Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment

Transcriptional characteristics of human brain alterations in major depressive disorder: A systematic review

Many patients with major depressive disorder (MDD) experience limited treatment effectiveness due to an incomplete understanding of its neurobiological underpinnings. This review integrates neuroimaging and genetic data to examine structural and functional brain changes in MDD, alongside their genetic bases. A PRISMA-guided systematic review of imaging transcriptomics over the past decade was conducted using PubMed and Web of Science. Studies included MRI scans of both MDD patients and healthy controls, as well as brain-wide gene expression data, excluding those that were purely meta-analytical, lacked spatial correlations, or involved transdiagnostic analyses. Of the 206 studies reviewed, 20 met the inclusion criteria. Consistent patterns across studies reveal that key biological processes-such as synaptic signaling, calcium ion binding, neurodevelopment, immune regulation, and neurotransmitter transport-play a central role in brain alterations associated with MDD. Additionally, our findings suggest that electroconvulsive therapy (ECT) may alleviate symptoms by modulating these shared pathways. This review underscores the link between brain changes in MDD and specific gene expression profiles, offering insights that could inform more targeted therapeutic approaches.

Analysis of experiments with high frequency time series responses and the implications for power and sample size

Given more accessible non-invasive measuring devices, experimental response can now be observed as high-dimensional and high-frequency time series. Amidst the complex dependence structure in the data analysis, sample size determination and power analysis remain to be the key thematic focus of statistical inference. The issue is confounded with the complexity of time lag structure and phase shift usually observed in a non-uniform but normal process typically present in medical imaging data. To address these issues in case-control studies, responses can be analyzed to obtain evidence of group differences through time series clustering based on dynamic time warping. The warping of multiple time series provides a flexible distance measure robust to time point concurrence. Time series clustering partitions experimental units into groups, enabling the computation of distances to measure effect size through sum of squares of pairwise distances in warped time series.Time series clustering provides an alternative to analysis of variance when experimental responses are high-frequency time series data. Kernel regression is formulated to link sample size, effect size, power of the test, and level of significance accounting for the structure of the data generating process of the time series responses. This provides a strategy for clinicians to optimize the power of the test that can be achieved with a minimal sample size for this experimental setup. Time series clustering method is able to differentiate case and control groups in the simulated data and in the ADHD-200 fMRI dataset. The distance measured between two or more groups of time series can be used to determine sample size for a target power.

Transfer Learning of Deep Neural Networks Pretrained using the ABCD Dataset for General Psychopathology Prediction in Korean Adolescents

BACKGROUND

This study examines whether a deep neural network (DNN), trained to predict the general psychopathology factor (p-factor) using functional magnetic resonance imaging (fMRI) data from adolescents in the Adolescent Brain Cognitive Development (ABCD) study, generalizes to Korean adolescents.

METHOD

We trained a scanner-generalization neural network (SGNN) to predict p-factor scores from resting-state functional connectivity (RSFC) data of 6,905 ABCD adolescents, controlling for MRI scanner-related confounds. We then transferred the pretrained SGNN to a DNN to predict p-factor scores for 125 adolescents, including healthy individuals and those with major depressive disorder, using data from Seoul National University Hospital (SNUH). We compared the transferred DNN's performance with that of kernel ridge regression (KRR) and a baseline DNN.

RESULTS

The transferred DNN outperformed KRR (0.17 ± 0.16; 0.60 ± 0.07) and the baseline DNN (0.17 ± 0.16; 0.69 ± 0.11), achieving a higher Pearson's correlation coefficient (0.29 ± 0.18) and lower mean absolute error (0.59 ± 0.09; p < 0.005). We identified the default mode network (DMN) and visual network (VIS) as crucial functional networks (FNs) for predicting p-factors across both datasets. The dorsal attention network was specific to ABCD, while the cingulo-opercular and ventral attention networks were specific to SNUH.

CONCLUSION

The transferred SGNN successfully generalized to Korean adolescents. Altered RSFC in the DMN and VIS may serve as promising biomarkers for p-factor prediction across diverse populations, addressing heterogeneity in demographics, diagnoses, and MRI scanner characteristics.

Deep linear matrix approximate reconstruction with integrated BOLD signal denoising reveals reproducible hierarchical brain connectivity networks from multiband multi-echo fMRI

The hierarchical modular functional structure in the human brain has not been adequately depicted by conventional functional magnetic resonance imaging (fMRI) acquisition techniques and traditional functional connectivity reconstruction methods. Fortunately, rapid advancements in fMRI scanning techniques and deep learning methods open a novel frontier to map the spatial hierarchy within Brain Connectivity Networks (BCNs). The novel multiband multi-echo (MBME) fMRI technique has increased spatiotemporal resolution and peak functional sensitivity, while the advanced deep linear model (multilayer-stacked) named DEep Linear Matrix Approximate Reconstruction (DELMAR) enables the identification of hierarchical features without extensive hyperparameter tuning. We incorporate a multi-echo blood oxygenation level-dependent (BOLD) signal and DELMAR for denoising in its first layer, thereby eliminating the need for a separate multi-echo independent component analysis (ME-ICA) denoising step. Our results demonstrate that the DELMAR/Denoising/Mapping strategy produces more accurate and reproducible hierarchical BCNs than traditional ME-ICA denoising followed by DELMAR. Additionally, we showcase that MBME fMRI outperforms multiband (MB) fMRI in terms of hierarchical BCN mapping accuracy and precision. These reproducible spatial hierarchies in BCNs have significant potential for developing improved fMRI diagnostic and prognostic biomarkers of functional connectivity across a wide range of neurological and psychiatric disorders.

Biased Information Routing Through the Basolateral Amygdala, Altered Valence Processing, and Impaired Affective States Associated With Psychiatric Illnesses

Accumulating evidence supports a role for altered circuit function in impaired valence processing and altered affective states as a core feature of psychiatric illnesses. We review the circuit mechanisms underlying normal valence processing and highlight evidence supporting altered function of the basolateral amygdala, valence processing, and affective states across psychiatric illnesses. The mechanisms controlling network activity that governs valence processing are reviewed in the context of potential pathophysiological mechanisms mediating circuit dysfunction and impaired valence processing in psychiatric illnesses. Finally, we review emerging data demonstrating experience-dependent, biased information routing through the basolateral amygdala promoting negative valence processing and discuss the potential relevance to impaired affective states and psychiatric illnesses.

Major depressive disorder on a neuromorphic continuum

The heterogeneity of major depressive disorder (MDD) has hindered clinical translation and neuromarker identification. Biotyping facilitates solving the problems of heterogeneity, by dissecting MDD patients into discrete subgroups. However, interindividual variations suggest that depression may be conceptualized as a "continuum," rather than as a "category." We use a Bayesian model to decompose structural MRI features of MDD patients from a multisite cross-sectional cohort into three latent disease factors (spatial pattern) and continuum factor compositions (individual expression). The disease factors are associated with distinct neurotransmitter receptors/transporters obtained from open PET sources. Increases cortical thickness in sensory and decreases in orbitofrontal cortices (Factor 1) associate with norepinephrine and 5-HT2A density, decreases in the cingulo-opercular network and subcortex (Factor 2) associate with norepinephrine and 5-HTT density, and increases in social and affective brain systems (Factor 3) relate to 5-HTT density. Disease factor patterns can also be used to predict depressive symptom improvement in patients from the longitudinal cohort. Moreover, individual factor expressions in MDD are stable over time in a longitudinal cohort, with differentially expressed disease controls from a transdiagnostic cohort. Collectively, our data-driven disease factors reveal that patients with MDD organize along continuous dimensions that affect distinct sets of regions.

Frontostriatal Networks Undergo Functional Specialization During Adolescence that Follows a Ventral-Dorsal Gradient: Developmental Trajectories and Longitudinal Associations

Seminal studies in animal neuroscience demonstrate that frontostriatal circuits exhibit a ventral-dorsal functional gradient to integrate neural functions related to reward processing and cognitive control. Prominent neurodevelopmental models posit that heightened reward-seeking and risk-taking during adolescence result from maturational imbalances between frontostriatal neural systems underlying reward processing and cognitive control. The present study investigated whether the development of ventral (VS) and dorsal (DS) striatal resting-state connectivity (rsFC) networks along this proposed functional gradient relates to putative imbalances between reward and executive systems posited by a dual neural systems theory of adolescent development. 163 participants aged 11-25 years (54% female, 90% white) underwent resting scans at baseline and biennially thereafter, yielding 339 scans across four assessment waves. We observed developmental increases in VS rsFC with brain areas implicated in reward processing (e.g., subgenual cingulate gyrus and medial orbitofrontal cortex) and concurrent decreases with areas implicated in executive function (e.g., ventrolateral and dorsolateral prefrontal cortices). DS rsFC exhibited the opposite pattern. More rapid developmental increases in VS rsFC with reward areas were associated with developmental improvements in reward-based decision making, whereas increases in DS rsFC with executive function areas were associated with improved executive function, though each network exhibited some crossover in function. Collectively, these findings suggest that typical adolescent neurodevelopment is characterized by a divergence in ventral and dorsal frontostriatal connectivity that may relate to developmental improvements in affective decision-making and executive function.Significance Statement Anatomical studies in nonhuman primates demonstrate that frontostriatal circuits are essential for integration of neural functions underlying reward processing and cognition, with human neuroimaging studies linking alterations in these circuits to psychopathology. The present study characterized the developmental trajectories of frontostriatal resting state networks from childhood to young adulthood. We demonstrate that ventral and dorsal aspects of the striatum exhibit distinct age-related changes that predicted developmental improvements in reward-related decision making and executive function. These results highlight that adolescence is characterized by distinct changes in frontostriatal networks that may relate to normative increases in risk-taking. Atypical developmental trajectories of frontostriatal networks may contribute to adolescent-onset psychopathology.

Functional Hierarchy of the Human Neocortex from Cradle to Grave

Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial insight into the relationship between the brain's topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their development throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing thereafter. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes.

Multimodal neural correlates of childhood psychopathology

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples and robust to variations in analytical parameters. Although model parameters yielded statistically significant brain-behavior associations in unseen data, generalizability of the model was rather limited for all three latent components (r change from within- to out-of-sample statistics: LC1within = 0.36, LC1out = 0.03; LC2within = 0.34, LC2out = 0.05; LC3within = 0.35, LC3out = 0.07). Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

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

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

Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses

Recent advances in cognitive neuroscience have focused on using resting-state functional connectivity (RSFC) data from fMRI scans to more accurately predict human traits and behaviors. Traditional approaches generally analyze RSFC by correlating averaged time-series data across regions of interest (ROIs) or networks, which may overlook important spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project and the Brain Genomics Superstruct Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. To enhance prediction accuracy, we developed an ensemble learner that combines these qualitatively different methods using a weighted average approach. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model, integrating these diverse weak learners, outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

Negative Urgency and Lack of Perseverance Predict Suicidal Ideation and Attempts Among Young Adolescents

OBJECTIVE

Impulsivity has been recognized as an important factor in suicidal thoughts and behaviors (STBs). However, previous research linking impulsivity to STBs has largely relied on cross-sectional designs, considered only a subset of impulsivity measures, and typically focused on middle-to-older adolescents. Here, we explored multiple measures of impulsivity and assessed their predictive relation to suicidal ideation and suicide attempts.

METHOD

In a sample of 10,286 adolescents (ages 9-12; 47.4% female, 76.4% White, 19.4% Black, 6.4% Asian, 3.5% American Indian/Alaskan Native, 0.6% Native Hawaiian/Pacific Islander, 6.4% Other, 19.4% Hispanic, 12.1% Mixed/Multiple Race) from the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), we assessed impulsivity when youth were 9-10 years old, and suicidal ideation and attempts when youth were 11-12 years old. We measured impulsivity in three ways: a trait-like measure (UPPS-P Impulsive Behavior Scale), a behavioral measure (delay discounting task), and a neural measure (Cortical Delay Discounting [C-DD]). Suicidal ideation and attempts were assessed using the Kiddie Schedule for Affective Disorder and Schizophrenia (KSADS) suicide module.

RESULTS

Negative urgency and lack of perseverance (at ages 9-10) significantly predicted a higher likelihood of suicidal ideation (Negative Urgency: Odds Ratio [OR] = 1.254, p < .001; Lack of Perseverance: OR = 1.152, p = .035) and suicide attempts (Negative Urgency: OR = 1.328, p = .009; Lack of Perseverance: OR = 1.270, p = .009) when youth were 11-12 years old.

CONCLUSIONS

Negative urgency and lack of perseverance are robust predictors of future suicidal ideation and suicide attempts in young adolescents. These findings highlight the importance of assessing for and targeting these dimensions of impulsivity in clinical settings.

Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction

Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.

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 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.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders

BACKGROUND

Autism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs).

METHODS

Data came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16-35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL's PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted.

RESULTS

ImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance.

LIMITATIONS

Despite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities.

CONCLUSIONS

The lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

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

Importance

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

Objective

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

Design Setting and Participants

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

Main Outcomes and Measures

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

Results

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

Conclusions and Relevance

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

KEY POINTS

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

MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

A measure of reliability convergence to select and optimize cognitive tasks for individual differences research

Surging interest in individual differences has faced setbacks in light of recent replication crises in psychology, for example in brain-wide association studies exploring brain-behavior correlations. A crucial component of replicability for individual differences studies, which is often assumed but not directly tested, is the reliability of the measures we use. Here, we evaluate the reliability of different cognitive tasks on a dataset with over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. We further show the effect on reliability of measuring over multiple time points, with tasks assessing different cognitive domains being differentially affected. Data collected over more than one session may be required to achieve trait-like stability.

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and the underlying patterns of neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the amygdala of two male macaques. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5 Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-modal approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.

A synergetic turn in cognitive neuroscience of brain diseases

Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method

The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Understanding divergence: Placing developmental neuroscience in its dynamic context

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

Clinical and epidemiological profile of patients with mental disorders in a specialized outpatient clinic and its role in the psychosocial care network

Introduction

Mental health disorders (MHDs) are responsible for much impairment of quality of life in Brazil and worldwide. Early diagnosis and effective treatment strategies are required due to the heterogeneous symptoms and multifactorial etiology.

Methods

A descriptive retrospective observational study was performed aiming to characterize the clinical and psychiatric profiles of patients with MHD attending a Brazilian public tertiary psychiatric outpatient clinic, which is a reference health service for more than 2 million inhabitants. Predominant clinical and sociodemographic aspects of patients were evaluated between March 2019 and March 2021.

Results

A total of 8,384 appointments were analyzed. The majority of patients were female, and the mean age was 45 years old. Generalized anxiety disorder (GAD) was the most common MHD. The prevailing symptoms were sadness, anxiety, and irritability, with the most prescribed medications being selective serotonin reuptake inhibitors.

Conclusion

The epidemiological characterization of mental disorders in specialized mental health outpatient clinics provides evidence for the establishment of more specific protocols and advocates a dimensional transdiagnostic approach as an aid to public mental health services.

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow

Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from  structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.

Frontostriatal circuitry and the tryptophan kynurenine pathway in major psychiatric disorders

RATIONALE

An imbalance of the tryptophan kynurenine pathway (KP) commonly occurs in psychiatric disorders, though the neurocognitive and network-level effects of this aberration are unclear.

OBJECTIVES

In this study, we examined the connection between dysfunction in the frontostriatal brain circuits, imbalances in the tryptophan kynurenine pathway (KP), and neurocognition in major psychiatric disorders.

METHODS

Forty first-episode medication-naive patients with schizophrenia (SCZ), fifty patients with bipolar disorder (BD), fifty patients with major depressive disorder (MDD), and forty-two healthy controls underwent resting-state functional magnetic resonance imaging. Plasma levels of KP metabolites were measured, and neurocognitive function was evaluated. Frontostriatal connectivity and KP metabolites were compared between groups while controlling for demographic and clinical characteristics. Canonical correlation analyses were conducted to explore multidimensional relationships between frontostriatal circuits-KP and KP-cognitive features.

RESULTS

Patient groups shared hypoconnectivity between bilateral ventrolateral prefrontal cortex (vlPFC) and left insula, with disorder-specific dysconnectivity in SCZ related to PFC, left dorsal striatum hypoconnectivity. The BD group had higher anthranilic acid and lower xanthurenic acid levels than the other groups. KP metabolites and ratios related to disrupted frontostriatal dysconnectivity in a transdiagnostic manner. The SCZ group and MDD group separately had high-dimensional associations between KP metabolites and cognitive measures.

CONCLUSIONS

The findings suggest that KP may influence cognitive performance across psychiatric conditions via frontostriatal dysfunction.

Quantifying the contribution of subject and group factors in brain activation

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

International study of the Complex Stress Reaction Syndrome: Implications for transdiagnostic clinical practice

BACKGROUND

The debate regarding diagnostic classification systems in psychiatry (categorial vs dimensional systems) has essential implications for the diagnosis, prevention and treatment of stress reactions. We previously found a unique pattern of stress reaction in a study executed during the coronavirus disease 2019 pandemic using large representative samples in two countries, and termed it the Complex Stress Reaction Syndrome (CSRS).

AIM

To investigate CSRS, Type A (psychiatric symptoms, spanning anxiety, depression, stress symptoms, and post-traumatic stress disorder (PTSD)), with or without long-coronavirus disease (COVID) residuals (CSRS, Type B, neuropsychiatric symptoms spanning cognitive deficits and fatigue, excluding systemic symptoms). Our two-tailed hypothesis was that CSRS is a condition related to an unrecognized type of stress reaction in daily life in the general population (Type A) or that it is related to the severe acute respiratory syndrome coronavirus 2 infection and its long-COVID residuals (Type B).

METHODS

977 individuals in four continents (North America, Europe, Australia and the Middle East) completed the online study questionnaire in six languages using the Qualtrics platform. The study was managed by six teams in six countries that promoted the study on social media. The questionnaire assessed anxiety, depression, stress symptoms and PTSD (CSRS, Type A), cognitive deficits and fatigue (CSRS, Type B). The data were analyzed using Proportion Analyses, Multivariate Analysis of Co-Variance (MANCOVA), linear regression analyses and validated clinical cutoff points.

RESULTS

The results of the Proportion Analyses showed that the prevalence of 4 symptoms spanning anxiety, depression, stress symptoms, and PTSD was significantly higher than the most prevalent combinations of fewer symptoms across 4 continents, age groups, and gender. This supports the transdiagnostic argument embedded in the CSRS (Type A). The same pattern of results was found in infected/recovered individuals. The prevalence of the 4 psychiatric symptoms combination was significantly greater than that of 5 and 6 symptoms, when adding cognitive deficits and fatigue, respectively. MANCOVA showed a significant three-way interaction (age × gender × continent). Further analyses showed that the sources of this three-way interaction were threefold relating to two sub-populations at-risk: (1) Individuals that self-identified as non-binary gender scored significantly higher on all 4 psychiatric symptoms of the CSRS, Type A at young age groups (< 50 years old) in North America compared to (self-identified) women and men located in the 4 continents studied, and to other ages across the adult life span; and (2) This pattern of results (CSRS, Type A) was found also in women at young ages (< 40 years old) in North America who scored higher compared to men and women in other continents and other ages. Linear regression analyses confirmed the MANCOVA results.

CONCLUSION

These results show a combined mental health risk factor related to stress reactivity, suggesting that the CSRS is sensitive to populations at risk and may be applied to future identification of other vulnerable sub-populations. It also supports the transdiagnostic approach for more accurate prevention and treatment. Time will tell if such transdiagnostic syndromes will be part of the discussions on the next revisions of the traditional classification systems or whether the crisis in psychiatry further evolves.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the macaque amygdala and activated them with a highly selective and potent DREADD agonist, deschloroclozapine. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Interestingly, activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-disciplinary approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.

Not one thing at a time: When concomitant multiple stressors produce a transdiagnostic clinical picture

A condition of exposure to multiple stressors resulting in a mixed clinical picture spanning conventional categories without meeting any of them in full, encompasses a risk for a list of comorbidities preventing appropriate prevention and treatment. New transformative transdiagnostic approaches suggest changes spanning conventional categories. They base their systems of classification on biomarkers as well as on brain structural and functional dysregulation as associated with behavioral and emotional symptoms. These new approaches received critiques for not being specific enough and for suggesting a few biomarkers for psychopathology as a whole. Therefore, they put the value of differential diagnosis at risk of avoiding appropriate derived prevention and treatment. Multiplicity of stressors has been considered mostly during and following catastrophes, without considering the resulting mixed clinical picture and life event concomitant stressors. We herewith suggest a new category within the conventional classification systems: The Complex Stress Reaction Syndrome, for a condition of multiplicity of stressors, which showed a mixed clinical picture for daily life in the post coronavirus disease 2019 era, in the general population. We argue that this condition may be relevant to daily, regular life, across the lifespan, and beyond conditions of catastrophes. We further argue that this condition may worsen without professional care and it may develop into a severe mental health disorder, more costly to health systems and the suffering individuals. Means for derived prevention and treatment are discussed.

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

Functional magnetic resonance imaging (fMRI) with optogenetic neural manipulation is a powerful tool that enables brain-wide mapping of effective functional networks. To achieve flexible manipulation of neural excitation throughout the mouse cortex, we incorporated spatiotemporal programmable optogenetic stimuli generated by a digital micromirror device into an MRI scanner via an optical fiber bundle. This approach offered versatility in space and time in planning the photostimulation pattern, combined with in situ optical imaging and cell-type-specific or circuit-specific genetic targeting in individual mice. Brain-wide effective connectivity obtained by fMRI with optogenetic stimulation of atlas-based cortical regions is generally congruent with anatomically defined axonal tracing data but is affected by the types of anesthetics that act selectively on specific connections. fMRI combined with flexible optogenetics opens a new path to investigate dynamic changes in functional brain states in the same animal through high-throughput brain-wide effective connectivity mapping.

Opposing and emotion-specific associations between frontal activation with depression and anxiety symptoms during facial emotion processing in generalized anxiety and depression

Major depression (MDD) and generalized anxiety disorder (GAD) have become one of the leading global causes of disability and both are characterized by marked interpersonal and social impairments. However, despite high comorbidity and overlapping social-emotional deficits, it remains unclear whether MDD and GAD share a common neural basis during interpersonal processing. In the present study, we combined an emotional face processing paradigm with fMRI and dimensional and categorical analyses in a sample of unmedicated MDD and GAD patients (N = 72) as well as healthy controls (N = 35). No group differences were found in categorical analyses. However, the dimensional analyses revealed that dorsolateral prefrontal cortex (dlPFC) reactivity to sad facial expressions was positively associated with depression symptom load, yet negatively associated with anxiety symptom load in the entire sample. On the network level depression symptom load was positively associated with functional connectivity between the bilateral amygdala and a widespread network including the anterior cingulate and insular cortex. Together, these findings suggest that the dlPFC - engaged in cognitive and emotional processing - exhibits symptom- and emotion-specific alteration during interpersonal processing. Dysregulated communication between the amygdala and core regions of the salience network may represent depression-specific neural dysregulations.

Corpus callosum structural characteristics in very preterm children and adolescents: Developmental trajectory and relationship to cognitive functioning

Previous studies suggest that structural alteration of the corpus callosum, i.e., the largest white matter commissural pathway, occurs after a preterm birth in the neonatal period and lasts across development. The present study aims to unravel corpus callosum structural characteristics across childhood and adolescence in very preterm (VPT) individuals, and their associations with general intellectual, executive and socio-emotional functioning. Neuropsychological assessments, T1-weighted and multi-shell diffusion MRI were collected in 79 VPT and 46 full term controls aged 6-14 years. Volumetric, diffusion tensor and neurite orientation dispersion and density imaging (NODDI) measures were extracted on 7 callosal portions using TractSeg. A multivariate data-driven approach (partial least squares correlation) and a cohort-based age normative modelling approach were used to explore associations between callosal characteristics and neuropsychological outcomes. The VPT and a full-term control groups showed similar trends of white-matter maturation over time, i.e., increase FA and reduced ODI, in all callosal segments, that was associated with increase in general intellectual functioning. However, using a cohort-based age-related normative modelling, findings show atypical pattern of callosal development in the VPT group, with reduced callosal maturation over time that was associated with poorer general intellectual and working memory functioning, as well as with lower gestational age.

Environmental effects on brain functional networks in a juvenile twin population

The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.

Uncovering individual differences in fine-scale dynamics of functional connectivity

Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.

Task-Based and Resting-State Functional MRI in Observing Eloquent Cerebral Areas Personalized for Epilepsy and Surgical Oncology Patients: A Review of the Current Evidence

Functional magnetic resonance imaging (fMRI) is among the newest techniques of advanced neuroimaging that offer the opportunity for neuroradiologists, neurophysiologists, neuro-oncologists, and neurosurgeons to pre-operatively plan and manage different types of brain lesions. Furthermore, it plays a fundamental role in the personalized evaluation of patients with brain tumors or patients with an epileptic focus for preoperative planning. While the implementation of task-based fMRI has increased in recent years, the existing resources and evidence related to this technique are limited. We have, therefore, conducted a comprehensive review of the available resources to compile a detailed resource for physicians who specialize in managing patients with brain tumors and seizure disorders. This review contributes to the existing literature because it highlights the lack of studies on fMRI and its precise role and applicability in observing eloquent cerebral areas in surgical oncology and epilepsy patients, which we believe is underreported. Taking these considerations into account would help to better understand the role of this advanced neuroimaging technique and, ultimately, improve patient life expectancy and quality of life.

General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network

The general psychopathology factor (p-factor) represents shared variance across mental disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive Development (ABCD) Study offers an unprecedented opportunity to investigate functional networks (FNs) from functional magnetic resonance imaging (fMRI) associated with the psychopathology of an adolescent cohort (n > 10,000). However, the heterogeneities associated with the use of multiple sites and multiple scanners in the ABCD Study need to be overcome to improve the prediction of the p-factor using fMRI. We proposed a scanner-generalization neural network (SGNN) to predict the individual p-factor by systematically reducing the scanner effect for resting-state functional connectivity (RSFC). We included 6905 adolescents from 18 sites whose fMRI data were collected using either Siemens or GE scanners. The p-factor was estimated based on the Child Behavior Checklist (CBCL) scores available in the ABCD study using exploratory factor analysis. We evaluated the Pearson's correlation coefficients (CCs) for p-factor prediction via leave-one/two-site-out cross-validation (LOSOCV/LTSOCV) and identified important FNs from the weight features (WFs) of the SGNN. The CCs were higher for the SGNN than for alternative models when using both LOSOCV (0.1631 ± 0.0673 for the SGNN vs. 0.1497 ± 0.0710 for kernel ridge regression [KRR]; p < 0.05 from a two-tailed paired t-test) and LTSOCV (0.1469 ± 0.0381 for the SGNN vs. 0.1394 ± 0.0359 for KRR; p = 0.01). It was found that (a) the default-mode and dorsal attention FNs were important for p-factor prediction, and (b) the intra-visual FN was important for scanner generalization. We demonstrated the efficacy of our novel SGNN model for p-factor prediction while simultaneously eliminating scanner-related confounding effects for RSFC.

Multi-class classification model for psychiatric disorder discrimination

BACKGROUND

Physicians follow-up a symptom-based approach in the diagnosis of psychiatric diseases. According to this approach, a process based on internationally valid diagnostic tools such as The Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD), patient reports and the observation and experience of the physician is monitored. As in other fields of medicine, the search for biomarkers that can be used in processes related to diseases continues in psychiatry and various researches are carried out in this field.

OBJECTIVES

Within the scope of this study, a dataset containing electroencephalogram (EEG) measurements of individuals diagnosed with different psychiatric diseases were analyzed by machine learning methods and the diseases were differentiated/classified with the models obtained. Thus, it was investigated whether EEG data could be a biomarker for psychiatric diseases.

MATERIALS AND METHODS

In the dataset analyzed within the scope of the study, for 550 patients (81 bipolar disorder, 95 attention deficit and hyperactivity disorder - ADHD, 67 depression, 34 obsessive compulsive disorder - OCD, 75 opioid, 146 posttraumatic stress disorder - PTSD, 52 schizophrenia) and 84 healthy individuals, there are 634 samples (rows), 77 variables (columns) in total. 76 of the variables consist of absolute power values belonging to 4 frequency bands (alpha, beta, delta, theta) collected from 19 different electrodes. 80 % of the dataset was used for training the models and 20 % of the data was used for testing the performance of the models. The 5-fold cross validation (CV) method, which repeats 3 times in the training dataset, was used and with this method, the hyperparameters used in the models were also optimized. Different models have been established with the selected hyperparameters and the performance of these models has been tested with the test dataset. C5.0, random forest (RF), support vector machine (SVM) and artificial neural networks (ANN) were used to build the models.

RESULTS

Within the scope of the study, the absolute power values obtained from EEG measurements performed using 19 electrodes were analyzed by machine learning methods. It was concluded that classification between disease groups was feasible with a high accuracy (C5.0-0.841, SVM_radial - 0.841, RF - 0.762). It was observed that ADHD, depression and schizophrenia diseases can be differentiated better (F-score = 1, balanced accuracy = 1) once the results were evaluated on a class category basis according to the F- measure and balanced accuracy values.

DISCUSSION AND CONCLUSION

Through the medium of the analyzes made within the scope of this study, it was investigated whether EEG data could be used as a biomarker for the detection and diagnosis of psychiatric diseases. The findings obtained from this study revealed that by using EEG data as a biomarker, it can be highly predicted whether a person has a psychiatric disease or not. Once evaluated with broad strokes, it is feasible to assert that it is possible to analyze whether the person who consults a physician with a complaint is ranked among the psychiatric disease class with EEG measurement. When trying to differentiate between numerous and diverse disease categories, it may be claimed that some diseases (ADHD, depression, schizophrenia) can be distinguished better by coming to the fore on a model basis. Considering the findings, it is anticipated that the analyzes obtained as a result of this study will contribute to the studies to be conducted using machine learning in the field of psychiatry.

Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study

Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.

Meta-analysis of structural evidence for the Hierarchical Taxonomy of Psychopathology (HiTOP) model

BACKGROUND

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a classification system that seeks to organize psychopathology using quantitative evidence - yet the current model was established by narrative review. This meta-analysis provides a quantitative synthesis of literature on transdiagnostic dimensions of psychopathology to evaluate the validity of the HiTOP framework.

METHODS

Published studies estimating factor-analytic models from diagnostic and statistical manual of mental disorders (DSM) diagnoses were screened. A total of 120,596 participants from 35 studies assessing 23 DSM diagnoses were included in the meta-analytic models. Data were pooled into a meta-analytic correlation matrix using a random effects model. Exploratory factor analyses were conducted using the pooled correlation matrix. A hierarchical structure was estimated by extracting one to five factors representing levels of the HiTOP framework, then calculating congruence coefficients between factors at sequential levels.

RESULTS

Five transdiagnostic dimensions fit the DSM diagnoses well (comparative fit index = 0.92, root mean square error of approximation = 0.07, and standardized root-mean-square residual = 0.03). Most diagnoses had factor loadings >|0.30| on the expected factors, and congruence coefficients between factors indicated a hierarchical structure consistent with the HiTOP framework.

CONCLUSIONS

A model closely resembling the HiTOP framework fit the data well and placement of DSM diagnoses within transdiagnostic dimensions were largely confirmed, supporting it as valid structure for conceptualizing and organizing psychopathology. Results also suggest transdiagnostic research should (1) use traits, narrow symptoms, and dimensional measures of psychopathology instead of DSM diagnoses, (2) assess a broader array of constructs, and (3) increase focus on understudied pathologies.

Pre- and post-task resting-state differs in clinical populations

Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Amygdala-prefrontal connectivity in children with maladaptive aggression is modulated by social impairment

Aggressive behavior is common across childhood-onset psychiatric disorders and is associated with impairments in social cognition and communication. The present study examined whether amygdala connectivity and reactivity during face emotion processing in children with maladaptive aggression are moderated by social impairment. This cross-sectional study included a well-characterized transdiagnostic sample of 101 children of age 8-16 years old with clinically significant levels of aggressive behavior and 32 typically developing children without aggressive behavior. Children completed a face emotion perception task of fearful and calm faces during functional magnetic resonance imaging. Aggressive behavior and social functioning were measured by standardized parent ratings. Relative to controls, children with aggressive behavior showed reduced connectivity between the amygdala and the dorsolateral prefrontal cortex (PFC) during implicit emotion processing. In children with aggressive behavior, the association between reduced amygdala-ventrolateral PFC connectivity and greater severity of aggression was moderated by greater social impairment. Amygdala reactivity to fearful faces was also associated with severity of aggressive behavior for children without social deficits but not for children with social deficits. Social impairments entail difficulties in interpreting social cues and enacting socially appropriate responses to frustration or provocation, which increase the propensity for an aggressive response via diminished connectivity between the amygdala and the ventral PFC.

Distinct Neural Profiles of Frontoparietal Networks in Boys with ADHD and Boys with Persistent Depressive Disorder

Working memory deficits are common in attention-deficit/hyperactivity disorder (ADHD) and depression-two common neurodevelopmental disorders with overlapping cognitive profiles but distinct clinical presentation. Multivariate techniques have previously been utilized to understand working memory processes in functional brain networks in healthy adults but have not yet been applied to investigate how working memory processes within the same networks differ within typical and atypical developing populations. We used multivariate pattern analysis (MVPA) to identify whether brain networks discriminated between spatial versus verbal working memory processes in ADHD and Persistent Depressive Disorder (PDD). Thirty-six male clinical participants and 19 typically developing (TD) boys participated in a fMRI scan while completing a verbal and a spatial working memory task. Within a priori functional brain networks (frontoparietal, default mode, salience), the TD group demonstrated differential response patterns to verbal and spatial working memory. The PDD group showed weaker differentiation than TD, with lower classification accuracies observed in primarily the left frontoparietal network. The neural profiles of the ADHD and PDD differed specifically in the SN where the ADHD group's neural profile suggests significantly less specificity in neural representations of spatial and verbal working memory. We highlight within-group classification as an innovative tool for understanding the neural mechanisms of how cognitive processes may deviate in clinical disorders, an important intermediary step towards improving translational psychiatry.

Boosting psychological change: Combining non-invasive brain stimulation with psychotherapy

Mental health disorders and substance use disorders are a leading cause of morbidity and mortality worldwide, and one of the most important challenges for public health systems. While evidence-based psychotherapy is generally pursued to address mental health challenges, psychological change is often hampered by non-adherence to treatments, relapses, and practical barriers (e.g., time, cost). In recent decades, Non-invasive brain stimulation (NIBS) techniques have emerged as promising tools to directly target dysfunctional neural circuitry and promote long-lasting plastic changes. While the therapeutic efficacy of NIBS protocols for mental illnesses has been established, neuromodulatory interventions might also be employed to support the processes activated by psychotherapy. Indeed, combining psychotherapy with NIBS might help tailor the treatment to the patient's unique characteristics and therapeutic goal, and would allow more direct control of the neuronal changes induced by therapy. Herein, we overview emerging evidence on the use of NIBS to enhance the psychotherapeutic effect, while highlighting the next steps in advancing clinical and research methods toward personalized intervention approaches.