Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

Polygenic Risk, Psychopathology, and Personalized Functional Brain Network Topography in Adolescence

Importance

Functional brain networks are associated with both behavior and genetic factors. To uncover biological mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development.

Objective

To determine the associations among transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence.

Design, Setting, and Participants

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing longitudinal cohort study of 21 collection sites across the US. This cross-sectional analysis includes ABCD baseline data collected between September 2016 and October 2018. The ABCD Study is a multisite community-based study. The sample is largely recruited through school systems. ABCD exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere with protocol and scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, and anesthesia exposure. Data were analyzed from January 2023 to July 2024.

Exposures

Polygenic risk scores of transdiagnostic genetic factors F1 (PRS-F1) and F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium and UK Biobanks datasets. PRS-F1 indexes liability for common psychiatric symptoms and disorders related to mood disturbance; PRS-F2 indexes liability for rarer forms of mental illness characterized by mania and psychosis.

Main Outcomes and Measures

P-factor derived from bifactor models of youth- and parent-reported mental health assessments and person-specific functional brain network topography derived from functional magnetic resonance imaging scans.

Results

Total participants included 11 873 children aged 9 to 10 years; 5678 (47.8%) were female, and the mean (SD) age was 9.92 (0.62) years. PFN topography was found to be heritable (imaging subsample, n = 7459; 57.1% of vertices: mean h2, 0.35; false discovery rate-corrected P < .05). PRS-F1 was associated with p-factor (European ancestry subsample, n = 5815; r, 0.12; 95% CI, 0.09-0.15; P < .001). Interindividual differences in functional network topography were associated with p-factor (imaging subsample, n = 7459; mean r, 0.12), PRS-F1 (imaging and European ancestry subsample, n = 3982; mean r, 0.05), and PRS-F2 (n = 3982; mean r, 0.08). Cortical maps of p-factor and PRS-F1 regression coefficients were correlated (r, 0.70; P = .003, permutation test, N = 1000).

Conclusions and Relevance

Polygenic risk for transdiagnostic adulthood psychopathology was associated with both p-factor and heritable PFN topography during early adolescence in this study. These results may advance our understanding of the developmental drivers of psychopathology.

Parcellation of individual brains: From group level atlas to precise mapping

Individual brains vary greatly in morphology, connectivity, and organization. Group-level brain parcellations, which do not account for individual variations in brain parcels, are increasingly limited in their applicability, especially given the rapid development of precision medicine. Accurate individual-level brain functional mapping is pivotal for comprehending variations in brain functions and behaviors, the early and precise identification of brain abnormalities, and personalized treatments for neuropsychiatric disorders. Recent advances in neuroimaging and machine learning techniques have led to a surge in studies on the parcellation of individual brains. In this paper, we present an overview of recent advances in the methodologies of individual brain parcellation, including optimization- and learning-based methods. We then introduce comprehensive evaluation metrics to validate individual functional regions, and discuss how individual brain mapping advances neuroscience research and clinical medicine. Finally, major challenges and future directions of individual brain parcellation are summarized. In conclusion, we provide a comprehensive overview of individual brain parcellation methods, validations, and applications, highlighting current challenges and the urgent need for integrated platforms that encompass datasets, methods, and validations.

Beyond the uniform creative brain: Inter-individual variability in functional connectivity correlates with creativity

Creativity, characterized by the pursuit of uniqueness and novelty, highlights the importance of individual variability, which have been a key focus in cognitive and behavioral research on creativity. However, most studies on the neural basis of creativity have primarily focused on consistent patterns of brain activity across individuals, with little attention to the variability in brain function. In this study, inter-subject representational similarity analysis was employed to investigate the relationship between inter-individual variability in resting-state functional connectivity and creative ability. The results revealed significant positive correlations between individual variability in functional connectivity maps of multiple brain regions, including the superior frontal gyrus, orbital gyrus, precuneus, cingulate gyrus, and lateral occipital cortex, and variability in creative ability. Notably, both intra-network variability within the default mode network (DMN) and visual network, as well as inter-network variability among the DMN, visual, sensorimotor, dorsal attention, and fronto-parietal networks, were linked to the variability in creative ability. The variations in functional connectivity patterns effectively distinguished individuals with high creative ability from those with lower ability. By examining creativity from the perspective of individual variability, this study provides new insights into the neural mechanisms underlying creativity.

Individualized cerebellar damage predicts the presence of behavioral disorders in children with brainstem tumors

BACKGROUND

Brainstem tumors often cause intractable neurobehavioral issues, which can be a challenge for patients and surgeons. Research on cerebellar changes in these patients is limited, despite symptoms similar to cerebellar injuries. This study aims to investigate cerebellar damage pattern resulting from brainstem tumors and its association with behavioral disorders.

METHODS

This study enrolled 147 children with brainstem tumors. A U-Net-based segmentation algorithm is used to divide their cerebellums into 26 lobules. And these lobules are then used to build a normative model for assessing individual structural deviations. Furthermore, a behavior prediction model is developed using the total outlier count (tOC) index and cerebellar lobule volume as predictive features.

RESULTS

Over 95% of patients are found to have negative deviations in cerebellar regions, particularly in anterior lobules like Left V. Higher tOC is significantly associated with severe social problems (r = 0.31, p = 0.001) and withdrawal behavior (r = 0.28, p = 0.001). Smaller size of cerebellar regions strongly correlates with more pronounced social problems (r = 0.27, p = 0.007) and withdrawal behavior (r = 0.25, p = 0.015). Notably, lobules Right X, V, IV, VIIB, Left IX, VIII, and X influence social problems, while Left V, Right IV, Vermis VI, and VIII impact withdrawal behavior.

CONCLUSIONS

Our study reveals cerebellar damage patterns in patients with brainstem tumors, emphasizing the role of both anterior and posterior cerebellar lobes in social problems and withdrawal behavior. This research sheds light on the cerebro-brainstem-cerebellar underlying complex behavioral disorders in brainstem tumor patients.

Polygenic Risk, Psychopathology, and Personalized Functional Brain Network Topography in Adolescence

Importance

Functional brain networks are associated with both behavior and genetic factors. To uncover biological mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development.

Objective

To determine the relationships among transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence.

Design

The Adolescent Brain Cognitive Development (ABCD) Study ⍰ is an ongoing longitudinal cohort study of 21 collection sites across the United States. Here, we conduct a cross-sectional analysis of ABCD baseline data, collected 2017-2018.

Setting

The ABCD Study ® is a multi-site community-based study.

Participants

The sample is largely recruited through school systems. Exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere with protocol and scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, and anesthesia exposure.

Exposures

Polygenic risk scores of transdiagnostic genetic factors F1 (PRS-F1) and F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium and UK Biobanks datasets. PRS-F1 indexes liability for common psychiatric symptoms and disorders related to mood disturbance; PRS-F2 indexes liability for rarer forms of mental illness characterized by mania and psychosis.

Main Outcomes and Measures

(1) P-factor derived from bifactor models of youth- and parent-reported mental health assessments. (2) Person-specific functional brain network topography derived from functional magnetic resonance imaging (fMRI) scans.

Results

Total participants included 11,873 youths ages 9-10 years old; 5,678 (47.8%) were female, and the mean (SD) age was 9.92 (0.62) years. PFN topography was found to be heritable ( N =7,459, 57.1% of vertices h 2 p FDR <0.05, mean h 2 =0.35). PRS-F1 was associated with p-factor ( N =5,815, r =0.12, 95% CI [0.09-0.15], p<0.001). Interindividual differences in functional network topography were associated with p-factor ( N =7,459, mean r =0.12), PRS-F1 ( N =3,982, mean r =0.05), and PRS-F2 ( N =3,982, mean r =0.08). Cortical maps of p-factor and PRS-F1 regression coefficients were highly correlated ( r =0.7, p =0.003).

Conclusions and Relevance

Polygenic risk for transdiagnostic adulthood psychopathology is associated with both p-factor and heritable PFN topography during early adolescence. These results advance our understanding of the developmental drivers of psychopathology.

Key Points

Question: What are the relationships among transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence?Findings: In this cross-sectional analysis of the Adolescent Brain Cognitive Development (ABCD) Study ⍰ ( N =11,873, ages 9-10), we found that a PRS of common mood-related psychopathology in adulthood (PRS-F1) was associated with p-factor during early adolescence. Interindividual differences in p-factor, PRS-F1, and PRS-F2 (capturing more severe psychotic disorders in adulthood) were all robustly associated with PFN topography. Meaning: Polygenic risk for transdiagnostic adulthood psychopathology is associated with both p-factor and PFN topography during early adolescence.

Structure-function coupling in network connectivity and associations with negative affectivity in a group of transdiagnostic adolescents

The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated 'domain-specific' factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.

Mapping the neurodevelopmental predictors of psychopathology

Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

Embracing variability in the search for biological mechanisms of psychiatric illness

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations. Embracing such variability is necessary to understand pathophysiological mechanisms and develop more targeted and effective treatments.

Hierarchical individual variation and socioeconomic impact on personalized functional network topography in children

BACKGROUND

The spatial layout of large-scale functional brain networks exhibits considerable inter-individual variability, especially in the association cortex. Research has demonstrated a link between early socioeconomic status (SES) and variations in both brain structure and function, which are further associated with cognitive and mental health outcomes. However, the extent to which SES is associated with individual differences in personalized functional network topography during childhood remains largely unexplored.

METHODS

We used a machine learning approach-spatially regularized non-negative matrix factorization (NMF)-to delineate 17 personalized functional networks in children aged 9-10 years, utilizing high-quality functional MRI data from 6001 participants in the Adolescent Brain Cognitive Development study. Partial least square regression approach with repeated random twofold cross-validation was used to evaluate the association between the multivariate pattern of functional network topography and three SES factors, including family income-to-needs ratio, parental education, and neighborhood disadvantage.

RESULTS

We found that individual variations in personalized functional network topography aligned with the hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicted the three SES factors from unseen individuals. The associations between functional topography and SES factors were also hierarchically organized along the sensorimotor-association cortical axis, exhibiting stronger positive associations in the higher-order association cortex. Additionally, we have made the personalized functional networks publicly accessible.

CONCLUSIONS

These results offer insights into how SES influences neurodevelopment through personalized functional neuroanatomy in childhood, highlighting the cortex-wide, hierarchically organized plasticity of the functional networks in response to diverse SES backgrounds.

The promise of precision functional mapping for neuroimaging in psychiatry

Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.

Multi-echo Acquisition and Thermal Denoising Advances Precision Functional Imaging

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision fMRI data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant specific fMRI.

Identifying developmental changes in functional brain connectivity associated with cognitive functioning in children and adolescents with ADHD

Youth diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) often show deficits in various measures of higher-level cognition, such as, executive functioning. Poorer cognitive functioning in children with ADHD has been associated with differences in functional connectivity across the brain. However, little is known about the developmental changes to the brain's functional properties linked to different cognitive abilities in this cohort. To characterize these changes, we analyzed fMRI data (ADHD = 373, NT = 106) collected while youth between the ages of 6 and 16 watched a short movie-clip. We applied machine learning models to identify patterns of network connectivity in response to movie-watching that differentially predict cognitive abilities in our cohort. Using out-of-sample cross validation, our models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children (ages 6 - 11), but not in adolescents with ADHD (ages 12-16). Connections with the default mode, memory retrieval, and dorsal attention were driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular, and frontoparietal networks were more important in middle childhood. This work demonstrated that machine learning approaches can identify distinct functional connectivity profiles associated with cognitive abilities at different developmental stages in children and adolescents with ADHD.

Reproducible Sex Differences in Personalized Functional Network Topography in Youth

Background

A key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex.

Aims

We aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks.

Method

We leveraged person-specific atlases of functional brain networks defined using nonnegative matrix factorization in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants' sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression.

Results

Sex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants' PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons.

Conclusions

Sex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.

Puberty interacts with sleep and brain network organization to predict mental health

Introduction

Along with pubertal development, the transition to adolescence brings about increased risk for sleep disturbances and mental health problems. Functional connectivity of overlapping large-scale brain networks, such as increased connectivity between the default mode and dorsal attention networks, has been reported to relate to both sleep and mental health problems. Clarifying whether pubertal development interacts with sleep disturbances and functional brain networks to predict mental health may provide information to improve the timing and design of interventions targeting sleep disturbances in adolescents.

Methods

To examine how pubertal status and tempo relate to sleep disturbances and shape the relationship between sleep disturbances and mental health problems, we harnessed a large sample of children aged 10-14 years from the Adolescent Brain and Cognitive Development (ABCD) Study (N ~ 3,000-10,000). We used graph theoretical tools to probe how pubertal development concurrently interacts with sleep disturbances and brain network organization to predict mental health problems.

Results

We found that advanced pubertal status, but not pubertal tempo, predicted sleep disturbances; however, both pubertal status and tempo interact with sleep disturbances to predict mental health problems and engage in three-way interactions with sleep and brain network organization to predict mental health problems.

Discussion

Overall, this work suggests that less advanced pubertal status and slower tempo are risk factors for the strongest links between sleep disturbances, brain organization, and mental health problems. Further, our findings speak to the importance of accounting for interactions in the constellation of factors that surround complex behavioral and clinical syndromes, here internalizing and externalizing disorders, and provide new context to consider for targeted interventions.

Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients

Background

Antipsychotic medications offer limited long-term benefit to about 30% of patients with schizophrenia. We aimed to explore the individual-specific imaging markers to predict 1-year treatment response of schizophrenia.

Methods

Structural morphology and functional topological features related to treatment response were identified using an individualized parcellation analysis in conjunction with machine learning (ML). We performed dimensionality reductions using the Pearson correlation coefficient and three feature selection analyses and classifications using 10 ML classifiers. The results were assessed through a 5-fold cross-validation (training and validation cohorts, n = 51) and validated using the external test cohort (n = 17).

Results

ML algorithms based on individual-specific brain network proved more effective than those based on group-level brain network in predicting outcomes. The most predictive features based on individual-specific parcellation involved the GMV of the default network and the degree of the control, limbic, and default networks. The AUCs for the training, validation, and test cohorts were 0.947, 0.939, and 0.883, respectively. Additionally, the prediction performance of the models constructed by the different feature selection methods and classifiers showed no significant differences.

Conclusion

Our study highlighted the potential of individual-specific network parcellation in treatment resistant schizophrenia prediction and underscored the crucial role of feature attributes in predictive model accuracy.

A precision functional atlas of personalized network topography and probabilities

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

pNet: A toolbox for personalized functional networks modeling

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

Development of white matter fiber covariance networks supports executive function in youth

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Functional topography of the neocortex predicts covariation in complex cognitive and basic motor abilities

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

Activation of Internal Correctness Monitoring Circuitry in Youths With Psychosis Spectrum Symptoms

BACKGROUND

Self-directed performance monitoring is a critical contributor to cognitive performance and general functioning and is impacted by psychiatric symptoms and personality traits, but has been understudied in psychosis-risk states. We have shown that ventral striatum (VS) responds to correctness during cognitive tasks where no explicit feedback is required, and this intrinsic reinforcement response is reduced in schizophrenia.

METHODS

Here, we examined this phenomenon in youths (n = 796, age range 11-22 years) from the Philadelphia Neurodevelopmental Cohort (PNC) performing a working memory functional magnetic resonance imaging task. We hypothesized that VS would respond to internal correctness monitoring, while classic salience network regions, such as dorsal anterior cingulate cortex and anterior insular cortex, would reflect internal error monitoring and that these responses would increase with age. We expected that neurobehavioral measures of performance monitoring would be reduced in youths with subclinical psychosis spectrum features and would correlate with amotivation severity.

RESULTS

Supporting these hypotheses, we found correct>incorrect activation in VS and incorrect>correct activation in anterior cingulate cortex and anterior insular cortex. Furthermore, VS activation was positively correlated with age, reduced in youths with psychosis spectrum features, and inversely correlated with amotivation. However, these patterns were not significant in anterior cingulate cortex and anterior insular cortex.

CONCLUSIONS

These findings advance our understanding of the neural underpinnings of performance monitoring and its impairment in adolescents with psychosis spectrum features. Such understanding can facilitate investigation of the developmental trajectory of normative and aberrant performance monitoring; contribute to early identification of youths at elevated risk for poor academic, occupational, or psychiatric outcomes; and provide potential targets for therapeutic development.

Development of top-down cortical propagations in youth

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

Computing personalized brain functional networks from fMRI using self-supervised deep learning

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

Functional Topography of the Neocortex Predicts Covariation in Complex Cognitive and Basic Motor Abilities

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g., hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

The promise of a model-based psychiatry: building computational models of mental ill health

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.