Lesion network mapping (LNM) is a neuroimaging framework that uses normative functional connectivity (FC) data to link heterogeneous brain lesions and functional alterations to brain networks implicated in neurological and psychiatric conditions. However, many of the networks identified by LNM and related methods appear to be highly similar across diverse conditions such as addiction, depression, psychosis and epilepsy. To understand this similarity, we re-examined the data from multiple LNM studies and assessed the methodological roots of the method. Our findings reveal a foundational limitation: at its core, LNM involves a repetitive sampling of one and the same FC matrix. As a result, it systematically maps sets of local brain changes-whether they are patient lesions, magnetic resonance imaging-derived alterations, synthetic or random-onto the same nonspecific properties of the used FC data, producing highly similar networks across conditions. This central limitation cautions the use of LNM as a method for studying distinct biological networks underlying brain disorders. Our work may aid the development of a new generation of network-mapping methods from first principles.

Current psychiatric neuroimaging supports the view that major depressive disorder (MDD) is a dysconnection syndrome, characterized by structural brain dysconnectivity. Recent studies investigating this question, however, did not evaluate the involvement of comorbid disorders, of which anxiety disorders (ANX) are particularly prevalent. Here, we investigated the structural connectivity alterations observed in MDD with and without comorbid ANX. To this end, we reconstructed structural brain networks of n = 781 individuals with a diagnosis of MDD who had at least one diagnosis of an ANX (n = 249) and those without any diagnosis of ANX (n = 532), as well as n = 906 healthy controls (HC) from structural and diffusion-weighted MRI. The network-based statistic (NBS) toolbox was employed to evaluate network-level differences in structural connectivity among the three groups. Transdiagnostic analyses were conducted to explore the dimensional relationship between anxiety and structural connectivity. NBS revealed decreased structural connectivity in MDD patients without comorbid ANX and increased structural connectivity in MDD patients with comorbid ANX relative to HC, with both effects found in spatially overlapping white matter connections. Transdiagnostic analyses suggested that increases in anxiety were associated with increased structural connectivity across all groups. Our finding that hyperconnectivity rather than hypoconnectivity characterizes the structural connectome of MDD patients with comorbid ANX challenges the applicability of the dysconnection syndrome hypothesis to MDD with comorbid ANX, warranting symptom-based investigations of brain changes in mental disorders.

Objective:Genetic factors play a substantial role in the etiology of autism and its co-occurrence with other conditions and traits. The primary objective of this study was to clarify the associations between the autism polygenic score and autism diagnosis, autistic traits, and related behavioral and neurobiological traits. Method:Peer-reviewed studies written in English reporting univariate associations were included. PubMed, Web of Science, PsycINFO, and Scopus were systematically searched on November 2, 2022, and January 6, 2023. The quality of included studies was assessed using the QUIPS tool, systematic review with best-evidence synthesis was applied, and meta-analyses were performed if >5 studies were conducted on similar phenotypes. Results:Of 72 eligible studies (pooled N = 720,087), 61 received high-quality ratings. Meta-analysis of 9 studies revealed strong evidence for an association between the autism polygenic score and autism diagnosis (meta-analytic r = 0.158 [95% CI 0.067-0.249]). The systematic review revealed strong evidence for an association with social behavior, depression, and motor skills and weak evidence for physical activity. Associations with other outcomes were inconclusive, and effect sizes were generally small (median r = 0.03). Conclusion:The autism polygenic score is consistently associated with autism diagnosis and a small number of co-occurring traits. Associations with many other traits and conditions are not significant. Due to its inconsistent associations and limited generalizability, it must be emphasized that the autism polygenic score does not have clinical utility and should be applied only for scientific purposes, with improvements needed for a deeper understanding of the polygenic underpinnings of autism. Diversity & Inclusion Statement:One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. Study registration information:The Association Between Polygenic Scores for Autism Spectrum Disorder and Autism Spectrum Disorder and Associated Traits: A Systematic Review and Meta-analysis; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022307993.

Background Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores. Methods The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort. Results PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [p(FDR)] = [1.8 x 10(-16), 4.5 x 10(-2)]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (p(FDR) < 9.7 x 10(-5)), lower cognitive performance (p(FDR) < 5.3 x 10(-5)), and lower general well-being (p(FDR) < 9.7 x 10(-4)). Conclusions Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

Human evolution involved major anatomical transformations, including a rapid increase in brain volume over the last 2 million years. Examination of fossil records provides insight into these physical changes but offers limited information on the evolution of brain function and cognition. A complementary approach integrates genome dating from the Human Genome Dating Project with genome-wide association studies to trace the emergence of genetic variants linked to human traits over 5 million years. We find that genetic variants underlying cortical morphology (~300,000 years, P = 4 × 10-28), fluid intelligence (~500,000 years, P = 1.4 × 10-4), and psychiatric disorders (~475,000 years, P = 5.9 × 10-33) emerged relatively recently in hominin evolution. Among psychiatric phenotypes, variants associated with depression (~24,000 years, P = 1.6 × 10-4) and alcoholism-related traits (~40,000 years, P = 5.2 × 10-12) are the youngest. Genes with recent evolutionary modifications are involved in intelligence (P = 1.7 × 10-6) and cortical area (P = 3.5 × 10-4) and exhibit elevated expression in language-related areas (P = 7.1 × 10-4), a hallmark of human cognition. Our findings suggest that recently evolved genetic variants shaped the human brain, cognition, and psychiatric traits.

BACKGROUND: Psychiatric conditions show overlap in their symptoms, genetics, and involvement in brain areas and circuits. Structural alterations in the brain have been found to run in parallel with expression profiles of risk genes at the level of the brain transcriptome, which may point toward a potential transdiagnostic vulnerability of the brain to disease processes.METHODS: We characterized the transcriptomic vulnerability of the cortex across 4 major psychiatric disorders based on collated data from patients with psychiatric disorders (n = 390) and matched control participants (n = 293). We compared normative expression profiles of risk genes linked to schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder to examine cross-disorder overlap in spatial expression profiles across the cortex and their concordance with a magnetic resonance imaging-derived cross-disorder profile of structural brain alterations.RESULTS: We showed high expression of psychiatric risk genes converging on multimodal cortical regions of the limbic, ventral attention, and default mode networks versus primary somatosensory networks. Risk genes were found to be enriched among genes associated with the magnetic resonance imaging cross-disorder profile, suggestive of a common link between brain anatomy and the transcriptome in psychiatric conditions. Characterization of this crossdisorder structural alteration map further shows enrichment for gene markers of astrocytes, microglia, and supragranular cortical layers.CONCLUSIONS: Our findings suggest that normative expression profiles of disorder risk genes confer a shared and spatially patterned vulnerability of the cortex across multiple psychiatric conditions. Transdiagnostic overlap in transcriptomic risk suggests a common pathway to brain dysfunction across psychiatric disorders.

Human evolution is characterised by extensive changes of body and brain, with perhaps one of the core developments being the fast increase in cranial capacity and brain volume. Paleontological records are the most direct method to study such changes, but they can unfortunately provide a limited view of how ‘soft traits’ such as brain function and cognitive abilities have evolved in humans. A potential complementary approach is to identify when particular genetic variants associated with human phenotypes (such as height, body mass index, intelligence, and also disease) have emerged in the 6-7 million years since we diverged from chimpanzees. In this study, we combine data from genome-wide association studies on human brain and cognitive traits with estimates of human genome dating. We systematically analyse the temporal emergence of genetic variants associated with modern-day human brain and cognitive phenotypes over the last five million years. Our analysis provides evidence that genetic variants related to neocortex structure (e.g., area, thickness; median evolutionary age = 400,170 years old), cognition (e.g., fluid intelligence; median age = 459,465), education (median age = 637,646), and psychiatric disorders (median age = 412,639) have emerged more recently in human evolution than expected by chance. In contrast, variants related to other physical traits, such as height (median age = 811,305) and body mass index (median age = 794,265), emerged relatively later. We further show that genes containing recent evolutionary modifications (from around 54,000 to 4,000 years ago) are linked to intelligence ( P = 2 × 10 −6 ) and neocortical surface area ( P = 6.7 × 10 −4 ), and that these genes tend to be highly expressed in cortical areas involved in language and speech (pars triangularis, P = 6.2 × 10 −4 ). Elucidating the temporal dynamics of genetic variants associated with brain and cognition is another source of evidence to advance our understanding of human evolution.

Network neuroscience has emerged as a leading method to study brain connec-tivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

Connectomics has become a prime method for studying brain circuitry. The success of these investigations hinge on the capacity to detect the effects present in the data – that is, statistical power. Here, we discuss four main facets of power in connectomics: sample size, variance, effect size and network topology. We discuss how these factors (1) shape the overall power of connectome studies and (2) give rise to ‘differential power’ within individual studies, rendering some network effects easier to detect than others. We discuss how power impacts our understanding of brain networks and their circuitry and review strategies to optimally work with – not around – power in connectomics.

Background: Temstem is a mobile application developed in cooperation with voice-hearing persons to help them cope with distressing voices. After psychoeducation about voice hearing, Temstem offers two functions: Silencing is a mode designed to inhibit voice activity through the processing of incompatible language; the Challenging mode introduces dual tasking (as used in eye movement desensitisation and reprocessing) designed to reduce the emotionality and vividness of a voice memory. Two different language games, Lingo Tapper and Word Link, are provided, containing both functions. This study aimed to explore the momentary effects of Temstem on voice -hearing distress, emotionality and vividness in a naturalistic sample of voice-hearing app users.Method: Temstem is freely available in the Netherlands. We collected data through the app from 1048 individual users who had given informed consent for the study. We assessed changes in pre-and post-session scores on distress, emotionality and vividness, and we evaluated differences in outcomes between the games and whether effects remained stable over multiple sessions.Results: Users had been hearing voices for an average of 4.95 years; 79 % had been informed about Temstem by a mental health therapist or coach. After a Silencing session, voice-hearing distress was reduced, t(958) = 27.12, p < .001, d = 0.49; the degree of reduction remained stable after repeated use, F(1, 7905.57) = 1.91, p = .167. After a Challenging session, emotionality, t(651) = 23.16, p < .001, d = 0.74, and the vividness of voice memories were reduced, t(651) = 22.20, p < .001, d = 0.71; both diminished slightly with frequent use, F(1, 2222.86) = 7.21, p < .05; F(1, 2289.92) = 4.25, p < .05. In comparison with Lingo Tapper, larger reductions were seen for a Word Link game: emotionality t(226) = 2.88, p < .005, d = 0.21; vividness t(226) = 2.29, p < .05, d = 0.17.Discussion: In this heterogeneous sample of voice-hearing individuals, Temstem appeared to be a promising coping tool; momentary voice-hearing distress and the emotionality and vividness of voice statements were reduced after a Temstem session. Despite important limitations and the need for more research, naturalistic studies of user app data may yield interesting and generalisable findings.

An important current question in neuroimaging concerns the sample sizes required for producing reliable and reproducible results. Recent findings suggest that brain-wide association studies (BWAS) linking neuroimaging features with behavioural phenotypes in the general population are characterised by (very) weak effects and consequently need large samples sizes of 3000+ to lead to reproducible findings. A second, important goal in neuroimaging is to study brain structure and function under disease conditions, where effects are likely much larger. This difference in effect size is important. We show by means of power calculations and empirical analysis that neuroimaging studies in clinical populations need hundreds -and not necessarily thousands-of participants to lead to reproducible findings.

Abstract Background Evidence suggests that in individuals with psychosis, paranoia is reduced after trauma-focused therapy (TFT) aimed at co-morbid posttraumatic stress disorder (PTSD). Objective To identify mediators of the effect of TFT on paranoia. Method In a multicenter single-blind randomized controlled trial 155 outpatients in treatment for psychosis were allocated to 8 sessions Prolonged Exposure (PE; n=53), 8 sessions Eye Movement Desensitization and Reprocessing (EMDR) therapy (n=55), or a waiting-list condition (WL; n=47) for treatment of co-morbid PTSD. Measures were performed on (1) paranoia (GPTS); (2) DSM-IV-TR PTSD symptom clusters (CAPS-IV; i.e., intrusions, avoidance, and hyperarousal); (3) negative posttraumatic cognitions (PTCI; i.e., negative self posttraumatic cognitions, negative world posttraumatic cognitions and self-blame); (4) depression (BDI-II); and (5) cognitive biases (i.e., jumping to conclusion, attention to threat, belief inflexibility and external attribution), cognitive limitations (i.e., social cognition problems and subjective cognitive problems), and safety behaviors (DACOBS). Outcome in terms of symptoms of paranoia (1) and potential mediators (2-5) were evaluated at post-treatment, controlling for baseline scores. Results The effects of TFT on paranoia were primarily mediated by negative self and negative world posttraumatic cognitions, representing almost 70% of the total indirect effect. Safety behaviors and social cognition problems were involved in the second step mediational pathway models. Conclusions Targeting the cognitive dimension of PTSD in TFT in psychosis could be an effective way to influence paranoia, whereas addressing safety behaviors and social cognition problems might enhance the impact of TFT on paranoia.