May 15, 2025
Østergaard FG, Kas MJH.
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
Mar 4, 2025
Bussu G, Portugal AM, Falck-Ytter T.
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
Jan 13, 2025
Floris DL, Llera A, Zabihi M, Moessnang C, Jones EJH, Mason L, Haartsen R, Holz NE, Mei T, Elleaume C, Vieira BH, Pretzsch CM, Forde NJ, Baumeister S, Dell’Acqua F, Durston S, Banaschewski T, Ecker C, Holt RJ, Baron-Cohen S, Bourgeron T, Charman T, Loth E, Murphy DGM, Buitelaar JK, Beckmann CF; EU–AIMS LEAP group; Langer N.
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
Dec 28, 2024
Cai N, Verhulst B, Andreassen OA, Buitelaar J, Edenberg HJ, Hettema JM, Gandal M, Grotzinger A, Jonas K, Lee P, Mallard TT, Mattheisen M, Neale MC, Nurnberger JI Jr, Peyrout W, Tucker-Drob EM, Smoller JW, Kendler KS.
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
Dec 19, 2024
Seelemeyer H, Gurr C, Leyhausen J, Berg LM, Pretzsch CM, Schäfer T, Hermila B, Freitag CM, Loth E, Oakley B, Mason L, Buitelaar JK, Beckmann CF, Floris DL, Charman T, Banaschewski T, Jones E, Bourgeron T; EU-AIMS LEAP Group; Murphy D, Ecker C.
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
As part of the “Horizon 2020” programme, the European Commission is funding the five-year CANDY research project with a total of six million Euro under grant agreement No. 847818.