Reproducible brain-wide association studies require thousands of individuals

Eye-tracking measurement of attention bias to social threat among youth: A replication and extension study

Attentional bias to social threat cues has been linked to heightened anxiety and irritability in youth. Yet, inconsistent methodology has limited replication and led to mixed findings. The current study aims to 1) replicate and extend two previous pediatric studies demonstrating a relationship between negative affectivity and attentional bias to social threat and 2) examine the test-retest reliability of an eye-tracking paradigm among a subsample of youth. Attention allocation to negative versus non-negative emotional faces was measured using a free-viewing eye-tracking task among youth (N=185 total, 60% female, M age=13.10 years, SD age=2.77) with three face-pair conditions: happy-angry, neutral-disgust, sad-happy. Replicating procedures of two previous studies, linear mixed-effects models compared attention bias between children with anxiety disorders and healthy controls. Bifactor analysis was used to parse shared versus unique facets of general negative affectivity (i.e., anxiety, irritability), which were then examined in relation to attention bias. Test-retest reliability of the bias-index was estimated among a subsample of youth (N=36). No significant differences in attention allocation or bias emerged between anxiety and healthy control groups. While general negative affectivity across the sample was not associated with attention bias, there was a positive relationship for anxiety and irritability on duration of attention allocation toward negative faces. Test-retest reliability for attention bias was moderate (r=0.50, p<.01). While anxiety-related findings from the two previous studies were not replicated, the relationship between attention bias and facets of negative affect suggests a potential target for treatment. Evidence for test-retest reliability encourages future use of the eye-tracking task for researchers.

Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs

Recent advances in generative models have paved the way for enhanced generation of natural and medical images, including synthetic brain MRIs. However, the mainstay of current AI research focuses on optimizing synthetic MRIs with respect to visual quality (such as signal-to-noise ratio) while lacking insights into their relevance to neuroscience. To generate high-quality T1-weighted MRIs relevant for neuroscience discovery, we present a two-stage Diffusion Probabilistic Model (called BrainSynth) to synthesize high-resolution MRIs conditionally-dependent on metadata (such as age and sex). We then propose a novel procedure to assess the quality of BrainSynth according to how well its synthetic MRIs capture macrostructural properties of brain regions and how accurately they encode the effects of age and sex. Results indicate that more than half of the brain regions in our synthetic MRIs are anatomically plausible, i.e., the effect size between real and synthetic MRIs is small relative to biological factors such as age and sex. Moreover, the anatomical plausibility varies across cortical regions according to their geometric complexity. As is, the MRIs generated by BrainSynth significantly improve the training of a predictive model to identify accelerated aging effects in an independent study. These results indicate that our model accurately capture the brain's anatomical information and thus could enrich the data of underrepresented samples in a study. The code of BrainSynth will be released as part of the MONAI project at https://github.com/Project-MONAI/GenerativeModels.

Ventral striatal-cingulate resting-state functional connectivity in healthy adolescents relates to later depression symptoms in adulthood

BACKGROUND

Depression is a significant public health concern. Identifying biopsychosocial risk factors for depression is important for developing targeted prevention. Studies have demonstrated that blunted striatal activation during reward processing is a risk factor for depression; however, few have prospectively examined whether adolescent reward-related resting-state functional connectivity (rsFC) predicts depression symptoms in adulthood and how this relates to known risk factors (e.g., childhood trauma).

METHODS

At baseline, 66 adolescents (mean age = 14.7, SD = 1.4, 68 % female) underwent rsFC magnetic resonance imaging and completed the Children's Depression Inventory (CDI). At follow-up (mean time between adolescent scan and adult follow-up = 10.1 years, SD = 1.6, mean adult age = 24.8 years, SD = 1.7), participants completed the Childhood Trauma Questionnaire (CTQ) and Beck Depression Inventory- Second Edition (BDI-2). Average rsFC was calculated between nodes in mesocorticolimbic reward circuitry: ventral striatum (VS), rostral anterior cingulate cortex (rACC), medial orbitofrontal cortex, and ventral tegmental area. Linear regressions assessed associations between rsFC, BDI-2, and CTQ, controlling for adolescent CDI, sex assigned at birth, and scan age (Bonferroni corrected).

RESULTS

Greater childhood trauma was associated with higher adulthood depression symptoms. Stronger VS-rACC rsFC during adolescence was associated with greater depression symptoms in adulthood and greater childhood trauma.

LIMITATIONS

The small sample size, limited depression severity, and seed-based approach are limitations.

CONCLUSIONS

The associations between adolescent striatal-cingulate rsFC and childhood trauma and adult depression symptoms suggest this connectivity may be an early neurobiological risk factor for depression and that early life experience plays an important role. Increased VS-rACC connectivity may represent an over-regulatory response on the striatum, commonly reported in depression, and warrants further investigation.

Exploring the mediating role of the ventral attention network and somatosensory motor network in the association between childhood trauma and depressive symptoms in major depressive disorders

BACKGROUND

Childhood trauma is closely tied to adult depression, but the neurobiological mechanisms remain unclear. Previous studies suggested associations between depression and large-scale brain networks such as the Ventral Attention Network (VAN) and Somatosensory Motor Network (SMN). This study hypothesized that functional connectivity (FC) within and between these networks mediates the link between childhood trauma and adult depression.

METHODS

The Childhood Trauma Questionnaire (CTQ) assessed developmental experiences, and the Hamilton Rating Scale for Depression (HAMD-17) gauged depressive symptoms. Resting-state functional magnetic resonance imaging (fMRI) analyzed FC within and between the VAN and SMN.

RESULTS

Depression group exhibited significantly higher HAMD and CTQ scores, as well as elevated FC within the VAN and between the VAN and SMN (P < 0.05). Positive correlations were found between HAMD total score and FC within the VAN (P < 0.05, r = 0.35) and between the VAN and SMN (P < 0.05, r = 0.34), as well as with CTQ total score (P < 0.05, r = 0.27). Positive correlations were also observed between CTQ total score and FC within the VAN (P < 0.05, r = 0.31) and between the VAN and SMN (P < 0.05, r = 0.29). In the mediation model, FC within and between the VAN and SMN significantly mediated childhood trauma and depression.

LIMITATIONS

The cross-sectional design limits causal inference. The sample size for different trauma types is relatively small, urging caution in generalizing findings.

CONCLUSIONS

The study underscores the association between depression severity, VAN dysfunction, abnormal VAN-SMN FC, and childhood trauma. These findings contribute to understanding the neurobiological mechanisms underlying childhood trauma and depression.

Functional MRI of the Brainstem for Assessing Its Autonomic Functions: From Imaging Parameters and Analysis to Functional Atlas

BACKGROUND

The brainstem is a crucial component of the central autonomic nervous (CAN) system. Functional MRI (fMRI) of the brainstem remains challenging due to a range of factors, including diverse imaging protocols, analysis, and interpretation.

PURPOSE

To develop an fMRI protocol for establishing a functional atlas in the brainstem.

STUDY TYPE

Prospective cross-sectional study.

SUBJECTS

Ten healthy subjects (four males, six females).

FIELD STRENGTH/SEQUENCE

Using a 3.0 Tesla MR scanner, we acquired T1-weighted images and three different fMRI scans using fMRI protocols of the optimized functional Imaging of Brainstem (FIBS), the Human Connectome Project (HCP), and the Adolescent Brain Cognitive Development (ABCD) project.

ASSESSMENT

The temporal signal-to-noise-ratio (TSNR) of fMRI data was compared between the FIBS, HCP, and ABCD protocols. Additionally, the main normalization algorithms (i.e., FSL-FNIRT, SPM-DARTEL, and ANTS-SyN) were compared to identify the best approach to normalize brainstem data using root-mean-square (RMS) error computed based on manually defined reference points. Finally, a functional autonomic brainstem atlas that maps brainstem regions involved in the CAN system was defined using meta-analysis and data-driven approaches.

STATISTICAL TESTS

ANOVA was used to compare the performance of different imaging and preprocessing pipelines with multiple comparison corrections (P ≤ 0.05). Dice coefficient estimated ROI overlap, with 50% overlap between ROIs identified in each approach considered significant.

RESULTS

The optimized FIBS protocol showed significantly higher brainstem TSNR than the HCP and ABCD protocols (P ≤ 0.05). Furthermore, FSL-FNIRT RMS error (2.1 ± 1.22 mm; P ≤ 0.001) exceeded SPM (1.5 ± 0.75 mm; P ≤ 0.01) and ANTs (1.1 ± 0.54 mm). Finally, a set of 12 final brainstem ROIs with dice coefficient ≥0.50, as a step toward the development of a functional brainstem atlas.

DATA CONCLUSION

The FIBS protocol yielded more robust brainstem CAN results and outperformed both the HCP and ABCD protocols.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

"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.

Default mode network-associated intrinsic connectivity relates to individual learnability differences in errorless and trial-and-error learning

The intrinsic functional network architecture accounts for task-evoked brain activity changes and variabilities in cognitive performance. Relationships between the intrinsic functional network architecture and task performance or learning ability have been previously reported. However, the relationships between learning benefits and the characteristics of intrinsic functional network architecture for different types of learning methods remain unclear. In this study, we used graph theoretical analysis to examine the relationships between intrinsic functional network connectivity and learning benefits in two well-known learning methods in the field of cognitive rehabilitation-errorless learning (EL learning) and trial-and-error learning (T&E learning). We focused on the default mode network (DMN) as a task-relevant network, which can differentiate between EL and T&E learning and was found to be more important for T&E learning in a previous study. Participants performed a color-name association task with both learning methods. The graph metrics used were within-network connectivity and efficiency for the DMN. Within-DMN connectivity and DMN efficiency showed a significantly weak positive correlation with T&E scores but not with EL scores. These findings show that the intrinsic integration strength within the DMN relates to individuals' learnability through the T&E method.

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity.

Neuroimaging Biomarkers for Drug Discovery and Development in Schizophrenia

Schizophrenia is a chronic mental illness that affects up to 1% of the population. While efficacious therapies are available for positive symptoms, effective treatment of cognitive and negative symptoms remains an unmet need after decades of research. New developments in the field of neuroimaging are accelerating our knowledge gain regarding the underlying pathophysiology of symptoms in schizophrenia and psychosis spectrum disorders, inspiring new targets for drug development. However, no validated and qualified biomarkers are currently available to support the development of new therapeutics. This review summarizes the current use of neuroimaging technology in clinical drug development for psychotic disorders. As exemplified by drug development programs that target NMDA receptor hypofunction, neuroimaging results play a critical role in target discovery and establishing target engagement and dose selection. Furthermore, pharmacological neuroimaging may provide response biomarkers that allow for early decision making in proof-of-concept studies that leverage pharmacological challenge models in healthy volunteers. That said, while response and predictive biomarkers are starting to be evaluated in patient populations, they continue to play a limited role. Novel approaches to neuroimaging data acquisition and analysis may aid the establishment of biomarkers that are predictive at the individual level in the future. Nevertheless, various gaps in knowledge need to be addressed and biomarkers need to be validated to establish them as "fit for purpose" in drug development.

Subcortical volumes in offspring with a multigenerational family history of depression - A study across two cohorts

BACKGROUND

Having multiple previous generations with depression in the family increases offspring risk for psychopathology. Parental depression has been associated with smaller subcortical brain volumes in their children, but whether two prior generations with depression is associated with further decreases is unclear.

METHODS

Using two independent cohorts, 1) a Three-Generation Study (TGS, N = 65) with direct clinical interviews of adults and children across all three generations, and 2) the Adolescent Brain Cognitive Development Study (ABCD, N = 10,626) of 9-10 year-old children with family history assessed by a caregiver, we tested whether having more generations of depression in the family was associated with smaller subcortical volumes (using structural MRI).

RESULTS

In TGS, caudate, pallidum and putamen showed decreasing volumes with higher familial risk for depression. Having a parent and a grandparent with depression was associated with decreased volume compared to having no familial depression in these regions. Putamen volume was associated with depression at eight-year follow-up. In ABCD, smaller pallidum and putamen were associated with family history, which was driven by parental depression, regardless of grandparental depression.

LIMITATIONS

Discrepancies between cohorts could be due to interview type (clinical or self-report) and informant (individual or common informant), sample size or age. Future analyses of follow-up ABCD waves will be able to assess whether effects of grandparental depression on brain markers become more apparent as the children enter young adulthood.

CONCLUSIONS

Basal ganglia regional volumes are significantly smaller in offspring with a family history of depression in two independent cohorts.

Let's fail better: Using philosophical tools to improve neuroscientific research in psychiatry

Despite predictions that neuroscientific discoveries would revolutionize psychiatry, decades of research have not yet led to clinically significant advances in psychiatric care. For this reason, an increasing number of researchers are recognizing the limitations of a purely biomedical approach in psychiatric research. These researchers call for reevaluating the conceptualization of mental disorders and argue for a non-reductionist approach to mental health. The aim of this paper is to discuss philosophical assumptions that underly neuroscientific research in psychiatry and offer practical tools to researchers for overcoming potential conceptual problems that are derived from those assumptions. Specifically, we will discuss: the analogy problem, questioning whether mental health problems are equivalent to brain disorders, the normativity problem, addressing the value-laden nature of psychiatric categories and the priority problem, which describes the level of analysis (e.g., biological, psychological, social, etc.) that should be prioritized when studying psychiatric conditions. In addition, we will explore potential strategies to mitigate practical problems that might arise due to these implicit assumptions. Overall, the aim of this paper is to suggest philosophical tools of practical use for neuroscientists, demonstrating the benefits of a closer collaboration between neuroscience and philosophy.

Investigating cross-sectional and longitudinal relationships between brain structure and distinct dimensions of externalizing psychopathology in the ABCD sample

Externalizing psychopathology in childhood is a predictor of poor outcomes across the lifespan. Children exhibiting elevated externalizing symptoms also commonly show emotion dysregulation and callous-unemotional (CU) traits. Examining cross-sectional and longitudinal neural correlates across dimensions linked to externalizing psychopathology during childhood may clarify shared or distinct neurobiological vulnerability for psychopathological impairment later in life. We used tabulated brain structure and behavioural data from baseline, year 1, and year 2 timepoints of the Adolescent Brain Cognitive Development Study (ABCD; baseline n = 10,534). We fit separate linear mixed effect models to examine whether baseline brain structures in frontolimbic and striatal regions (cortical thickness or subcortical volume) were associated with externalizing symptoms, emotion dysregulation, and/or CU traits at baseline and over a two-year period. The most robust relationships found at the cross-sectional level was between cortical thickness in the right rostral middle frontal gyrus and bilateral pars orbitalis was positively associated with CU traits (β = |0.027-0.033|, pcorrected = 0.009-0.03). Over the two-year follow-up period, higher baseline cortical thickness in the left pars triangularis and rostral middle frontal gyrus predicted greater decreases in externalizing symptoms ((F = 6.33-6.94, pcorrected = 0.014). The results of the current study suggest that unique regions within frontolimbic and striatal networks may be more strongly associated with different dimensions of externalizing psychopathology. The longitudinal findings indicate that brain structure in early childhood may provide insight into structural features that influence behaviour over time.

Brain activity associated with emotion regulation predicts individual differences in working memory ability

Previous behavioral research has found that working memory is associated with emotion regulation efficacy. However, there has been mixed evidence as to whether the neural mechanisms between emotion regulation and working memory overlap. The present study tested the prediction that individual differences on the working memory subtest of the Weschler Adult Intelligence Scale (WAIS-IV) could be predicted from the pattern of brain activity produced during emotion regulation in regions typically associated with working memory, such as the dorsal lateral prefrontal cortex (dlPFC). A total of 101 participants completed an emotion regulation fMRI task in which they either viewed or reappraised negative images. Participants also completed working memory test outside the scanner. A whole brain covariate analysis contrasting the reappraise negative and view negative BOLD response found that activity in the right dlPFC positively related to working memory ability. Moreover, a multivoxel pattern analysis approach using tenfold cross-validated support vector regression in regions-of-interest associated with working memory, including bilateral dlPFC, demonstrated that we could predict individual differences in working memory ability from the pattern of activity associated with emotion regulation. These findings support the idea that emotion regulation shares underlying cognitive processes and neural mechanisms with working memory, particularly in the dlPFC.

Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population

BACKGROUND

Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum.

METHODS

In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis.

RESULTS

Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity.

LIMITATIONS

Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits.

CONCLUSIONS

Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity.

The dorsomedial prefrontal cortex promotes self-control by inhibiting the egocentric perspective

The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate-the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.

Heterogenous brain activations across individuals localize to a common network

Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.

Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients-both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p = 2e-10, Hedges' g = 1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC = 0.62) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC = 0.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

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.

The status of MRI databases across the world focused on psychiatric and neurological disorders

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets

BACKGROUND

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure.

METHODS

Using three population-based datasets (N = 1813), we trained the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N = 378, N = 334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association.

FINDINGS

Sleep quality could predict individual DSS (r = 0.43, R2 = 0.18, rMSE = 2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R2 = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS.

INTERPRETATION

Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS.

FUNDING

The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.

Trait reward sensitivity modulates connectivity with the temporoparietal junction and Anterior Insula during strategic decision making

Many decisions happen in social contexts such as negotiations, yet little is understood about how people balance fairness versus selfishness. Past investigations found that activation in brain areas involved in executive function and reward processing was associated with people offering less with no threat of rejection from their partner, compared to offering more when there was a threat of rejection. However, it remains unclear how trait reward sensitivity may modulate activation and connectivity patterns in these situations. To address this gap, we used task-based fMRI to examine the relation between reward sensitivity and the neural correlates of bargaining choices. Participants (N = 54) completed the Sensitivity to Punishment (SP)/Sensitivity to Reward (SR) Questionnaire and the Behavioral Inhibition System/Behavioral Activation System scales. Participants performed the Ultimatum and Dictator Games as proposers and exhibited strategic decisions by being fair when there was a threat of rejection, but being selfish when there was not a threat of rejection. We found that strategic decisions evoked activation in the Inferior Frontal Gyrus (IFG) and the Anterior Insula (AI). Next, we found elevated IFG connectivity with the Temporoparietal junction (TPJ) during strategic decisions. Finally, we explored whether trait reward sensitivity modulated brain responses while making strategic decisions. We found that people who scored lower in reward sensitivity made less strategic choices when they exhibited higher AI-Angular Gyrus connectivity. Taken together, our results demonstrate how trait reward sensitivity modulates neural responses to strategic decisions, potentially underscoring the importance of this factor within social and decision neuroscience.

Network-level enrichment provides a framework for biological interpretation of machine learning results

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

Amygdala connectivity is associated with withdrawn/depressed behavior in a large sample of children from the Adolescent Brain Cognitive Development (ABCD) Study®

Many psychopathologies tied to internalizing symptomatology emerge during adolescence, therefore identifying neural markers of internalizing behavior in childhood may allow for early intervention. We utilized data from the Adolescent Brain and Cognitive Development (ABCD) Study® to evaluate associations between cortico-amygdalar functional connectivity, polygenic risk for depression (PRSD), traumatic events experienced, internalizing behavior, and internalizing subscales: withdrawn/depressed behavior, somatic complaints, and anxious/depressed behaviors. Data from 6371 children (ages 9-11) were used to analyze amygdala resting-state fMRI connectivity to Gordon parcellation based whole-brain regions of interest (ROIs). Internalizing behaviors were measured using the parent-reported Child Behavior Checklist. Linear mixed-effects models were used to identify patterns of cortico-amygdalar connectivity associated with internalizing behaviors. Results indicated left amygdala connections to auditory, frontoparietal network (FPN), and dorsal attention network (DAN) ROIs were significantly associated with withdrawn/depressed symptomatology. Connections relevant for withdrawn/depressed behavior were linked to social behaviors. Specifically, amygdala connections to DAN were associated with social anxiety, social impairment, and social problems. Additionally, an amygdala connection to the FPN ROI and the auditory network ROI was associated with social anxiety and social problems, respectively. Therefore, it may be important to account for social behaviors when looking for brain correlates of depression.

The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression

Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Tactile stimulation designs adapted to clinical settings result in reliable fMRI-based somatosensory digit maps

Movement constraints in stroke survivors are often accompanied by additional impairments in related somatosensory perception. A complex interplay between the primary somatosensory and motor cortices is essential for adequate and precise movements. This necessitates investigating the role of the primary somatosensory cortex in movement deficits of stroke survivors. The first step towards this goal could be a fast and reliable functional Magnetic Resonance Imaging (fMRI)-based mapping of the somatosensory cortex applicable for clinical settings. Here, we compare two 3 T fMRI-based somatosensory digit mapping techniques adapted for clinical usage in seven neurotypical volunteers and two sessions, to assess their validity and retest-reliability. Both, the traveling wave and the blocked design approach resulted in complete digit maps in both sessions of all participants, showing the expected layout. Similarly, no evidence for differences in the volume of activation, nor the activation overlap between neighboring activations could be detected, indicating the general feasibility of the clinical adaptation and their validity. Retest-reliability, indicated by the Dice coefficient, exhibited reasonable values for the spatial correspondence of single digit activations across sessions, but low values for the spatial correspondence of the area of overlap between neighboring digits across sessions. Parameters describing the location of the single digit activations exhibited very high correlations across sessions, while activation volume and overlap only exhibited medium to low correlations. The feasibility and high retest-reliabilities for the parameters describing the location of the single digit activations are promising concerning the implementation into a clinical context to supplement diagnosis and treatment stratification in upper limb stroke patients.

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.

The morphospace of the brain-cognition organisation

Over the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space-morphospace capturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others-the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.

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.

Midlife dynamics of white matter architecture in lexical production

We aimed to examine the white matter changes associated with lexical production difficulties, beginning in midlife with increased naming latencies. To delay lexical production decline, middle-aged adults may rely on domain-general and language-specific compensatory mechanisms proposed by the LARA model (Lexical Access and Retrieval in Aging). However, the white matter changes supporting these mechanisms remains largely unknown. Using data from the CAMCAN cohort, we employed an unsupervised and data-driven methodology to examine the relationships between diffusion-weighted imaging and lexical production. Our findings indicate that midlife is marked by alterations in brain structure within distributed dorsal, ventral, and anterior cortico-subcortical networks, marking the onset of lexical production decline around ages 53-54. Middle-aged adults may initially adopt a "semantic strategy" to compensate for lexical production challenges, but this strategy seems compromised later (ages 55-60) as semantic control declines. These insights underscore the interplay between domain-general and language-specific processes in the trajectory of lexical production performance in healthy aging and hint at potential biomarkers for language-related neurodegenerative pathologies.

Psychopathy and medial frontal cortex: A systematic review reveals predominantly null relationships

Theories have posited that psychopathy is caused by dysfunction in the medial frontal cortex, including ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and dorsomedial prefrontal cortex (dmPFC). Recent reviews have questioned the reproducibility of neuroimaging findings within this field. We conducted a systematic review to describe the consistency of magnetic resonance imaging (MRI) findings according to anatomical subregion (vmPFC, ACC, dmPFC), experimental task, psychopathy assessment, study power, and peak coordinates of significant effects. Searches of PsycInfo and MEDLINE databases produced 77 functional and 24 structural MRI studies that analyzed the medial frontal cortex in relation to psychopathy in adult samples. Findings were predominantly null (85.4 % of 1573 tests across the three medial frontal regions). Studies with higher power observed null effects at marginally lower rates. Finally, peak coordinates of significant effects were widely dispersed. The evidence failed to support theories positing the medial frontal cortex as a consistent neural correlate of psychopathy. Theory and methods in the field should be revised to account for predominantly null neuroimaging findings.

Towards a neurodevelopmental model of bipolar disorder: a critical review of trait- and state-related functional neuroimaging in adolescents and young adults

Neurodevelopmental mechanisms are increasingly implicated in bipolar disorder (BD), highlighting the importance of their study in young persons. Neuroimaging studies have demonstrated a central role for frontotemporal corticolimbic brain systems that subserve processing and regulation of emotions, and processing of reward in adults with BD. As adolescence and young adulthood (AYA) is a time when fully syndromal BD often emerges, and when these brain systems undergo dynamic maturational changes, the AYA epoch is implicated as a critical period in the neurodevelopment of BD. Functional magnetic resonance imaging (fMRI) studies can be especially informative in identifying the functional neuroanatomy in adolescents and young adults with BD (BDAYA) and at high risk for BD (HR-BDAYA) that is related to acute mood states and trait vulnerability to the disorder. The identification of early emerging brain differences, trait- and state-based, can contribute to the elucidation of the developmental neuropathophysiology of BD, and to the generation of treatment and prevention targets. In this critical review, fMRI studies of BDAYA and HR-BDAYA are discussed, and a preliminary neurodevelopmental model is presented based on a convergence of literature that suggests early emerging dysfunction in subcortical (e.g., amygdalar, striatal, thalamic) and caudal and ventral cortical regions, especially ventral prefrontal cortex (vPFC) and insula, and connections among them, persisting as trait-related features. More rostral and dorsal cortical alterations, and bilaterality progress later, with lateralization, and direction of functional imaging findings differing by mood state. Altered functioning of these brain regions, and regions they are strongly connected to, are implicated in the range of symptoms seen in BD, such as the insula in interoception, precentral gyrus in motor changes, and prefrontal cortex in cognition. Current limitations, and outlook on the future use of neuroimaging evidence to inform interventions and prevent the onset of mood episodes in BDAYA, are outlined.

Designing for data sharing: Considerations for advancing health equity in data management and dissemination

Data sharing, the act of making scientific research data available to others, can accelerate innovation and discoveries, and ultimately enhance public health. The National Cancer Institute Implementation Science Centers in Cancer Control convened a diverse group of research scientists, practitioners, and community partners in three interactive workshops (May-June 2022) to identify and discuss factors that must be considered when designing research for equitable data sharing with a specific emphasis on implementation science and social, behavioral, and population health research. This group identified and operationalized a set of seven key considerations for equitable data sharing-conceptualized as an inclusive process that fairly includes the perspectives and priorities of all partners involved in and impacted by data sharing, with consideration of ethics, history, and benefits-that were integrated into a framework. Key data-sharing components particularly important for health equity included: elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and adverse impact of data sharing on diverse beneficiaries of health data and research.

Polygenic Risk Underlies Youth Psychopathology and Personalized Functional Brain Network Topography

Importance

Functional brain networks are associated with both behavior and genetic factors. To uncover clinically translatable 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 relationship between 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, 57.06% 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 is the relationship between 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 psychopathology in adulthood (PRS-F1) was associated with p-factor during early adolescence. Interindividual differences in p-factor, PRS-F1, and PRS-F2 (capturing rarer psychopathology 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.

Assessing neurocognitive maturation in early adolescence based on baby and adult functional brain landscapes

Adolescence is a period of growth in cognitive performance and functioning. Recently, data-driven measures of brain-age gap, which can index cognitive decline in older populations, have been utilized in adolescent data with mixed findings. Instead of using a data-driven approach, here we assess the maturation status of the brain functional landscape in early adolescence by directly comparing an individual's resting-state functional connectivity (rsFC) to the canonical early-life and adulthood communities. Specifically, we hypothesized that the degree to which a youth's connectome is better captured by adult networks compared to infant/toddler networks is predictive of their cognitive development. To test this hypothesis across individuals and longitudinally, we utilized the Adolescent Brain Cognitive Development (ABCD) Study at baseline (9-10 years; n = 6,489) and 2-year-follow-up (Y2: 11-12 years; n = 5,089). Adjusted for demographic factors, our anchored rsFC score (AFC) was associated with better task performance both across and within participants. AFC was related to age and aging across youth, and change in AFC statistically mediated the age-related change in task performance. In conclusion, we showed that a model-fitting-free index of the brain at rest that is anchored to both adult and baby connectivity landscapes predicts cognitive performance and development in youth.

Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models

ABSTRACT

Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. We conducted a survey of 57 previously published articles that included neuroimaging-based predictive modeling of pain, comparing classification and prediction performance based on the following modeling variables-the levels of data, spatial scales, idiographic vs population models, and sample sizes. The findings revealed a preference for population-level modeling with brain-wide features, aligning with the goal of clinical translation of neuroimaging biomarkers. However, a systematic evaluation of the influence of different modeling options was hindered by a limited number of independent test results. This prompted us to conduct benchmark analyses using a locally collected functional magnetic resonance imaging dataset (N = 124) involving an experimental thermal pain task. The results demonstrated that data levels, spatial scales, and sample sizes significantly impact model performance. Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.

Functional brain connectivity changes associated with day-to-day fluctuations in affective states

Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models' generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months.

The effects of APOEe4 allele on cerebral structure, function, and related interactions with cognition in young adults

In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer's Disease (AD) on brain in pre-symptomatic stages. We investigated the neuroimaging correlates of the APOEe4 genetic risk factor for AD in young adulthood, its relationship with cognition, and potential effects of other variables on the findings. While conventional volumetric analyses revealed no consistent differences, more sophisticated analyses identified subtle structural differences between APOEe4 carriers and non-carriers. Findings from diffusion studies were limited, but functional studies demonstrated consistent alterations in connectivity and activity. The complex relationship between APOE genotype, neuroimaging variables, and cognition revealed no consensus on the directionality of findings. Methodological choices, including analytical approaches, sample size, and the influence of other genes, gender, and ethnicity, varied across studies, impacting comparability and generalizability. Recommendations for future research include multimodal and longitudinal imaging, standardisation of pipelines, advanced analytical techniques, and collaborative data pooling.

MRI economics: Balancing sample size and scan duration in brain wide association studies

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan time given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan time per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan time are broadly interchangeable up to 20-30 min of data. However, the returns of scan time diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed overhead costs associated with each participant (e.g., recruitment, non-imaging measures), prediction accuracy in many small-scale and some large-scale BWAS might benefit from longer scan time than typically assumed. These results generalize across phenotypic domains, scanners, acquisition protocols, racial groups, mental disorders, age groups, as well as resting-state and task-state functional connectivity. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations maximize sample size, at the expense of scan time, which can result in sub-optimal prediction accuracies and inefficient use of resources. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

DeepResBat: Deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Diminished functional segregation and resilience are associated with symptomatic severity and cognitive impairments in schizophrenia: a large-scale study

Background

The research findings on the topological properties of functional connectomes (TP-FCs) in patients with schizophrenia (SZPs) exhibit inconsistencies and contradictions, which can be attributed to limitations such as small sample sizes and heterogeneous data processing techniques.

Aims

To address these limitations, we conducted a large-scale study. Uniform data processing flows were employed to investigate the aberrant TP-FCs and the associations between TP-FCs and symptoms or cognitions (A-TP-SCs) in SZPs.

Methods

The large-scale study included six datasets from four sites, involving 497 SZPs and 374 healthy controls (HCs). A uniform process for imaging data preprocessing and functional connectivity matrix configuration was used. ComBat was employed for data harmonisation, and various TPs were calculated. We explored between-group differences in brain functional integration (FI) and functional segregation (FS) measured with TP-FCs, and conducted partial correlation analyses, with adjustments for age, gender and educational level, to identify A-TP-SCs.

Results

Compared with random networks and HCs, SZPs maintained small-worldness and global FI capacity despite their compromised global FS capacity and resilience. A decline in nodal FI and FS capacity was observed in sensory areas, whereas an increase in nodal FI capacity was found in regions associated with cognition and information integration. In addition, associations between TP-FCs and positive symptoms, negative symptoms or cognitive functions including speed of processing, visual learning and the ability to inhibit cognitive interference were identified in SZPs.

Conclusions

The identified A-TP-SCs verified that reductions in FS and resilience indicated pathological impairments in schizophrenia. The A-TP-SCs or TP-FCs, which measured the same attributes of the functional connectomes, exhibited high internal consistency, robustly reinforcing these findings.

Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients- both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p=2e-10, Hedges' g=1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC=.61) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC=.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

Alterations of perfusion and functional connectivity of the cingulate motor area are associated with psychomotor retardation in major depressive disorder

Psychomotor retardation, characterized by slowing of speech, thoughts, and a decrease of movements, is frequent in patients with major depressive disorder (MDD). However, its neurobiological correlates are still poorly understood. This study aimed to explore if cerebral blood flow (CBF) and resting state functional connectivity (rs-FC) of the motor network are altered in patients with MDD and if these changes are associated with psychomotor retardation. Thirty-six right-handed patients with depression and 19 right-handed healthy controls (HC) that did not differ regarding age and sex underwent arterial spin labelling (ASL) and resting-state functional MRI (rs-fMRI) scans. Psychomotor retardation was assessed with the motoric items of the core assessment of psychomotor change (CORE) questionnaire. Patients with MDD had more pronounced psychomotor retardation scores than HC. Patients with MDD had reduced CBF in bilateral cingulate motor area (CMA) and increased resting-state functional connectivity (rs-FC) between the cluster in the CMA and a cluster localized in bilateral supplementary motor areas (SMA). Furthermore, increased rs-FC between the CMA and the left SMA was associated with more pronounced psychomotor retardation. Our results suggest that reduced perfusion of the CMA and increased rs-FC between the CMA and the SMA are associated with psychomotor retardation in patients with depression.

Transdiagnostic Symptom Domains are Associated with Head Motion During Multimodal Imaging in Children

Background

Head motion is a challenge for neuroimaging research in developmental populations. However, it is unclear how transdiagnostic symptom domains including attention, disruptive behavior (e.g., externalizing behavior), and internalizing problems are linked to scanner motion in children, particularly across structural and functional MRI. The current study examined whether transdiagnostic domains of attention, disruptive behavior, and internalizing symptoms are associated with scanner motion in children during multimodal imaging.

Methods

In a sample of 9,045 children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control metrics and mean framewise displacement for the following: T1-weighted structural, resting-state fMRI, diffusion MRI, Stop-Signal Task, Monetary Incentive Delay task, and Emotional n-Back task. The Child Behavior Checklist was used as a continuous measure of symptom severity.

Results

Greater attention and disruptive behavior problem severity was associated with a lower likelihood of passing motion quality control across several imaging modalities. In contrast, increased internalizing severity was associated with a higher likelihood of passing motion quality control. Increased attention and disruptive behavior problem severity was also associated with increased mean motion, whereas increased internalizing problem severity was associated with decreased mean motion.

Conclusion

Transdiagnostic domains emerged as predictors of motion in youths. These findings have implications for advancing development of generalizable and robust brain-based biomarkers, computational approaches for mitigating motion effects, and enhancing accessibility of imaging protocols for children with varying symptom severities.

A structural MRI marker predicts individual differences in impulsivity and classifies patients with behavioral-variant frontotemporal dementia from matched controls

Impulsivity and higher preference for sooner over later rewards (i.e., delay discounting) are transdiagnostic markers of many psychiatric and neurodegenerative disorders. Yet, their neurobiological basis is still debated. Here, we aimed at 1) identifying a structural MRI signature of delay discounting in healthy adults, and 2) validating it in patients with behavioral variant frontotemporal dementia (bvFTD)-a neurodegenerative disease characterized by high impulsivity. We used a machine-learning algorithm to predict individual differences in delay discounting rates based on whole-brain grey matter density maps in healthy male adults (Study 1, N=117). This resulted in a cross-validated prediction-outcome correlation of r=0.35 (p=0.0028). We tested the validity of this brain signature in an independent sample of 166 healthy adults (Study 2) and its clinical relevance in 24 bvFTD patients and 18 matched controls (Study 3). In Study 2, responses of the brain signature did not correlate significantly with discounting rates, but in both Studies 1 and 2, they correlated with psychometric measures of trait urgency-a measure of impulsivity. In Study 3, brain-based predictions correlated with discounting rates, separated bvFTD patients from controls with 81% accuracy, and were associated with the severity of disinhibition among patients. Our results suggest a new structural brain pattern-the Structural Impulsivity Signature (SIS)-which predicts individual differences in impulsivity from whole-brain structure, albeit with small-to-moderate effect sizes. It provides a new brain target that can be tested in future studies to assess its diagnostic value in bvFTD and other neurodegenerative and psychiatric conditions characterized by high impulsivity.

Generalizable Links Between Borderline Personality Traits and Functional Connectivity

BACKGROUND

Symptoms of borderline personality disorder (BPD) often manifest during adolescence, but the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here, we aimed to investigate how multivariate patterns of functional connectivity are associated with borderline personality traits in large samples of young adults and adolescents.

METHODS

We used functional magnetic resonance imaging data from young adults and adolescents from the HCP-YA (Human Connectome Project Young Adult) (n = 870, ages 22-37 years, 457 female) and the HCP-D (Human Connectome Project Development) (n = 223, ages 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five-Factor Inventory. A ridge regression model with cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity.

RESULTS

Multivariate functional connectivity patterns significantly predicted out-of-sample BPD scores in unseen data in young adults (HCP-YA ppermuted = .001) and older adolescents (HCP-D ppermuted = .001). Regional predictive capacity was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD scores aligned with those associated with development in youth.

CONCLUSIONS

Individual differences in functional connectivity in developmentally sensitive regions are associated with borderline personality traits.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Molecular and micro-architectural mapping of gray matter alterations in psychosis

The psychosis spectrum encompasses a heterogeneous range of clinical conditions associated with abnormal brain development. Detecting patterns of atypical neuroanatomical maturation across psychiatric disorders requires an interpretable metric standardized by age-, sex- and site-effect. The molecular and micro-architectural attributes that account for these deviations in brain structure from typical neurodevelopment are still unknown. Here, we aggregate structural magnetic resonance imaging data from 38,696 healthy controls (HC) and 1256 psychosis-related conditions, including first-degree relatives of schizophrenia (SCZ) and schizoaffective disorder (SAD) patients (n = 160), individuals who had psychotic experiences (n = 157), patients who experienced a first episode of psychosis (FEP, n = 352), and individuals with chronic SCZ or SAD (n = 587). Using a normative modeling approach, we generated centile scores for cortical gray matter (GM) phenotypes, identifying deviations in regional volumes below the expected trajectory for all conditions, with a greater impact on the clinically diagnosed ones, FEP and chronic. Additionally, we mapped 46 neurobiological features from healthy individuals (including neurotransmitters, cell types, layer thickness, microstructure, cortical expansion, and metabolism) to these abnormal centiles using a multivariate approach. Results revealed that neurobiological features were highly co-localized with centile deviations, where metabolism (e.g., cerebral metabolic rate of oxygen (CMRGlu) and cerebral blood flow (CBF)) and neurotransmitter concentrations (e.g., serotonin (5-HT) and acetylcholine (α4β2) receptors) showed the most consistent spatial overlap with abnormal GM trajectories. Taken together these findings shed light on the vulnerability factors that may underlie atypical brain maturation during different stages of psychosis.