Dissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models

Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG

Research has shown that many copy number variations (CNVs) increase the risk of neurodevelopmental disorders (e.g., autism, ADHD, schizophrenia). However, little is known about the effects of CNVs on brain development and function. Resting-state electroencephalography (EEG) is a suitable method to study the disturbances of neuronal functioning in CNVs. We aimed to determine whether there are resting-state EEG signatures that are characteristic of children with pathogenic CNVs. EEG resting-state brain activity of 109 CNV carriers (66 deletion carriers, 43 duplication carriers) aged 3 to 17 years was recorded for 4 minutes. To better account for developmental variations, EEG indices (power spectral density and functional connectivity) were corrected with a normative model estimated from 256 Healthy Brain Network controls. Results showed a decreased exponent of the aperiodic activity and a reduced alpha peak frequency in CNV carriers. Additionally, the study showed altered periodic components and connectivity in several frequency bands. Deletion and duplication carriers exhibited a similar overall pattern of deviations in spectral and connectivity measures, although the significance and effect sizes relative to the control group varied across frequency bands. Deletion and duplication carriers can be differentiated by their periodic power in the gamma band and connectivity in the low alpha band, with duplication carriers showing more disrupted alterations than deletion carriers. The distinctive alterations in spectral patterns were found to be most prominent during adolescence. The results suggest that CNV carriers show electrophysiological alterations compared to neurotypical controls, regardless of the gene dosage effect and their affected genomic region. At the same time, while duplications and deletions share common electrophysiological alterations, each exhibits distinct brain alteration signatures that reflect gene dosage-specific effects.

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset

Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures, and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), autoencoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities, and evaluation pipelines rather than different machine learning models may explain variations in accuracy in the published literature. The highest accuracy in our framework was obtained when using ensemble models (p < 0.001), leading to an accuracy of 72.2% and AUC = 0.77 using GCN classifiers. However, an SVM classifier performed with an accuracy of 70.1% and AUC = 0.77, just marginally below GCN, and significant differences were not found when comparing different algorithms under the same testing conditions (p > 0.05). Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability in selecting relevant features contributing to model decision making. The code is available at https://github.com/YilanDong19/Machine-learning-with-ABIDE.

Atypical maturation of the functional connectome hierarchy in autism

BACKGROUND

Autism spectrum disorder (ASD) is marked by disruptions in low-level sensory processing and higher-order sociocognitive functions, suggesting a complex interplay between different brain regions across the cortical hierarchy. However, the developmental trajectory of this hierarchical organization in ASD remains underexplored. Herein, we investigated the maturational abnormalities in the cortical hierarchy among individuals with ASD.

METHODS

Resting-state functional magnetic resonance imaging data from three large-scale datasets were analyzed: Autism Brain Imaging Data Exchange I and II and Lifespan Human Connectome Project Development (aged 5-22 years). The principal functional connectivity gradient representing cortical hierarchy was estimated using diffusion map embedding. By applying normative modeling with the generalized additive model for location, scale, and shape (GAMLSS), we captured the nonlinear trajectories of the developing functional gradient, as well as the individual-level deviations in ASD from typical development based on centile scores measured as deviations from the normative curves. A whole-brain summary metric, the functional hierarchy score, was derived to measure the extent of abnormal maturation in individuals with ASD. Finally, through a series of mediation analyses, we examined the potential role of network-level connectomic disruptions between the diagnoses and deviations in the cortical hierarchy.

RESULTS

The maturation of cortical hierarchy in individuals with ASD followed a non-linear trajectory, showing delayed maturation during childhood compared to that of typically developing individuals, followed by an accelerated "catch-up" phase during adolescence and a subsequent decline in young adulthood. The nature of these deviations varied across networks, with sensory and attention networks displaying the most pronounced abnormalities in childhood, while higher-order networks, particularly the default mode network (DMN), remaining impaired from childhood to adolescence. Mediation analyses revealed that the persistent reduction in DMN segregation throughout development was a key contributor to the atypical development of cortical hierarchy in ASD.

LIMITATIONS

The uneven distribution of samples across age groups, particularly in the later stages of development, limited our ability to fully capture developmental trajectories among older individuals.

CONCLUSIONS

These findings highlight the importance of understanding the developmental trajectories of cortical organization in ASD, collectively suggesting that early interventions aimed at promoting the normative development of higher-order networks may be critical for improving outcomes in individuals with ASD.

Using normative models pre-trained on cross-sectional data to evaluate intra-individual longitudinal changes in neuroimaging data

Longitudinal neuroimaging studies offer valuable insight into brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully leverage the potential of longitudinal neuroimaging, we need methodologies that account for the complex interplay between population variation and individual dynamics. We extend the normative modelling framework, which evaluates an individual's position relative to population standards, to assess an individual's longitudinal change compared to the population's standard dynamics. Using normative models pre-trained on over 58,000 individuals, we introduce a quantitative metric termed 'z-diff' score, which quantifies a temporal change in individuals compared to a population standard. This approach offers advantages in flexibility in dataset size and ease of implementation. We applied this framework to a longitudinal dataset of 98 patients with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and 1 year later. Compared to cross-sectional analyses, showing global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time-an effect undetected by traditional longitudinal methods. Overall, our framework presents a flexible and effective methodology for analysing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

Cortical Thickness Differences in Autistic Children With and Without Intellectual Disability

Of the 1 in 36 individuals in the United States who are diagnosed with autism spectrum disorder, nearly 40% also have intellectual disability (ID). The cortex has been widely implicated in neural processes underlying autistic behaviors as well as intellectual ability. Thus, neuroimaging features such as cortical thickness are of particular interest as a possible biomarkers of the condition. However, neuroimaging studies often fail to include autistic individuals with ID. As a result, there are few studies of cortical thickness in autistic individuals across the entire range of intellectual abilities. This study used MRI to evaluate cortical thickness in young autistic children (n = 88, mean age 5.37 years) with a large range of intellectual ability (IQ 19-133) as well as nonautistic, nondevelopmentally delayed (referred to here as typically developing [TD]) peers (n = 53, mean age 5.29 years). We first investigated associations between full scale IQ and cortical thickness in both autistic and TD children. Autistic children had significant negative associations (i.e., thinner cortex, higher IQ) in bilateral entorhinal cortex, right fusiform gyrus, superior, middle and inferior temporal gyri, and right temporal pole that were not present in TD children. Significantly thicker cortex was also observed in these regions for autistic children with ID (i.e., IQ ≤ 70) compared with those without. Last, given the reported correspondence between the severity of autism symptoms and intellectual ability, we compared cortical thickness associations with both IQ and ADOS Calibrated Severity Scores and found these patterns overlapped to a significant degree across the cortex.

Common neuroanatomical differential factors underlying heterogeneous gray matter volume variations in five common psychiatric disorders

Multifaceted evidence has shown that psychiatric disorders share common neurobiological mechanisms. However, the tremendous inter-individual heterogeneity among patients with psychiatric disorders limits trans-diagnostic studies with case-control designs, aimed at identifying clinically promising neuroimaging biomarkers. This study aims to identify neuroanatomical differential factors (ND factors) underlying gray matter volume variations in five psychiatric disorders. We leverage 4 independent datasets of 878 patients diagnosed with psychiatric disorders and 585 healthy controls (HCs) to identify shared ND factors underlying individualized gray matter volume variations. Individualized gray matter volume variations are represented with the linear weighted sum of ND factors, and each case is assigned a unique factor composition, thus preserving interindividual variation. We identify four robust ND factors that can be generalized to unseen disorders. ND factors show significant association with group-level morphological abnormalities, reconciling individual- and group-level morphological abnormalities, and are characterized by dissociable cognitive processes, molecular signatures, and connectome-informed epicenters. Moreover, using factor compositions as features, we discover two robust transdiagnostic subtypes with opposite gray matter volume variations relative to HCs. In conclusion, we identify four reproducible and shared neuroanatomical factors that underlie the highly heterogeneous morphological abnormalities in psychiatric disorders.

Cerebellar Neuromodulation in Autism Spectrum Disorders and Social Cognition: Insights from Animal and Human Studies

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by social atypicalities and repetitive behaviors. Growing evidence suggests that alterations in brain networks may contribute to ASD symptoms. The cerebellum, with its widespread connections to the cortex, has emerged as a potential key player in ASD. Non-invasive neuromodulation techniques, such as transcranial direct current stimulation (tDCS) or repetitive transcranial magnetic stimulation (rTMS) offer a promising avenue for modulating brain activity and potentially alleviating ASD symptoms. In addition, preclinical studies in rodents further emphasize the therapeutic effect of cerebellar stimulation to target autism-related symptoms. This article reviews both clinical and preclinical studies aiming to modulate cerebellar circuits to improve symptoms of ASD. We found ten relevant studies assessing the effect of cerebellar neuromodulation in human and preclinical models. Posterior cerebellar tDCS represented the most frequent neuromodulation method and suggested that cerebellar tDCS can lead to improvements in symptoms of ASD and restore cerebellar connectivity in individuals with ASD. In neurotypical participants, there is evidence that cerebellar tDCS can enhance social cognitive abilities. These results are in line with preclinical studies, suggesting that chemogenetic stimulation can modulate cerebellar circuits involved in ASD and improve related behaviors. Further research is needed to establish standardized protocols, assess long-term effects, and investigate the underlying mechanisms of cerebellar stimulation. We examine research questions that need to be addressed before launching large scale randomized clinical trials.

Individualized gray matter morphological abnormalities unveil two neuroanatomical obsessive-compulsive disorder subtypes

Obsessive-compulsive disorder (OCD) is a highly heterogeneous disorder, with notable variations among cases in structural brain abnormalities. To address this heterogeneity, our study aimed to delineate OCD subtypes based on individualized gray matter morphological differences. We recruited 100 untreated, first-episode OCD patients and 106 healthy controls for structural imaging scans. Utilizing normative models of gray matter volume, we identified subtypes based on individual morphological abnormalities. Sensitivity analyses were conducted to validate the reproducibility of clustering outcomes. To gain deeper insights into the connectomic and molecular underpinnings of structural brain abnormalities in the identified subtypes, we investigated their associations with normal brain network architecture and the distribution of neurotransmitter receptors/transporters. Our findings revealed two distinct OCD subtypes exhibiting divergent patterns of structural brain abnormalities. Sensitivity analysis results confirmed the robustness of the identified subtypes. Subtype 1 displayed significantly increased gray matter volume in regions including the frontal gyrus, precuneus, insula, hippocampus, parahippocampal gyrus, amygdala, and temporal gyrus, while subtype 2 exhibited decreased gray matter volume in the frontal gyrus, precuneus, insula, superior parietal gyrus, temporal gyrus, and fusiform gyrus. When considering all patients collectively, structural brain abnormalities nullified. The identified subtypes were characterized by divergent disease epicenters. Specifically, subtype 1 showed disease epicenters in the middle frontal gyrus, while subtype 2 displayed disease epicenters in the striatum, thalamus and hippocampus. Furthermore, structural brain abnormalities in these subtypes displayed distinct associations with neurotransmitter receptors/transporters. The identified subtypes offer novel insights into nosology and the heterogeneous nature of OCD.

Spectral normative modeling of brain structure

Normative modeling in neuroscience aims to characterize interindividual variation in brain phenotypes and thus establish reference ranges, or brain charts, against which individual brains can be compared. Normative models are typically limited to coarse spatial scales due to computational constraints, limiting their spatial specificity. They additionally depend on fixed regions from fixed parcellation atlases, restricting their adaptability to alternative parcellation schemes. To overcome these key limitations, we propose spectral normative modeling (SNM), which leverages brain eigenmodes for efficient spatial reconstruction to generate normative ranges for arbitrary new regions of interest. Benchmarking against conventional counterparts, SNM achieves a 98.3% speedup in computing accurate normative ranges across spatial scales, from millimeters to the whole brain. We demonstrate its utility by elucidating high-resolution individual cortical atrophy patterns and characterizing the heterogeneous nature of neurodegeneration in Alzheimer's disease. SNM lays the groundwork for a new generation of spatially precise brain charts, offering substantial potential to drive advances in individualized precision medicine.

From Symptomatology to Functioning - Applying the ICF to Autism Measures to Facilitate Neurodiversity-Affirmative Data Harmonization

PURPOSE

A considerable number of screening and diagnostic tools for autism exist, but variability in these measures presents challenges to data harmonization and the comparability and generalizability of findings. At the same time, there is a movement away from autism symptomatology to stances that capture heterogeneity and appreciate diversity. The International Classification of Functioning, Disability and Health (ICF) provides a classification system that can support content harmonization of different screening and diagnostic tools for autism while enabling the translation of diagnostic information into functioning.

METHOD

Here we linked commonly used screening and diagnostic measures within the field of autism to the ICF to facilitate the unification of data obtained from these measures.

RESULTS

As expected, screening and diagnostic measures primarily focus on body functions and activities and participation domains of the ICF, and much less on environmental factors, reflecting biomedical and adaptive behavior operationalizations of autism derived from diagnostic manuals.

CONCLUSION

By translating symptomology-based information to the continuous and diagnostically neutral view of functioning, the ICF linking presented here may provide a means to harmonize measures of autism characteristics while enabling diagnostic information to be re-examined through a more neurodiversity-affirmative lens.

Sex differences in trajectories of cortical development in autistic children from 2-13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2-13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Comparative analysis of cortical anatomy in male participants with internet gaming disorder or tobacco use disorder: Insights from normative modeling

Background

Research on individual differences in brain structural features of internet gaming disorder (IGD) and established addictions such as tobacco use disorder (TUD) is currently limited. This study utilized normative modeling to analyze the cortical thickness (CT) development patterns of male patients with IGD and TUD, aiming to provide further insights into whether IGD qualifies as an addiction.

Methods

Surface-based brain morphometry (SBM) was used to calculate CT from T1-weighted magnetic resonance imaging data of 804 male participants (665 healthy individuals, 68 IGD and 71 TUD). Gaussian process regression was employed to generate normative models of CT development. Deviation maps were produced to depict deviations of IGD and TUD participants from the typical developmental patterns.

Results

Both addiction groups exhibited widespread cortical thinning, particularly in regions such as the bilateral temporal pole and medial orbitofrontal cortex. The TUD group demonstrated a higher degree of individualization and limited spatial overlap compared to the IGD group. Opposite trends in CT changes were observed between the two groups in the bilateral pericalcarine cortex and pars triangularis.

Conclusions

These findings regarding the similarities and differences between IGD and TUD provide support for the idea that IGD shares common features with substance-related addictions and contribute to a deeper understanding of the neural mechanisms underlying IGD.

COVID-19 lockdown effects on adolescent brain structure suggest accelerated maturation that is more pronounced in females than in males

Adolescence is a period of substantial social-emotional development, accompanied by dramatic changes to brain structure and function. Social isolation due to lockdowns that were imposed because of the COVID-19 pandemic had a detrimental impact on adolescent mental health, with the mental health of females more affected than males. We assessed the impact of the COVID-19 pandemic lockdowns on adolescent brain structure with a focus on sex differences. We collected MRI structural data longitudinally from adolescents prior to and after the pandemic lockdowns. The pre-COVID data were used to create a normative model of cortical thickness change with age during typical adolescent development. Cortical thickness values in the post-COVID data were compared to this normative model. The analysis revealed accelerated cortical thinning in the post-COVID brain, which was more widespread throughout the brain and greater in magnitude in females than in males. When measured in terms of equivalent years of development, the mean acceleration was found to be 4.2 y in females and 1.4 y in males. Accelerated brain maturation as a result of chronic stress or adversity during development has been well documented. These findings suggest that the lifestyle disruptions associated with the COVID-19 pandemic lockdowns caused changes in brain biology and had a more severe impact on the female than the male brain.

Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

Dissecting Heterogeneity in Functional Network Connectivity Aberrations in Antipsychotic Medication-Naïve First Episode Psychosis Patients - A Normative Modeling Study

Importance

While there is a general consensus that functional connectome pathology is a key mechanism underlying psychosis spectrum disorders, the literature is plagued with inconsistencies and translation into clinical practice is non-existent. This is perhaps because group-level findings may not be accurate reflections of pathology at the individual patient level.

Objective

To characterize inter-individual heterogeneity in functional networks and investigate if normative values can be leveraged to identify biologically less heterogeneous subgroups of patients.

Design Setting and Participants

We used data collected in a case-control study conducted at the University of Alabama at Birmingham (UAB). We recruited antipsychotic medication-naïve first-episode psychosis patients from UAB outpatient, inpatient, and emergency room settings.

Main Outcomes and Measures

Individual-level patterns of deviations from a normative reference range in resting-state functional networks using the Yeo-17 atlas for parcellations.

Results

Statistical analyses included 108 medication-naïve first-episode psychosis patients. We found that there is a high level of inter-individual heterogeneity in resting-state network connectivity deviations from the normative reference range. Interestingly 48% of patients did not have any functional connectivity deviations, and no more than 11.1% of patients shared functional deviations between the same regions of interest. In a post hoc analysis, we grouped patients based on deviations into four theoretically possible groups. We discovered that all four groups do exist in our experimental data and showed that subgroups based on deviation profiles were significantly less heterogeneous compared to the overall group (positive deviation group: z= -2.88, p = 0.002; negative deviation group: z= -3.36, p<0.001).

Conclusions and Relevance

Our findings experimentally demonstrate that there is a high level of inter-individual heterogeneity in resting-state network pathology in first-episode psychosis patients which support the idea that group-level findings are not accurate reflections of pathology at the individual level. We also demonstrated that normative functional connectivity deviations may have utility for identifying biologically less heterogeneous subgroups of patients, even though they are not distinguishable clinically. Our findings constitute a significant step towards making precision psychiatry a reality, where patients are selected for treatments based on their individual biological characteristics.

KEY POINTS

Question: How heterogeneous is individual-level resting-state functional network pathology in patients suffering from a first psychotic episode? Can normative reference values in functional network connectivity be leveraged to identify biologically more homogenous subgroups of patients?Findings: We report that functional network pathology is highly heterogeneous, with no more than 11% of patients sharing functional deviations between the same regions of interest.Meaning: Normative modeling is a tool that can map individual neurobiological differences and enables the classification of a clinically heterogenous patient group into subgroups that are neurobiologically less heterogenous.

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

Background

Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear.

Methods

We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks.

Results

The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals.

Conclusions

The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.

Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients

Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

BACKGROUND AND OBJECTIVE

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.

METHODS

We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.

RESULTS

We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.

CONCLUSION

We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

Understanding divergence: Placing developmental neuroscience in its dynamic context

Neurodevelopment is not merely a process of brain maturation, but an adaptation to constraints unique to each individual and to the environments we co-create. However, our theoretical and methodological toolkits often ignore this reality. There is growing awareness that a shift is needed that allows us to study divergence of brain and behaviour across conventional categorical boundaries. However, we argue that in future our study of divergence must also incorporate the developmental dynamics that capture the emergence of those neurodevelopmental differences. This crucial step will require adjustments in study design and methodology. If our ultimate aim is to incorporate the developmental dynamics that capture how, and ultimately when, divergence takes place then we will need an analytic toolkit equal to these ambitions. We argue that the over reliance on group averages has been a conceptual dead-end with regard to the neurodevelopmental differences. This is in part because any individual differences and developmental dynamics are inevitably lost within the group average. Instead, analytic approaches which are themselves new, or simply newly applied within this context, may allow us to shift our theoretical and methodological frameworks from groups to individuals. Likewise, methods capable of modelling complex dynamic systems may allow us to understand the emergent dynamics only possible at the level of an interacting neural system.

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.

Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort

BACKGROUND

Autism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group.

METHODS

We studied 781 autistic and 927 non-autistic individuals (6-30 years, IQ ≥ 50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants' loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group.

RESULTS

The two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (β = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (β = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085).

LIMITATIONS

The distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample.

CONCLUSIONS

The current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.

Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling

INTRODUCTION

Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy.

METHODS

We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n = 86). Regional cortical thickness was compared to a healthy reference cohort (n = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels.

RESULTS

The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count.

DISCUSSION

Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients.

Neurodevelopmental disorders: 2024 update

Neurodevelopmental disorders encompass a range of conditions such as intellectual disability, autism spectrum disorder, rare genetic disorders and developmental and epileptic encephalopathies, all manifesting during childhood. Over 1,500 genes involved in various signaling pathways, including numerous transcriptional regulators, spliceosome elements, chromatin-modifying complexes and de novo variants have been recognized for their substantial role in these disorders. Along with new machine learning tools applied to neuroimaging, these discoveries facilitate genetic diagnoses, providing critical insights into neuropathological mechanisms and aiding in prognosis, and precision medicine. Also, new findings underscore the importance of understanding genetic contributions beyond protein-coding genes and emphasize the role of RNA and non-coding DNA molecules but also new players, such as transposable elements, whose dysregulation generates gene function disruption, epigenetic alteration, and genomic instability. Finally, recent developments in analyzing neuroimaging now offer the possibility of characterizing neuronal cytoarchitecture in vivo, presenting a viable alternative to traditional post-mortem studies. With a recently launched digital atlas of human fetal brain development, these new approaches will allow answering complex biological questions about fetal origins of cognitive function in childhood. In this review, we present ten fascinating topics where major progress has been made in the last year.

Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists

BACKGROUND

The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop.

METHODS

To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset.

RESULTS

At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively.

CONCLUSIONS

This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.

Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists

Background

The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop.

Methods

To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset.

Results

At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively.

Conclusions

This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.

Age-related changes in neural responses to sensory stimulation in autism: a cross-sectional study

BACKGROUND

Sensory over-responsivity (SOR) is an impairing sensory processing challenge in autism spectrum disorder (ASD) which shows heterogenous developmental trajectories and appears to improve into adulthood in some but not all autistic individuals. However, the neural mechanisms underlying interindividual differences in these trajectories are currently unknown.

METHODS

Here, we used functional magnetic resonance imaging (fMRI) to investigate the association between age and neural activity linearly and nonlinearly in response to mildly aversive sensory stimulation as well as how SOR severity moderates this association. Participants included 52 ASD (14F) and 41 (13F) typically developing (TD) youth, aged 8.6-18.0 years.

RESULTS

We found that in pre-teens, ASD children showed widespread activation differences in sensorimotor, frontal and cerebellar regions compared to TD children, while there were fewer differences between ASD and TD teens. In TD youth, older age was associated with less activation in the prefrontal cortex. In contrast, in ASD youth, older age was associated with more engagement of sensory integration and emotion regulation regions. In particular, orbitofrontal and medial prefrontal cortices showed a nonlinear relationship with age in ASD, with an especially steep increase in sensory-evoked neural activity during the mid-to-late teen years. There was also an interaction between age and SOR severity in ASD youth such that these age-related trends were more apparent in youth with higher SOR.

LIMITATIONS

The cross-sectional design limits causal interpretations of the data. Future longitudinal studies will be instrumental in determining how prefrontal engagement and SOR co-develop across adolescence.

CONCLUSIONS

Our results suggest that enhanced recruitment of prefrontal regions may underlie age-related decreases in SOR for a subgroup of ASD youth.

Morphological and Functional Changes of Cerebral Cortex in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by early-onset impairments in socialization, communication, repetitive behaviors, and restricted interests. ASD exhibits considerable heterogeneity, with clinical presentations varying across individuals and age groups. The pathophysiology of ASD is hypothesized to be due to abnormal brain development influenced by a combination of genetic and environmental factors. One of the most consistent morphological parameters for assessing the abnormal brain structures in patients with ASD is cortical thickness. Studies have shown changes in the cortical thickness within the frontal, temporal, parietal, and occipital lobes of individuals with ASD. These changes in cortical thickness often correspond to specific clinical features observed in individuals with ASD. Furthermore, the aberrant brain anatomical features and cortical thickness alterations may lead to abnormal brain connectivity and synaptic structure. Additionally, ASD is associated with cortical hyperplasia in early childhood, followed by a cortical plateau and subsequent decline in later stages of development. However, research in this area has yielded contradictory findings regarding the cortical thickness across various brain regions in ASD.

Unpacking the functional heterogeneity of the Emotional Face Matching Task: a normative modelling approach

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N=7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning.

Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.

Linking functional and structural brain organisation with behaviour in autism: a multimodal EU-AIMS Longitudinal European Autism Project (LEAP) study

Neuroimaging analyses of brain structure and function in autism have typically been conducted in isolation, missing the sensitivity gains of linking data across modalities. Here we focus on the integration of structural and functional organisational properties of brain regions. We aim to identify novel brain-organisation phenotypes of autism. We utilised multimodal MRI (T1-, diffusion-weighted and resting state functional), behavioural and clinical data from the EU AIMS Longitudinal European Autism Project (LEAP) from autistic (n = 206) and non-autistic (n = 196) participants. Of these, 97 had data from 2 timepoints resulting in a total scan number of 466. Grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps were extracted from respective MRI modalities and were then integrated with Linked Independent Component Analysis. Linear mixed-effects models were used to evaluate the relationship between components and group while accounting for covariates and non-independence of participants with longitudinal data. Additional models were run to investigate associations with dimensional measures of behaviour. We identified one component that differed significantly between groups (coefficient = 0.33, padj = 0.02). This was driven (99%) by variance of the right fusiform gyrus connectopic map 2. While there were multiple nominal (uncorrected p < 0.05) associations with behavioural measures, none were significant following multiple comparison correction. Our analysis considered the relative contributions of both structural and functional brain phenotypes simultaneously, finding that functional phenotypes drive associations with autism. These findings expanded on previous unimodal studies by revealing the topographic organisation of functional connectivity patterns specific to autism and warrant further investigation.

Fine-grained topographic organization within somatosensory cortex during resting-state and emotional face-matching task and its association with ASD traits

Sensory atypicalities are particularly common in autism spectrum disorders (ASD). Nevertheless, our knowledge about the divergent functioning of the underlying somatosensory region and its association with ASD phenotype features is limited. We applied a data-driven approach to map the fine-grained variations in functional connectivity of the primary somatosensory cortex (S1) to the rest of the brain in 240 autistic and 164 neurotypical individuals from the EU-AIMS LEAP dataset, aged between 7 and 30. We estimated the S1 connection topography ('connectopy') at rest and during the emotional face-matching (Hariri) task, an established measure of emotion reactivity, and accessed its association with a set of clinical and behavioral variables. We first demonstrated that the S1 connectopy is organized along a dorsoventral axis, mapping onto the S1 somatotopic organization. We then found that its spatial characteristics were linked to the individuals' adaptive functioning skills, as measured by the Vineland Adaptive Behavior Scales, across the whole sample. Higher functional differentiation characterized the S1 connectopies of individuals with higher daily life adaptive skills. Notably, we detected significant differences between rest and the Hariri task in the S1 connectopies, as well as their projection maps onto the rest of the brain suggesting a task-modulating effect on S1 due to emotion processing. All in all, variation of adaptive skills appears to be reflected in the brain's mesoscale neural circuitry, as shown by the S1 connectivity profile, which is also differentially modulated during rest and emotional processing.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Understanding the relationship between cerebellar structure and social abilities

BACKGROUND

The cerebellum contains more than 50% of all neurons in the brain and is involved in a broad range of cognitive functions, including social communication and social cognition. Inconsistent atypicalities in the cerebellum have been reported in individuals with autism compared to controls suggesting the limits of categorical case control comparisons. Alternatively, investigating how clinical dimensions are related to neuroanatomical features, in line with the Research Domain Criteria approach, might be more relevant. We hypothesized that the volume of the "cognitive" lobules of the cerebellum would be associated with social difficulties.

METHODS

We analyzed structural MRI data from a large pediatric and transdiagnostic sample (Healthy Brain Network). We performed cerebellar parcellation with a well-validated automated segmentation pipeline (CERES). We studied how social communication abilities-assessed with the social component of the Social Responsiveness Scale (SRS)-were associated with the cerebellar structure, using linear mixed models and canonical correlation analysis.

RESULTS

In 850 children and teenagers (mean age 10.8 ± 3 years; range 5-18 years), we found a significant association between the cerebellum, IQ and social communication performance in our canonical correlation model.

LIMITATIONS

Cerebellar parcellation relies on anatomical boundaries, which does not overlap with functional anatomy. The SRS was originally designed to identify social impairments associated with autism spectrum disorders.

CONCLUSION

Our results unravel a complex relationship between cerebellar structure, social performance and IQ and provide support for the involvement of the cerebellum in social and cognitive processes.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Evidence for embracing normative modeling

In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.

[Hippocampal Development Deviation and Its Relationship With Cognition in Major Psychiatric Disorders]

Objective

To investigate hippocampal development deviation and its association with cognition in patients with major psychiatric disorders (MPDs), including schizophrenia, bipolar disorder and major depressive disorder.

Methods

The T1-weighted MRI data of 174 first-episode drug-naïve schizophrenia (FES) atients, 169 bipolar disorder (BD) patients, 184 major depressive disorder (MDD) patients, and 321 healthy controls were collected and their hippocampal volume was extracted after preprocessing with Freesurfer 5.3. A normative neurodevelopment model was applied to calculate the hippocampal deviation scores. Data on cognitive functions, including visual memory, attention, spatial working memory, were collected. Comparison by different sexes was made to identify difference between the hippocampal development deviation scores of the control group and those of the disease groups. We also investigated the moderating effect of age on the deviation score and explored the association between the deviation score and cognitive function.

Results

The hippocampal development deviation scores of patients with MPDs were significantly lower than those of the healthy controls (false discovery rate [FDR]-P<0.05). Analysis of the moderating effect of age revealed lower deviation scores in young patients (<[25.83-28.56] yr.) and higher deviation scores in old patients (>[35.87-54.35] yr.) in comparison with those of the healthy controls. The right hippocampal deviation scores in male FES patients were positively correlated with the number of errors for tasks concerning spatial working memory ( r=0.32, FDR-P=0.04).

Conclusion

Our findings suggest abnormal hippocampal development in MPDs patients and its different distribution in MPDs patients of different age groups. The hippocampal development deviation score may provide a new perspective for further understanding of etiology in MPDs.

Age differences and brain maturation provide insight into heterogeneous results in autism spectrum disorder

Studies comparing individuals with autism spectrum disorder (ASD) to typically developing (TD) individuals have yielded inconsistent results. These inconsistencies reflect, in part, atypical trajectories of development in children and young adults with ASD compared to TD peers. These different trajectories alter group differences between children with and without ASD as they age. This paper first summarizes the disparate trajectories evident in our studies and, upon further investigation, laboratories using the same recruiting source. These studies indicated that cognition improves into adulthood typically, and is associated with the maturation of striatal, frontal, and temporal lobes, but these age-related improvements did not emerge in the young adults with ASD. This pattern - of improvement into adulthood in the TD group but not in the group with ASD - occurred in both social and non-social tasks. However, the difference between TD and ASD trajectories was most robust on a social task, face recognition. While tempting to ascribe this uneven deficit to the social differences in ASD, it may also reflect the prolonged typical development of social cognitive tasks such as face recognition into adulthood. This paper then reviews the evidence on age-related and developmental changes from other studies on ASD. The broader literature also suggests that individuals with ASD do not exhibit the typical improvements during adolescence on skills important for navigating the transition to adulthood. These skills include execution function, social cognition and communication, and emotional recognition and self-awareness. Relatedly, neuroimaging studies indicate arrested or atypical brain maturation in striatal, frontal, and temporal regions during adolescence in ASD. This review not only highlights the importance of a developmental framework and explicit consideration of age and/or stage when studying ASD, but also the potential importance of adolescence on outcomes in ASD.

Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder

Language impairment is comorbid in most children with Autism Spectrum Disorder (ASD) but its neural basis is poorly understood. Using structural magnetic resonance imaging (MRI), the present study provides the whole-brain comparison of both volume- and surface-based characteristics between groups of children with and without ASD and investigates the relationships between these characteristics in language-related areas and the language abilities of children with ASD measured with standardized tools. A total of 36 school-aged children participated in the study: 18 children with ASD and 18 age- and sex-matched typically developing controls. The results revealed that multiple regions differed between groups of children in gray matter volume, gray matter thickness, gyrification, and cortical complexity (fractal dimension). White matter volume and sulcus depth did not differ between groups of children in any region. Importantly, gray matter thickness and gyrification of language-related areas were related to language functioning in children with ASD. Thus, the results of the present study shed some light on the structural brain abnormalities associated with language impairment in ASD.

Does the current state of biomarker discovery in autism reflect the limits of reductionism in precision medicine? Suggestions for an integrative approach that considers dynamic mechanisms between brain, body, and the social environment

Over the past decade, precision medicine has become one of the most influential approaches in biomedical research to improve early detection, diagnosis, and prognosis of clinical conditions and develop mechanism-based therapies tailored to individual characteristics using biomarkers. This perspective article first reviews the origins and concept of precision medicine approaches to autism and summarises recent findings from the first "generation" of biomarker studies. Multi-disciplinary research initiatives created substantially larger, comprehensively characterised cohorts, shifted the focus from group-comparisons to individual variability and subgroups, increased methodological rigour and advanced analytic innovations. However, although several candidate markers with probabilistic value have been identified, separate efforts to divide autism by molecular, brain structural/functional or cognitive markers have not identified a validated diagnostic subgroup. Conversely, studies of specific monogenic subgroups revealed substantial variability in biology and behaviour. The second part discusses both conceptual and methodological factors in these findings. It is argued that the predominant reductionist approach, which seeks to parse complex issues into simpler, more tractable units, let us to neglect the interactions between brain and body, and divorce individuals from their social environment. The third part draws on insights from systems biology, developmental psychology and neurodiversity approaches to outline an integrative approach that considers the dynamic interaction between biological (brain, body) and social mechanisms (stress, stigma) to understanding the origins of autistic features in particular conditions and contexts. This requires 1) closer collaboration with autistic people to increase face validity of concepts and methodologies; (2) development of measures/technologies that enable repeat assessment of social and biological factors in different (naturalistic) conditions and contexts, (3) new analytic methods to study (simulate) these interactions (including emergent properties), and (4) cross-condition designs to understand which mechanisms are transdiagnostic or specific for particular autistic sub-populations. Tailored support may entail both creating more favourable conditions in the social environment and interventions for some autistic people to increase well-being.

Normative model detects abnormal functional connectivity in psychiatric disorders

INTRODUCTION

The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders.

METHODS

We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject.

RESULTS AND DISCUSSION

We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.

Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression

Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

Deviations from normative brain white and gray matter structure are associated with psychopathology in youth

Combining imaging modalities and metrics that are sensitive to various aspects of brain structure and maturation may help identify individuals that show deviations in relation to same-aged peers, and thus benefit early-risk-assessment for mental disorders. We used one timepoint multimodal brain imaging, cognitive, and questionnaire data from 1280 eight- to twenty-one-year-olds from the Philadelphia Neurodevelopmental Cohort. We estimated age-related gray and white matter properties and estimated individual deviation scores using normative modeling. Next, we tested for associations between the estimated deviation scores, and with psychopathology domain scores and cognition. More negative deviations in DTI-based fractional anisotropy (FA) and the first principal eigenvalue of the diffusion tensor (L1) were associated with higher scores on psychosis positive and prodromal symptoms and general psychopathology. A more negative deviation in cortical thickness (CT) was associated with a higher general psychopathology score. Negative deviations in global FA, surface area, L1 and CT were also associated with poorer cognitive performance. No robust associations were found between the deviation scores based on CT and DTI. The low correlations between the different multimodal magnetic resonance imaging-based deviation scores suggest that psychopathological burden in adolescence can be mapped onto partly distinct neurobiological features.

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models

The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.

Greater variability in rhesus macaque (Macaca mulatta) endocranial volume among males than females

The greater male variability (GMV) hypothesis proposes that traits are more variable among males than females, and is supported by numerous empirical studies. Interestingly, GMV is also observed for human brain size and internal brain structure, a pattern which may have implications for sex-biased neurological and psychiatric conditions. A better understanding of neuroanatomical variability in non-human primates may illuminate whether certain species are appropriate models for these conditions. Here, we tested for sex differences in the variability of endocranial volume (ECV, a proxy for brain size) in a sample of 542 rhesus macaques (Macaca mulatta) from a large pedigreed free-ranging population. We also examined the components of phenotypic variance (additive genetic and residual variance) to tease apart the potential drivers of sex differences in variability. Our results suggest that males exhibit more variable ECVs, and that this pattern reflects either balancing/disruptive selection on male behaviour (associated with alternative male mating strategies) or sex chromosome effects (associated with mosaic patterns of X chromosome gene expression in females), rather than extended neurodevelopment among males. This represents evidence of GMV for brain size in a non-human primate species and highlights the potential of rhesus macaques as a model for sex-biased brain-based disorders.

Contrasting Case-Control and Normative Reference Approaches to Capture Clinically Relevant Structural Brain Abnormalities in Patients With First-Episode Psychosis Who Are Antipsychotic Naive

Importance

To make progress toward precision psychiatry, it is crucial to move beyond case-control studies and instead capture individual variations and interpret them in the context of a normal range of biological systems.

Objective

To evaluate whether baseline deviations from a normative reference range in subcortical volumes are better predictors of antipsychotic treatment response than raw volumes in patients with first-episode psychosis (FEP) who were naive to antipsychotic medication.

Design, Setting, and Participants

In this prospective longitudinal study, patients with first-episode psychosis who were referred from different clinical settings (emergency department, inpatient units, and outpatient clinics) at the University of Alabama at Birmingham were included. A total of 286 patients were screened, 114 consented, 104 enrolled in the treatment trial, and 85 completed the trial. Patients were observed for 16 weeks. Controls were matched by age and sex. Data were collected between June 2016 and July 2021, and data were analyzed from August 2021 to June 2022.

Interventions

Risperidone on a flexible dosing scheme for 16 weeks. There was an option to switch to aripiprazole for excessive adverse effects.

Main Outcomes and Measures

The main outcome of this study was to evaluate, in patients with FEP who were naive to antipsychotic medication, the association of baseline raw volumes and volume deviations in subcortical brain regions with response to antipsychotic medication. Raw brain volumes or volume deviation changes after treatment were not examined.

Results

Of 190 included participants, 111 (58.4%) were male, and the mean (SD) age was 23.7 (5.5) years. Volumes and deviations were quantified in 98 patients with FEP, and data from 92 controls were used as comparison for case-control contrasts and reference curve calibration. In case-control contrasts, patients with FEP had lower raw thalamus (P = .002; F = 9.63; df = 1), hippocampus (P = .009; F = 17.23; df = 1), amygdala (P = .01; F = 6.55; df = 1), ventral diencephalon (P = .03; F = 4.84; df = 1), and brainstem volumes (P = .004; F = 8.39; df = 1). Of 98 patients, 36 patients with FEP (36%) displayed extreme deviations. Associations with treatment response significantly differed between raw volume and deviation measures in the caudate (z = -2.17; P = .03) and putamen (z = -2.15; P = .03).

Conclusions and Relevance

These data suggest that normative modeling allows capture of interindividual heterogeneity of regional brain volumes in patients with FEP and characterize structural pathology in a clinically relevant fashion. This holds promise for progress in precision medicine in psychiatry, where group-level studies have failed to derive reliable maps of structural pathology.

Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Resting-state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently-proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment-to-moment-activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting-state functional magnetic resonance imaging (rs-fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high-amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.

Cortical Source Analysis of the Face Sensitive N290 ERP Component in Infants at High Risk for Autism

Appropriate head models for cortical source analysis were investigated and applied to source analyses examining the neural bases of the face-sensitive N290 event-related potential (ERP) component in infants at high risk for autism spectrum disorder (ASD). This included infant siblings of children with ASD (ASIBs) and infants with fragile X syndrome (FXS). First, alternative head models for use with ASIBs and FXS were investigated. Head models created from the infant's own MRI were examined in relation to five head models based on average MRI templates. The results of the head model comparison identified group-specific (i.e., ASIB or FXS) head models created from a large collection of structural MRIs as the best substitution for the head model created from the participant's own structural MRI. Second, the cortical source analysis was completed on N290 data collected from a previous study to investigate brain areas associated with face sensitive ERP responses. Participants' own MRIs were used for head models when available, and the group-specific head model was used when the participants' own MRIs were not available. The results provide evidence for unique patterns of neural activation during face processing across infants at high and low risk for ASD and across etiologically distinct high-risk groups. All infants demonstrated greater activation to faces than toys in brain areas most associated with specialized face processing. Infants with FXS displayed higher levels of activation to faces across all areas analyzed, while ASIBs show more muted levels of activation. Overall, the results of the current study demonstrate the importance of group-specific head models for accurate cortical source analysis in infants at high risk for ASD. This also allows for further research on early distinctions in brain function based on risk status.

The normative modeling framework for computational psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.

Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder

OBJECTIVE

Autism spectrum disorder (ASD) is accompanied by highly individualized neuroanatomical deviations that potentially map onto distinct genotypes and clinical phenotypes. This study aimed to link differences in brain anatomy to specific biological pathways to pave the way toward targeted therapeutic interventions.

METHODS

The authors examined neurodevelopmental differences in cortical thickness and their genomic underpinnings in a large and clinically diverse sample of 360 individuals with ASD and 279 typically developing control subjects (ages 6-30 years) within the EU-AIMS Longitudinal European Autism Project (LEAP). The authors also examined neurodevelopmental differences and their potential pathophysiological mechanisms between clinical ASD subgroups that differed in the severity and pattern of sensory features.

RESULTS

In addition to significant between-group differences in "core" ASD brain regions (i.e., fronto-temporal and cingulate regions), individuals with ASD manifested as neuroanatomical outliers within the neurotypical cortical thickness range in a wider neural system, which was enriched for genes known to be implicated in ASD on the genetic and/or transcriptomic level. Within these regions, the individuals' total (i.e., accumulated) degree of neuroanatomical atypicality was significantly correlated with higher polygenic scores for ASD and other psychiatric conditions, and it scaled with measures of symptom severity. Differences in cortical thickness deviations were also associated with distinct sensory subgroups, especially in brain regions expressing genes involved in excitatory rather than inhibitory neurotransmission.

CONCLUSIONS

The study findings corroborate the link between macroscopic differences in brain anatomy and the molecular mechanisms underpinning heterogeneity in ASD, and provide future targets for stratification and subtyping.