Front Neurosci. 2025 Apr 30;19:1488709. doi: 10.3389/fnins.2025.1488709. eCollection 2025.

ABSTRACT

Manual scoring of longitudinal electroencephalographical (EEG) data is a slow and time-consuming process. Current advances in the application of machine-learning and artificial intelligence to EEG data are moving fast; however, there is still a need for expert raters to validate scoring of EEG data. We hypothesized that power-frequency profiles are determining the state and ‘set the framework’ for communication between neurons. Based on these assumptions, a scoring method with a set frequency profile for each vigilance state, both in sleep and awake, was developed and validated. We defined seven states of the functional brain with unique profiles in terms of frequency-power spectra, coherence, phase-amplitude coupling, α exponent, functional excitation-inhibition balance (fE/I), and aperiodic exponent. The new method requires a manual check of wake-sleep transitions and is therefore considered semi-automatic. This semi-automatic approach showed similar α exponent and fE/I when compared to traces scored manually. The new method was faster than manual scoring, and the advanced outcomes of each state were stable across datasets and epoch length. When applying the new method to the neurexin-1α (Nrxn1α) gene deficient mouse, a model of synaptic dysfunction relevant to autism spectrum disorders, several genotype differences in the 24-h distribution of vigilance states were detected. Most prominent was the decrease in slow-wave sleep when comparing wild-type mice to Nrxn1α-deficient mice. This new methodology puts forward an optimized and validated EEG analysis pipeline for the identification of translational electrophysiological biomarkers for brain disorders that are related to sleep architecture and E/I balance.

PMID:40370661 | PMC:PMC12075235 | DOI:10.3389/fnins.2025.1488709

J Child Psychol Psychiatry. 2025 Aug;66(8):1182-1196. doi: 10.1111/jcpp.14143. Epub 2025 Mar 4.

ABSTRACT

BACKGROUND: Infants vary significantly in the way they process and respond to sensory stimuli, and altered sensory processing has been reported among infants later diagnosed with autism. Previous work with adolescents and adults suggests that variability in sensory processing may have a strong genetic basis. Yet, little is known about the etiological factors influencing sensory differences in infancy, when brain circuits supporting social and non-social cognition are sculpted and learning about the world via sensory input largely occurs in interaction with caregivers.

METHODS: We analysed data from a community sample of monozygotic (MZ) and dizygotic (DZ) 5-month-old same-sex twins (n = 285 pairs, n = 158 MZ pairs, n = 150 male pairs) from the BabyTwins Study in Sweden (BATSS) using exploratory factor analysis, generalised estimating equations and multivariate twin models to delineate the phenotypic and etiological structure of individual variability across different sensory processing dimensions, as measured by the Infant/Toddler Sensory Profile. Developmental links to later autistic traits were also assessed, as measured by total scores from the Quantitative Checklist for Autism in Toddlers at 36 months.

RESULTS: Results suggested separability between sensory processing dimensions (i.e. sensation seeking, sensation avoiding, sensory sensitivity and low registration) at a phenotypic and etiological level, with significant contributions from additive genetics and family environment that were unique to each sensory dimension and significant but smaller contributions from shared influences. Sensory domains also showed etiological separability, with unique genetic influences to each domain, while contributions from shared environment were in part shared across domains. A higher incidence of tactile-related behaviours and behaviours associated with sensory sensitivity, sensation avoiding, and low registration were significantly associated with higher levels of autistic traits in toddlerhood.

CONCLUSIONS: This study provides a map of the phenotypic and etiological structure of sensory processing in infancy, which will be informative for studies of both typical and atypical development.

PMID:40035145 | PMC:PMC12267685 | DOI:10.1111/jcpp.14143

Nat Ment Health. 2025;3(1):31-45. doi: 10.1038/s44220-024-00349-4. Epub 2025 Jan 2.

ABSTRACT

Atypical face processing is commonly reported in autism. Its neural correlates have been explored extensively across single neuroimaging modalities within key regions of the face processing network, such as the fusiform gyrus (FFG). Nonetheless, it is poorly understood how variation in brain anatomy and function jointly impacts face processing and social functioning. Here we leveraged a large multimodal sample to study the cross-modal signature of face processing within the FFG across four imaging modalities (structural magnetic resonance imaging (MRI), resting-state functional magnetic resonance imaging, task-functional magnetic resonance imaging and electroencephalography) in 204 autistic and nonautistic individuals aged 7-30 years (case-control design). We combined two methodological innovations-normative modeling and linked independent component analysis-to integrate individual-level deviations across modalities and assessed how multimodal components differentiated groups and informed social functioning in autism. Groups differed significantly in a multimodal component driven by bilateral resting-state functional MRI, bilateral structure, right task-functional MRI and left electroencephalography loadings in face-selective and retinotopic FFG. Multimodal components outperformed unimodal ones in differentiating groups. In autistic individuals, multimodal components were associated with cognitive and clinical features linked to social, but not nonsocial, functioning. These findings underscore the importance of elucidating multimodal neural associations of social functioning in autism, offering potential for the identification of mechanistic and prognostic biomarkers.

PMID:39802935 | PMC:PMC11717707 | DOI:10.1038/s44220-024-00349-4

Mol Psychiatry. 2025 Apr;30(4):1627-1638. doi: 10.1038/s41380-024-02878-x. Epub 2024 Dec 27.

ABSTRACT

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.

PMID:39730880 | PMC:PMC11919726 | DOI:10.1038/s41380-024-02878-x

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Dec 17:S2451-9022(24)00379-3. doi: 10.1016/j.bpsc.2024.12.003. Online ahead of print.

ABSTRACT

BACKGROUND: Autism is accompanied by highly individualized patterns of neurodevelopmental differences in brain anatomy. This variability makes the neuroanatomy of autism inherently difficult to describe at the group level. Here, we examined interindividual neuroanatomical differences using a dimensional approach that decomposed the domains of social communication and interaction (SCI), restricted and repetitive behaviors (RRBs), and atypical sensory processing (ASP) within a neurodiverse study population. Moreover, we aimed to link the resulting neuroanatomical patterns to specific molecular underpinnings.

METHODS: Neurodevelopmental differences in cortical thickness (CT) and surface area (SA) were correlated with SCI, RRB, and ASP domain scores by regression of a general linear model in a large neurodiverse sample of 288 autistic individuals and 140 nonautistic individuals, ages 6 to 30 years, recruited within the European Autism Interventions Longitudinal European Autism Project (EU-AIMS LEAP). The domain-specific patterns of neuroanatomical variability were subsequently correlated with cortical gene expression profiles via the Allen Human Brain Atlas.

RESULTS: Across groups, behavioral variations in SCI, RRBs, and ASP were associated with interindividual differences in CT and SA in partially non-overlapping frontoparietal, temporal, and occipital networks. These domain-specific imaging patterns were enriched for genes that 1) are differentially expressed in autism, 2) mediate typical brain development, and 3) are associated with specific cortical cell types. Many of these genes were implicated in pathways governing synaptic structure and function.

CONCLUSIONS: Our study corroborates the close relationship between neuroanatomical variation and interindividual differences in autism-related symptoms and traits within the general framework of neurodiversity and links domain-specific patterns of neuroanatomical differences to putative molecular underpinnings.

PMID:39701384 | DOI:10.1016/j.bpsc.2024.12.003

Pretzsch CM, Arenella M, Lerch JP, Lombardo MV, Beckmann C, Schaefer T, Leyhausen J, Gurr C, Bletsch A, Berg LM, Seelemeyer H, Floris DL, Oakley B, Loth E, Bourgeron T, Charman T, Buitelaar J, McAlonan G, Murphy D, Ecker C; EU-AIMS LEAP Group.

JAMA Psychiatry. 2024 Oct 16. doi: 10.1001/jamapsychiatry.2024.3194. Online ahead of print.

ABSTRACT

IMPORTANCE: In the neurotypical brain, regions develop in coordinated patterns, providing a fundamental scaffold for brain function and behavior. Whether altered patterns contribute to clinical profiles in neurodevelopmental conditions, including autism, remains unclear.

OBJECTIVES: To examine if, in autism, brain regions develop differently in relation to each other and how these differences are associated with molecular/genomic mechanisms and symptomatology.

DESIGN, SETTING, AND PARTICIPANTS: This study was an analysis of one the largest deep-phenotyped, case-control, longitudinal (2 assessments separated by approximately 12-24 months) structural magnetic resonance imaging and cognitive-behavioral autism datasets (EU-AIMS Longitudinal European Autism Project [LEAP]; study dates, February 2014-November 2017) and an out-of-sample validation in the Brain Development Imaging Study (BrainMapASD) independent cohort. Analyses were performed during the 2022 to 2023 period. This multicenter study included autistic and neurotypical children, adolescents, and adults. Autistic participants were included if they had an existing autism diagnosis (DSM-IV/International Statistical Classification of Diseases and Related Health Problems, Tenth Revision or DSM-5 criteria). Autistic participants with co-occurring psychiatric conditions (except psychosis/bipolar disorder) and those taking regular medications were included.

EXPOSURES: Neuroanatomy of neurotypical and autistic participants.

MAIN OUTCOMES AND MEASURES: Intraindividual changes in surface area and cortical thickness over time, analyzed via surface-based morphometrics.

RESULTS: A total of 386 individuals in the LEAP cohort (6-31 years at first visit; 214 autistic individuals, mean [SD] age, 17.3 [5.4] years; 154 male [72.0%] and 172 neurotypical individuals, mean [SD] age, 16.35 [5.7] years; 108 male [62.8%]) and 146 individuals in the BrainMapASD cohort (11-18 years at first visit; 49 autistic individuals, mean [SD] age, 14.31 [2.4] years; 42 male [85.7%] and 97 neurotypical individuals, mean [SD] age, 14.10 [2.5] years; 58 male [59.8%]). Maturational between-group differences in cortical thickness and surface area were established that were mostly driven by sensorimotor regions (eg, across features, absolute loadings for early visual cortex ranged from 0.07 to 0.11, whereas absolute loadings for dorsolateral prefrontal cortex ranged from 0.005 to 0.06). Neurodevelopmental differences were transcriptomically enriched for genes expressed in several cell types and during various neurodevelopmental stages, and autism candidate genes (eg, downregulated genes in autism, including those regulating synaptic transmission; enrichment odds ratio =3.7; P =2.6 × -10). A more neurotypical, less autismlike maturational profile was associated with fewer social difficulties and more typical sensory processing (false discovery rate P <.05; Pearson r ≥0.17). Results were replicated in the independently collected BrainMapASD cohort.

CONCLUSIONS AND RELEVANCE: Results of this case-control study suggest that the coordinated development of brain regions was altered in autism, involved a complex interplay of temporally sensitive molecular mechanisms, and may be associated with both lower-order (eg, sensory) and higher-order (eg, social) clinical features of autism. Thus, examining maturational patterns may provide an analytic framework to study the neurobiological origins of clinical profiles in neurodevelopmental/mental health conditions.

PMID:39412777 | DOI:10.1001/jamapsychiatry.2024.3194

Arango C, Fegert JM, Picarel-Blanchot F, Marx U, Truffaut-Chalet L, Pénélaud PF, Buitelaar J

Eur Child Adolesc Psychiatry. 2024 Oct 10. doi: 10.1007/s00787-024-02587-4. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) in young people is a common psychiatric disorder, but treatment options are limited. Agomelatine has demonstrated short-term efficacy and safety in pediatric patients. We report here the results of a 92-week open-label extension (OLE). The international, multicenter, double-blind, study randomized 400 patients (80 children, 320 adolescents) with moderate-to-severe MDD to one of four treatment groups: agomelatine 10 mg (n = 102), agomelatine 25 mg (n = 95), placebo (n = 103), and fluoxetine 10-20 mg (n = 100). After 12 weeks, patients who could benefit from treatment continuation were offered entry into an optional OLE during which they received agomelatine 10 or 25 mg for a further 92 weeks. A total of 339 patients (271 adolescents) entered the OLE. Treatment groups considered for the OLE analysis reflected those received in the double-blind and OLE periods: agomelatine (10 or 25 mg) in both (ago/ago, n = 170); placebo then agomelatine 10-25 mg (pcb/ago, n = 85); or fluoxetine then agomelatine 10-25 mg (fluox/ago, n = 84). Mean age (± SD) at entry into the double-blind phase (Week 0) was 13.6 ± 2.7 years and 61.9% were female. Mean changes in Children’s Depression Rating Scale revised (CDRS-R) raw total score from Week 12 to last post-Week 12 value in the three groups were – 16.3 ± 12.2 (ago/ago), – 18.9 ± 16.1 (pcb/ago), and – 16.1 ± 15.5 (fluox/ago), reflecting the difference in efficacy between treatments during the double-blind period, and heterogeneity at W12 between the treatment groups. Adverse events considered related to treatment occurred in 14.5% of patients: 15.3% ago/ago, 16.5% pcb/ago, and 10.7% fluox/ago. Three patients (all adolescents) experienced treatment-related severe adverse events: two treated with ago/ago and one treated with pcb/ago. Among the adolescents, one treatment-related severe adverse event in a patient in the pcb/ago group led to study withdrawal. Agomelatine was associated with continuous improvement in depressive symptoms without unexpected safety signals. These findings support the safe use of agomelatine in a pediatric population with moderate-to-severe MDD for up to 104 weeks.Trial registration No: EUDRACT No. 2015-002181-23.

PMID:39390266 | DOI:10.1007/s00787-024-02587-4

Kurth F, Schijven D, van den Heuvel OA, Hoogman M, van Rooij D, Stein DJ, Buitelaar JK, Bölte S, Auzias G, Kushki A, Venkatasubramanian G, Rubia K, Bollmann S, Isaksson J, Jaspers-Fayer F, Marsh R, Batistuzzo MC, Arnold PD, Bressan RA, Stewart SE, Gruner P, Sorensen L, Pan PM, Silk TJ, Gur RC, Cubillo AI, Haavik J, O’Gorman Tuura RL, Hartman CA, Calvo R, McGrath J, Calderoni S, Jackowski A, Chantiluke KC, Satterthwaite TD, Busatto GF, Nigg JT, Gur RE, Retico A, Tosetti M, Gallagher L, Szeszko PR, Neufeld J, Ortiz AE, Ghisleni C, Lazaro L, Hoekstra PJ, Anagnostou E, Hoekstra L, Simpson B, Plessen JK, Deruelle C, Soreni N, James A, Narayanaswamy J, Reddy JY, Fitzgerald J, Bellgrove MA, Salum GA, Janssen J, Muratori F, Vila M, Giral MG, Ameis SH, Bosco P, Remnélius KL, Huyser C, Pariente JC, Jalbrzikowski M, Rosa PG, O’Hearn KM, Ehrlich S, Mollon J, Zugman A, Christakou A, Arango C, Fisher SE, Kong X, Franke B, Medland SE, Thomopoulos SI, Jahanshad N, Glahn DC, Thompson PM, Francks C, Luders E.

Hum Brain Mapp. 2024 Aug 1;45(11):e26754. doi: 10.1002/hbm.26754.

ABSTRACT

Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.

PMID:39046031 | PMC:PMC11267452 | DOI:10.1002/hbm.26754