Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies

Individualized functional connectome abnormalities obtained using two normative model unveil neurophysiological subtypes of obsessive compulsive disorder

The high heterogeneity observed among patients with obsessive-compulsive disorder (OCD) underscores the need to identify neurophysiological OCD subtypes to facilitate personalized diagnosis and treatment. In this study, our aim was to identify potential OCD subtypes based on individualized functional connectome abnormalities. We recruited a total of 99 patients with OCD and 104 healthy controls (HCs) matched for demographic characteristics. Individualized functional connectome abnormalities were obtained using normative models of functional connectivity strength (FCS) and used as features to unveil OCD subtypes. Sensitivity analyses were conducted to assess the reproducibility and robustness of the clustering outcomes. Patients exhibited significant intersubject heterogeneity in individualized functional connectome abnormalities. Two subtypes with distinct patterns of FCS abnormalities relative to HCs were identified. Subtype 1 patients primarily exhibited significantly decreased FCS in regions including the frontal gyrus, insula, hippocampus, and precentral/postcentral gyrus, whereas subtype 2 patients demonstrated increased FCS in widespread brain regions. When all patients were combined, no significant differences were observed. Additionally, the identified subtypes showed significant differences in age of onset. Furthermore, sensitivity analyses confirmed the reproducibility of our subtyping results. In conclusion, the identified OCD subtypes shed new light on the taxonomy and neurophysiological heterogeneity of OCD.

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model

BACKGROUND

The widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD.

METHODS

This study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability.

RESULTS

Model constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = - 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN.

CONCLUSIONS

This study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.

Individualized gray matter morphological abnormalities uncover two robust transdiagnostic biotypes

Psychiatric disorders exhibit a shared neuropathology, yet the diverse presentations among patients necessitate the identification of transdiagnostic subtypes to enhance diagnostic and treatment strategies. This study aims to unveil potential transdiagnostic subtypes based on personalized gray matter morphological abnormalities. A total of 496 patients with psychiatric disorders and 255 healthy controls (HCs) from three distinct datasets (one for discovery and two for validation) were enrolled. Individualized gray matter morphological abnormalities were determined using normative modeling to identify transdiagnostic subtypes. In the discovery dataset, two transdiagnostic subtypes with contrasting patterns of structural abnormalities compared to HCs were identified. Reproducibility and generalizability analyses demonstrated that these subtypes could be generalized to new patients and even to new disorders in the validation datasets. These subtypes were characterized by distinct disease epicenters. The gray matter abnormal pattern in subtype 1 was mainly linked to excitatory receptors, whereas subtype 2 showed a predominant association with inhibitory receptors. Furthermore, we observed that the gray matter abnormal pattern in subtype 2 was correlated with transcriptional profiles of inflammation-related genes, while subtype 1 did not show this association. Our findings reveal two robust transdiagnostic biotypes, offering novel insights into psychiatric nosology.

"Pscore": A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics

BACKGROUND

Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed.

PURPOSE

To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue.

STUDY TYPE

Retrospective cohort.

POPULATION

Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project.

FIELD STRENGTH/SEQUENCE

3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE.

ASSESSMENT

The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values.

STATISTICAL TESTS

ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%), PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations.

RESULTS

The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (averagePEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % [SD]).

DATA CONCLUSION

The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 1.

Neurobiology of attention-deficit hyperactivity disorder: historical challenges and emerging frontiers

Extensive investigations spanning multiple levels of inquiry, from genetic to behavioural studies, have sought to unravel the mechanistic foundations of attention-deficit hyperactivity disorder (ADHD), with the aspiration of developing efficacious treatments for this condition. Despite these efforts, the pathogenesis of ADHD remains elusive. In this Review, we reflect on what has been learned about ADHD while also providing a framework that may serve as a roadmap for future investigations. We emphasize that ADHD is a highly heterogeneous disorder with multiple aetiologies that necessitates a multifactorial dimensional phenotype, rather than a fixed dichotomous conceptualization. We highlight new findings that suggest a more brain-wide, 'global' view of the disorder, rather than the traditional localizationist framework, which asserts that a limited set of brain regions or networks underlie ADHD. Last, we underscore how underpowered studies that have aimed to associate neurobiology with ADHD phenotypes have long precluded the field from making progress. However, a new age of ADHD research with refined phenotypes, advanced methods, creative study designs and adequately powered investigations is beginning to put the field on a good footing. Indeed, the field is at a promising juncture to advance the neurobiological understanding of ADHD and fulfil the promise of clinical utility.

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.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

A perspective on brain-age estimation and its clinical promise

Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.

Functional network characteristics in anxiety- and mania-based subgroups of bipolar I disorder

BACKGROUND

Bipolar disorder I (BD-I) is a heterogeneous disorder with a high prevalence of comorbid anxiety. The aim of this study was to investigate whether anxiety and mania symptoms define distinct subgroups within BD-I and to explore potential differences in functional network characteristics between these subgroups.

METHODS

Subgroups were identified using scores from clinical anxiety and mania scales. After dimension reduction of these scores, data-driven clustering analysis with cross-validation was employed to reveal the existence of subgroups. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were pre-processed using fMRIPrep. After parcellation and network construction, global and regional graph theoretical measures were calculated per subgroup.

RESULTS

Clustering results revealed that, based on anxiety symptomatology, subjects fell into two distinct subgroups, whereas mania symptoms divided subjects into four unique subgroups. These subgroups varied notably on several symptom scales. Network assortativity was significantly associated with anxiety subgroups. Post-hoc pairwise comparisons did not reveal significant global functional network differences between the anxiety subgroups or between mania subgroups. Regional network differences between clinical subgroups were especially apparent for strength and degree in the temporal and frontal lobes.

LIMITATIONS

Small sample size of some subgroups is a limitation of this study as is the categorical rather than continuous representation of anxiety and mania symptoms.

CONCLUSIONS

BD-I populations may be stratified into robust subgroups based on anxiety and mania symptoms, showing differences in functional network connectivity. Our findings highlight new avenues of research for investigating heterogeneity in psychiatric populations.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

The impact of psychosocial adversity on brain and behaviour: an overview of existing knowledge and directions for future research

Environmental experiences play a critical role in shaping the structure and function of the brain. Its plasticity in response to different external stimuli has been the focus of research efforts for decades. In this review, we explore the effects of adversity on brain's structure and function and its implications for brain development, adaptation, and the emergence of mental health disorders. We are focusing on adverse events that emerge from the immediate surroundings of an individual, i.e., microenvironment. They include childhood maltreatment, peer victimisation, social isolation, affective loss, domestic conflict, and poverty. We also take into consideration exposure to environmental toxins. Converging evidence suggests that different types of adversity may share common underlying mechanisms while also exhibiting unique pathways. However, they are often studied in isolation, limiting our understanding of their combined effects and the interconnected nature of their impact. The integration of large, deep-phenotyping datasets and collaborative efforts can provide sufficient power to analyse high dimensional environmental profiles and advance the systematic mapping of neuronal mechanisms. This review provides a background for future research, highlighting the importance of understanding the cumulative impact of various adversities, through data-driven approaches and integrative multimodal analysis techniques.

Anhedonia Across and Beyond the Schizophrenia Spectrum

Anhedonia refers to the diminished ability to experience pleasure, and is a core feature of schizophrenia (SCZ). The neurocognitive and neural correlates of anhedonia remain elusive. Based on several influential theoretical models for negative symptoms, this selective review proposed four important neurocognitive domains, which may unveil the neurobiological mechanisms of anhedonia. The authors critically reviewed the current evidence regarding value representation of reward, prospection, emotion-behavior decoupling, and belief updating in the Chinese setting, covering both behavioral and neuroimaging research. We observed a limited application of the transdiagnostic approach in previous studies on the four domains, and the lack of adequate measures to tap into the expressivity deficit in SCZ. Despite many behavioral paradigms for these four domains utilized both social and non-social stimuli, previous studies seldom focused on the social-versus-non-social differentiation. We further advocated several important directions for future research.

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.

Regional patterns of human cortex development correlate with underlying neurobiology

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We employed deep autoencoders in an anomaly detection framework, combined with a confounder removal step integrating training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of subject- and group-level brain normative-deviating patterns, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

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.

The Future of Disentangling the Heterogeneity of Autism With Neuroimaging Studies

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition. Over the past decade, a considerable number of approaches have been developed to identify potential neuroimaging-based biomarkers of ASD that have uncovered specific neural mechanisms that underlie behaviors associated with ASD. However, the substantial heterogeneity among individuals who are diagnosed with ASD hinders the development of biomarkers. Disentangling the heterogeneity of ASD is pivotal to improving the quality of life for individuals with ASD by facilitating early diagnosis and individualized interventions for those who need support. In this review, we discuss recent advances in neuroimaging that have facilitated the characterization of the heterogeneity of this condition using 3 frameworks: neurosubtyping, dimensional models, and normative models. We also discuss the challenges, possible solutions, and clinical utility of these 3 frameworks. We argue that several factors need to be considered when parsing heterogeneity using neuroimaging, including co-occurring conditions, neurodevelopment, heredity and environment, and multisite and multimodal data. We close with a discussion of future directions for achieving a better understanding of the neural mechanisms that underlie neurodevelopmental heterogeneity and the future of precision medicine in ASD.

Consistently lower volumes across thalamus nuclei in very premature-born adults

Lasting thalamus volume reduction after preterm birth is a prominent finding. However, whether thalamic nuclei volumes are affected differentially by preterm birth and whether nuclei aberrations are relevant for cognitive functioning remains unknown. Using T1-weighted MR-images of 83 adults born very preterm (≤ 32 weeks' gestation; VP) and/or with very low body weight (≤ 1,500 g; VLBW) as well as of 92 full-term born (≥ 37 weeks' gestation) controls, we compared thalamic nuclei volumes of six subregions (anterior, lateral, ventral, intralaminar, medial, and pulvinar) across groups at the age of 26 years. To characterize the functional relevance of volume aberrations, cognitive performance was assessed by full-scale intelligence quotient using the Wechsler Adult Intelligence Scale and linked to volume reductions using multiple linear regression analyses. Thalamic volumes were significantly lower across all examined nuclei in VP/VLBW adults compared to controls, suggesting an overall rather than focal impairment. Lower nuclei volumes were linked to higher intensity of neonatal treatment, indicating vulnerability to stress exposure after birth. Furthermore, we found that single results for lateral, medial, and pulvinar nuclei volumes were associated with full-scale intelligence quotient in preterm adults, albeit not surviving correction for multiple hypotheses testing. These findings provide evidence that lower thalamic volume in preterm adults is observable across all subregions rather than focused on single nuclei. Data suggest the same mechanisms of aberrant thalamus development across all nuclei after premature birth.

Brain-Charting Autism and Attention-Deficit/Hyperactivity Disorder Reveals Distinct and Overlapping Neurobiology

BACKGROUND

Autism and attention-deficit/hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology that is still poorly understood. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together and sex differences are often overlooked. Population modeling, often referred to as normative modeling, provides a unified framework for studying age-specific and sex-specific divergences in brain development.

METHODS

Here, we used population modeling and a large, multisite neuroimaging dataset (N = 4255 after quality control) to characterize cortical anatomy associated with autism and ADHD, benchmarked against models of average brain development based on a sample of more than 75,000 individuals. We also examined sex and age differences and relationship with autistic traits and explored the co-occurrence of autism and ADHD.

RESULTS

We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume that was localized to the superior temporal cortex, whereas individuals with ADHD showed more global increases in cortical thickness but lower cortical volume and surface area across much of the cortex. The co-occurring autism+ADHD group showed a unique pattern of widespread increases in cortical thickness and certain decreases in surface area. We also found that sex modulated the neuroanatomy of autism but not ADHD, and there was an age-by-diagnosis interaction for ADHD only.

CONCLUSIONS

These results indicate distinct cortical differences in autism and ADHD that are differentially affected by age and sex as well as potentially unique patterns related to their co-occurrence.

Regional patterns of human cortex development correlate with underlying neurobiology

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps

Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ('latent indices') and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.

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.

Individualized Structural Perturbations on Normative Brain Connectome Restrict Deep Brain Stimulation Outcomes in Parkinson's Disease

BACKGROUND

Patients with Parkinson's disease (PD) respond to deep brain stimulation (DBS) variably. However, how brain substrates restrict DBS outcomes remains unclear.

OBJECTIVE

In this article, we aim to identify prognostic brain signatures for explaining the response variability.

METHODS

We retrospectively investigated a cohort of patients with PD (n = 141) between 2017 and 2022, and defined DBS outcomes as the improvement ratio of clinical motor scores. We used a deviation index to quantify individual perturbations on a reference structural covariance network acquired with preoperative T1-weighted magnetic resonance imaging. The neurobiological perturbations of patients were represented as z scored indices based on the chronological perturbations measured on a group of normal aging adults.

RESULTS

After applying stringent statistical tests (z > 2.5) and correcting for false discoveries (P < 0.01), we found that accelerated deviations mainly affected the prefrontal cortex, motor strip, limbic system, and cerebellum in PD. Particularly, a negative network within the accelerated deviations, expressed as "more preoperative deviations, less postoperative improvements," could predict DBS outcomes (mean absolute error = 0.09, R2 = 0.15). Moreover, a fusion of personal brain predictors and medical responses significantly improved traditional evaluations of DBS outcomes. Notably, the most important brain predictor, a pathway connecting the cognitive unit (prefrontal cortex) and motor control unit (cerebellum and motor strip), partially mediates DBS outcomes with the age at surgery.

CONCLUSIONS

Our findings suggest that individual structural perturbations on the cognitive motor control circuit are critical for modulating DBS outcomes. Interventions toward the circuit have the potential for additional clinical improvements. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Quantifying impairment and disease severity using AI models trained on healthy subjects

Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).

A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models

BACKGROUND

Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms.

DESIGN

Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms.

RESULTS

LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores.

CONCLUSIONS

This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

INTRODUCTION

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers.

METHODS

We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN.

RESULTS

Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression.

DISCUSSION

Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

Facilitating clinical use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative data and a diagnostic cutoff value

OBJECTIVE

The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) is well validated and commonly used to assess difficulties in everyday functioning regarding dementia. To facilitate interpretation and clinical implementation across different European countries, we aim to provide normative data and a diagnostic cutoff for dementia.

METHODS

Cross-sectional data from Dutch Brain Research Registry (N = 1,064; mean (M) age = 62 ± 11 year; 69.5% female), European Medial Information Framework-Alzheimer's Disease 90 + (N = 63; Mage = 92 ± 2 year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (N = 247; Mage = 63 ± 7 year; 72.1% female) were used. The generalized additive models for location, scale, and shape framework were used to obtain normative values (Z-scores). The beta distribution was applied, and combinations of age, sex, and educational attainment were modeled. The optimal cutoff for dementia was calculated using area under receiver operating curves (AUC-ROC) and Youden Index, using data from Amsterdam Dementia Cohort (N = 2,511, Mage = 64 ± 8 year, 44.4% female).

RESULTS

The best normative model accounted for a cubic-like decrease of IADL performance with age that was more pronounced in low compared to medium/high educational attainment. The cutoff for dementia was 1.85 standard deviation below the population mean (AUC = 0.97; 95% CI [0.97-0.98]).

CONCLUSION

We provide regression-based norms for A-IADL-Q and a diagnostic cutoff for dementia, which help improve clinical assessment of IADL performance across European countries.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders

BACKGROUND

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

METHODS

We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort.

RESULTS

Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable.

CONCLUSIONS

Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach

BACKGROUND

Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level.

METHODS

Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks.

RESULTS

We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms.

CONCLUSIONS

Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers

INTRODUCTION

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers.

METHODS

We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN.

RESULTS

Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression.

DISCUSSION

Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

Population-level normative models reveal race- and socioeconomic-related variability in cortical thickness of threat neurocircuitry

The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness.

Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals

One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks

BACKGROUND

Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges.

METHODS

We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed.

RESULTS

Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate.

CONCLUSIONS

In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.

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.

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Associations between atypical intracortical myelin content and neuropsychological functions in middle to older aged adults with ASD

INTRODUCTION

In vivo myeloarchitectonic mapping based on Magnetic Resonance Imaging (MRI) provides a unique view of gray matter myelin content and offers information complementary to other morphological indices commonly employed in studies of autism spectrum disorder (ASD). The current study sought to determine if intracortical myelin content (MC) and its age-related trajectories differ between middle aged to older adults with ASD and age-matched typical comparison participants.

METHODS

Data from 30 individuals with ASD and 36 age-matched typical comparison participants aged 40-70 years were analyzed. Given substantial heterogeneity in both etiology and outcomes in ASD, we utilized both group-level and subject-level analysis approaches to test for signs of atypical intracortical MC as estimated by T1w/T2w ratio.

RESULTS

Group-level analyses showed no significant differences in average T1w/T2w ratio or its associations with age between groups, but revealed significant positive main effects of age bilaterally, with T1w/T2w ratio increasing with age across much of the cortex. In subject-level analyses, participants were classified into subgroups based on presence or absence of clusters of aberrant T1w/T2w ratio, and lower neuropsychological function was observed in the ASD subgroup with atypically high T1w/T2w ratio in spatially heterogeneous cortical regions. These differences were observed across several neuropsychological domains, including overall intellectual functioning, processing speed, and aspects of executive function.

CONCLUSIONS

The group-level and subject-level approaches employed here demonstrate the value of examining inter-individual variability and provide important preliminary insights into relationships between brain structure and cognition in the second half of the lifespan in ASD, suggesting shared factors contributing to atypical intracortical myelin content and poorer cognitive outcomes for a subset of middle aged to older autistic adults. These atypicalities likely reflect diverse histories of neurodevelopmental deficits, and possible compensatory changes, compounded by processes of aging, and may serve as useful markers of vulnerability to further cognitive decline in older adults with ASD.

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

Initiating PeriCBD to probe perinatal influences on neurodevelopment during 3-10 years in China

Adverse perinatal factors can interfere with the normal development of the brain, potentially resulting in long-term effects on the comprehensive development of children. Presently, the understanding of cognitive and neurodevelopmental processes under conditions of adverse perinatal factors is substantially limited. There is a critical need for an open resource that integrates various perinatal factors with the development of the brain and mental health to facilitate a deeper understanding of these developmental trajectories. In this Data Descriptor, we introduce a multicenter database containing information on perinatal factors that can potentially influence children's brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3-10 years. Ultimately, our goal is to help understand how different adverse perinatal factors specifically impact cognitive development and neurodevelopment. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD.

Virtual reality gameplay classification illustrates the multidimensionality of visuospatial neglect

Brain injuries can significantly impact mental processes and lead to hidden disabilities not easily detectable. Traditional methods for assessing these impacts are imprecise, leading to unreliable prevalence estimates and treatments with uncertain effectiveness. Immersive virtual reality has shown promise for assessment, but its use as a standalone tool is rare. Our research focused on developing and validating a standalone immersive virtual reality classification system for unilateral spatial neglect, a condition common following brain injury characterized by inattention to one side of space. Our study involved 51 brain injury inpatients and 30 controls, all engaging with 'The Attention Atlas', an immersive virtual reality game for testing visual search skills. Our classification system aimed to identify patients with neglect, 'minor atypicality' (indicative of inattention not consistent enough to be labelled as neglect) or non-neglect. This categorization was based on a simple mathematical definition, utilizing gameplay to describe spatial orientation (to the left or right side) and attentional challenge (indicative of search inefficiency). These metrics were benchmarked against a normative model to detect atypical visual search, which refers to gameplay beyond the usual bounds. The combination of neglected side, orientation and challenge factors was used to categorize neglect. We discovered a strong correlation between atypical visual search patterns and neglect risk factors, such as middle cerebral artery stroke, parietal injuries and existing neglect diagnoses (Poisson regression incidence rate ratio = 7.18, 95% confidence interval = 4.41-11.90). In our study, immersive virtual reality-identified neglect in one-fourth of the patients (n = 13, 25.5%), minor atypicality in 17.6% (n = 9) and non-neglect in the majority, 56.9% (n = 29). This contrasts with standard assessments, which detected neglect in 17.6% (n = 9) of cases and had no intermediate category. Our analysis determined six categories of neglect, the most common being left hemispace neglect with above-median orientation and challenge scores. Traditional assessments were not significantly more accurate (accuracy = 84.3%, P = 0.06) than a blanket assumption of non-neglect. Traditional assessments were also relatively insensitive in detecting immersive virtual reality-identified neglect (53.8%), particularly in less severe cases and those involving right-side inattention. Our findings underline the effectiveness of immersive virtual reality in revealing various dimensions of neglect, surpassing traditional methods in sensitivity and detail and operating independently from them. To integrate immersive virtual reality into real-world clinical settings, collaboration with healthcare professionals, patients and other stakeholders is crucial to ensure practical applicability and accessibility.

Gray matter atrophy is constrained by normal structural brain network architecture in depression

BACKGROUND

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

METHODS

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

RESULTS

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

CONCLUSIONS

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Individual deviations from normative electroencephalographic connectivity predict antidepressant response

BACKGROUND

Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders.

METHODS

We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment.

RESULTS

We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses.

CONCLUSIONS

Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment.

TRIAL REGISTRATION

Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.

Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness

Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one's children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

A neuroimaging measure to capture heterogeneous patterns of atrophy in Parkinson's disease and dementia with Lewy bodies

INTRODUCTION

Parkinson's disease (PD) and Dementia with Lewy bodies (DLB) show heterogeneous brain atrophy patterns which group-average analyses fail to capture. Neuroanatomical normative modelling overcomes this by comparing individuals to a large reference cohort. Patient-specific atrophy patterns are measured objectively and summarised to index overall neurodegeneration (the 'total outlier count'). We aimed to quantify patterns of neurodegenerative dissimilarity in participants with PD and DLB and evaluate the potential clinical relevance of total outlier count by testing its association with key clinical measures in PD and DLB.

MATERIALS AND METHODS

We included 108 participants with PD and 61 with DLB. PD participants were subclassified into high and low visual performers as this has previously been shown to stratify those at increased dementia risk. We generated z-scores from T1w-MRI scans for each participant relative to normative regional cortical thickness and subcortical volumes, modelled in a reference cohort (n = 58,836). Outliers (z < -1.96) were aggregated across 169 brain regions per participant. To measure dissimilarity, individuals' Hamming distance scores were calculated. We also examined total outlier counts between high versus low visual performance in PD; and PD versus DLB; and tested associations between these and cognition.

RESULTS

There was significantly greater inter-individual dissimilarity in brain-outlier patterns in PD poor compared to high visual performers (W = 522.5; p < 0.01) and in DLB compared to PD (W = 5649; p < 0.01). PD poor visual performers had significantly greater total outlier counts compared to high (β = -4.73 (SE = 1.30); t = -3.64; p < 0.01) whereas a conventional group-level GLM failed to identify differences. Higher total outlier counts were associated with poorer MoCA (β = -0.55 (SE = 0.27), t = -2.04, p = 0.05) and composite cognitive scores (β = -2.01 (SE = 0.79); t = -2.54; p = 0.02) in DLB, and visuoperception (β = -0.67 (SE = 0.19); t = -3.59; p < 0.01), in PD.

CONCLUSIONS

Neuroanatomical normative modelling shows promise as a clinically informative technique in PD and DLB, where patterns of atrophy are variable.