of this work was funded by a grant from the Conseil Regional d'Ile de France (grant number EX024087). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study is multi-site; Ethical approval was obtained through ethics committees at each site: The London Queen Square Health Research Authority Research Ethics Committee of King's College London & University of Cambridge (KCL & UCAM) gave ethical approval for this work (13/LO/1156). The Radboud Universitair Medisch Centrum Instituut Waarborging Kwaliteit en Veiligheid Commissie Mensgebonden Onderzoek Regio Arnhem-Nijmegen (Radboud University Medical Centre Institute Ensuring Quality and Safety Committee on Research Involving Human Subjects Arnhem-Nijmegen) from Radboud University Nijmegen Medical Centre (RUNMC) & University Medical Centre Utrecht (UMCU) gave ethical approval for this work (2013/455). The UMM Universitatsmedizin Mannheim, Medizinishe Ethik Commission II (UMM University Medical Mannheim, Medical Ethics Commission II) from Central Institute of Mental Health (CIMH) gave ethical approval for this work (2014-540N-MA). The Universita Campus Bio Medica De Roma Comitato Etico (University Campus Bio-Medical Ethics Committee De Roma) from the University Campus Bio-Medico (UCBM) gave ethical approval for this work (18/14 PAR ComET CBM). The Centrala Etikprovningsnamnden (Central Ethical Review Board) from Karolinska Institutet (KI) gave ethical approval for this work (32 2010). The Ethics Committee overseeing the INOVAND cohort (Inserm C07 33) cohorts gave ethical approval for this work (CEER 2008 A00019 46). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets generated and analyzed in this study, including all modalities from the LEAP (EUAIMS / AIMS2 TRIALS) and InovAND cohorts are securely stored on ELIXIR LU servers, part of the European infrastructure for life science information, based at the Luxembourg Centre for Systems Biomedicine (LCSB) and supported by the Luxembourg National Data Service (LNDS). The LEAP dataset (clinical, cognitive, eye-tracking, neuroimaging, and genetic data) is available via the ELIXIR Luxembourg data catalog. The InovAND dataset will be made available through the same repository upon publication. Access to both datasets is granted upon reasonable request following review and approval by the Data Access Committee, including scientific leads, ethics experts, and Autism community representatives. Requests should be submitted via the data catalog website with a detailed project proposal describing the intended use of the data and must be aligned with General Data Protection Regulation (GDPR) requirements as well as the AIMS2 TRIALS consortium or InovAND data sharing policies, respectively.

Parkinson’s disease (PD) is a neurodegenerative disorder with motor symptoms (e.g., bradykinesia, tremors) and non-motor symptoms (e.g., cognitive deficits). Symptom progression varies across individuals, possibly due to differences in the spread of disease pathology. This study investigates individual-level gray matter atrophy in PD patients compared to a reference cohort, modeling neurobiological trajectories to understand symptom progression. Using normative modeling, we mapped individual deviations in gray matter atrophy in PD patients (Personalized Parkinson Project, PPP; N = 408; 42% female) against a reference model (N = 58, 836) of non-diagnosed individuals. Gray matter atrophy was defined as negative deviations from the normative model in cortical thickness and subcortical volume at baseline and two-year follow-up. We correlated the deviations with clinical motor and cognitive symptoms at an individual level and compared changes across PD subtypes (mild-motor predominant, intermediate, and diffuse-malignant). Cross-sectionally, PD patients showed significant gray matter atrophy, which correlated with cognitive impairment. Longitudinally, cortical thinning and subcortical atrophy patterns showed variation amongst subtypes. Specifically, the diffuse-malignant subtype, which is characterized by more diffuse symptoms and faster clinical progression, exhibited pronounced cortical thinning and subcortical atrophy over time. In this paper, we have considered the deviation scores at three levels of granularity: cases vs. control, subtypes, and the individual level. While our findings show subgroup-level patterns of variability, they also provide a method for exploring individual-level metrics of disease progression, acknowledging that individuals may deviate from the predefined categories or groups and can exhibit large variability over time.

Dreaming represents a complex and universal aspect of human sleep, yet it remains an intriguing phenomenon, with the neural mechanisms underlying dream experiences and their frequency not fully understood. This study employs a multimodal neuroimaging approach, integrating quantitative multi-parameter mapping, diffusion tensor imaging, and resting-state functional MRI, to investigate the neural correlates of dream recall frequency (DRF) in a large cohort of 258 healthy individuals. By employing Linked Independent Component Analysis (LICA), we were able to discern distinctive patterns of brain structure and function that correlated with variations in DRF. Our findings elucidate a complex relationship between dream recall and brain microstructure integrity, particularly in white matter regions of the orbitofrontal cortex, parahippocampal gyrus, superior parietal lobule, and occipital cortex. Higher DRF was related to increased white matter microstructure integrity in these regions and decreased gray matter volume in occipital and temporal areas. In terms of functional measures, higher DRF was associated with reduced connectivity across a range of resting-state networks, including the default mode, visual, and dorsal attention networks. This was particularly evident in the right precuneus and posterior cingulate cortex. These results suggest that enhanced dream recall may be related to the organization of higher-order visual and cognitive processing areas, supporting a top-down model of dreaming. This study contributes to a more comprehensive understanding of the neural substrates underlying individual differences in dream recall, offering a foundation for future investigations into the neurobiology and causal relationships of dreaming. ### Competing Interest Statement The authors have declared no competing interest.

A core challenge in neuropsychiatry is identifying brain activity that reflects individual variation in neurobehavioral domains critical to mental health – including those in the Research Domain Criteria. Here, we cross-analyzed mental health and brain function profiles in hundreds of individuals using factor analysis of 87 neurocognitive/psychiatric assessments and tensor independent component analysis of functional magnetic resonance imaging (fMRI) data acquired during five common task and movie-watching paradigms with putative mental health relevance. Across all brain network variants evoked across all paradigms, only individual differences in cognition were significantly or reproducibly reflected in brain activity, despite similar reliability and interindividual variability across mental health domains. Moreover, mental health diversity was not reflected in comparatively homogeneous evoked brain activity, and discordant subtypes were produced by clustering brain function data and mental health data. Brain activity evoked by common fMRI paradigms may contain insufficient non-cognitive information to explain normative mental health variation. ### Competing Interest Statement The authors have declared no competing interest. National Institute on Aging, https://ror.org/049v75w11, RF1AG078304-01