Genomic Studies Across the Lifespan Point to Early Mechanisms Determining Subcortical Volumes

Brain State Convergence and Divergence as Resting State FMRI Biomarkers: A Large-Scale Study of Continuous, Overlapping, Time-resolved States Differentiates Four Psychiatric Disorders

Identifying biomarkers- objective, quantifiable biologically-based measures to complement traditional clinical assessments- is critical for studying the links between brain and disorders. Recent advances in neuroimaging have shifted biomarker discovery from traditional univariate brain mapping techniques, which analyze individual brain regions separately, to multivariate predictive models that consider complex patterns across multiple regions, with dynamic functional network connectivity (dFNC) emerging as a key approach offering a dynamic view of the temporal coupling between different brain networks. Here, we introduce an innovative approach to estimate dynamic double functional independent primitives (ddFIP) by first applying a spatially constrained independent component analysis (ICA) to derive intrinsic connectivity networks (ICNs), followed by a second ICA applied to dFNC matrices. This procedure provides a set of states that reflect dynamic connectivity patterns. To characterize these states, we propose several dynamic measures: (1) amplitude convergence, which quantifies the extent to which multiple states contribute similarly to the connectivity profile at a given time (indicating more uniform state contributions); (2) amplitude divergence, quantifying the tendency for states to contribute at varying levels which does not assume dominance but rather reflects a spread of amplitudes across states; as well as (3) dynamic state density which shows the number of strongly occupied states, reflecting the brains preference for spending time in a smaller or larger set of dominant states. We apply this approach to uncover ddFIP-based biomarkers from seven resting-state functional magnetic resonance imaging (rs-fMRI) clinical datasets, which include four neuropsychiatric disorders- schizophrenia (SCZ), autism spectrum disorder (ASD), major depressive disorder (MDD), and bipolar disorder (BPD)- comprising a total of 5,805 participants. Our results revealed disorder-specific patterns in dynamic connectivity measures. SCZ exhibited widespread disruptions with high variability and increased divergence, suggesting a tendency for states to contribute at varying levels rather than uniformly. ASD, in contrast, showed significantly reduced divergence and increased convergence, indicating more uniform contributions across states and atypical stability in dynamic connectivity. BPD demonstrated heightened variability, particularly in mood regulation networks, while MDD displayed moderate disruptions, especially in self-referential processing networks. Notably, ASDs increased state convergence reflects a pattern where state weights are more similar, was sharply distinct from SCZs increased divergence, as indicated by state occupancy measures. In sum, our findings highlight the potential of continuous dFNC as a FNC-based biomarker to capture disorder-specific connectivity signatures. Moreover, by analyzing both the convergence and divergence of dynamic states, we capture a detailed view of connectivity, reflecting the brains adaptability and resilience within each disorder.

Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

Heritability of Subcortical Grey Matter Structures

Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson's correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45-0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72-0.85). A strong correlation between CAT12 and volBrain (r = 0.74-0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases.