It remains unknown whether migraine headache has a progressive component in its pathophysiology. Quantitative MRI may provide valuable insight into abnormal changes in the migraine interictum and assist in identifying disrupted brain networks. We carried out a data-driven study of structural integrity and functional connectivity of the resting brain in migraine without aura. MRI scanning was performed in 36 patients suffering from episodic migraine without aura and 33 age-matched healthy subjects. Voxel-wise analysis of regional brain volume was performed by registration of the T1-weighted MRI scans into a common study brain template using the tensor-based morphometry (TBM) method. Changes in functional synchronicity of the brain networks were assessed using probabilistic independent component analysis (ICA). TBM revealed that migraine is associated with reduced volume of the medial prefrontal cortex (mPFC). Among 375 functional brain networks, resting-state connectivity was decreased between two components spanning the visual cortex, posterior insula, and parietal somatosensory cortex. Our study reveals structural and functional alterations of the brain in the migraine interictum that may stem from underlying disease risk factors and the “silent” aura phenomenon. Longitudinal studies will be needed to investigate whether interictal brain changes are progressive and associated with clinical disease trajectories.

Background Attention‐deficit hyperactivity disorder (ADHD) is associated with white matter (WM) microstructure. Our objective was to investigate how WM microstructure is longitudinally related to symptom remission in adolescents and young adults with ADHD. Methods We obtained diffusion‐weighted imaging (DWI) data from 99 participants at two time‐points (mean age baseline: 16.91 years, mean age follow‐up: 20.57 years). We used voxel‐wise Tract‐Based Spatial Statistics (TBSS) with permutation‐based inference to investigate associations of inattention (IA) and hyperactivity‐impulsivity (HI) symptom change with fractional anisotropy (FA) at baseline, follow‐up, and change between time‐points. Results Remission of combined HI and IA symptoms was significantly associated with reduced FA at follow‐up in the left superior longitudinal fasciculus and the left corticospinal tract (CST; PFWE = 0.038 and PFWE = 0.044, respectively), mainly driven by an association between HI remission and follow‐up CST FA (PFWE = 0.049). There was no significant association of combined symptom decrease with FA at baseline or with changes in FA between the two assessments. Conclusions In this longitudinal DWI study of ADHD using dimensional symptom scores, we show that greater symptom decrease is associated with lower follow‐up FA in specific WM tracts. Altered FA thus may appear to follow, rather than precede, changes in symptom remission. Our findings indicate divergent WM developmental trajectories between individuals with persistent and remittent ADHD, and support the role of prefrontal and sensorimotor tracts in the remission of ADHD.

Molecular mechanisms underlying Alzheimer's disease (AD) are difficult to investigate, partly because diagnosis lags behind the insidious pathological processes. Therefore, identifying AD neuroimaging markers and their genetic modifiers may help study early mechanisms of neurodegeneration. We aimed to identify brain regions of the highest vulnerability to AD using a data‐driven search in the AD Neuroimaging Initiative (ADNI, n = 1,100 subjects), and further explored genetic variants affecting this critical brain trait using both ADNI and the younger UK Biobank cohort ( n = 8,428 subjects). Tensor‐Based Morphometry (TBM) and Independent Component Analysis (ICA) identified the limbic system and its interconnecting white‐matter as the most AD‐vulnerable brain feature. Whole‐genome analysis revealed a common variant in SHARPIN that was associated with this imaging feature (rs34173062, p = 2.1 × 10 −10 ). This genetic association was validated in the UK Biobank, where it was correlated with entorhinal cortical thickness bilaterally ( p = .002 left and p = 8.6 × 10 −4 right), and with parental history of AD ( p = 2.3 × 10 −6 ). Our findings suggest that neuroanatomical variation in the limbic system and AD risk are associated with a novel variant in SHARPIN. The role of this postsynaptic density gene product in β1‐integrin adhesion is in line with the amyloid precursor protein (APP) intracellular signaling pathway and the recent genome‐wide evidence.

Aging, the greatest risk factor for Alzheimer's disease (AD), may lead to the accumulation of somatic mutations in neurons. We investigated whether somatic mutations, specifically in longer genes, are implicated in AD etiology. First, we modeled the theoretical likelihood of genes being affected by aging‐induced somatic mutations, dependent on their length. We then tested this model and found that long genes are indeed more affected by somatic mutations and that their expression is more frequently reduced in AD brains. Furthermore, using gene‐set enrichment analysis, we investigated the potential consequences of such long gene disruption. We found that long genes are involved in synaptic adhesion and other synaptic pathways that are predicted to be inhibited in the brains of AD patients. Taken together, our findings indicate that long gene–dependent synaptic impairment may contribute to AD pathogenesis.

IntroductionThe last decade has seen a surge in well powered genome-wide association studies (GWASs) of complex behavioural traits, disorders, and more recently, of brain structural and functional neuroimaging features. However, the extreme polygenicity of these complex traits makes it difficult to translate the GWAS signal into mechanistic biological insights. We postulate that the covariance of SNP-effects across many brain features, as be captured by latent genomic components of SNP effect sizes. These may partly reflect the concerted multi-locus genomic effects through known molecular pathways and protein-protein interactions. Here, we test the feasibility of a new data-driven method to derive such latent components of genome-wide effects on more than thousand neuroimaging derived traits, and investigate their utility in interpreting the complex biological processes that shape the GWAS signal.MethodsWe downloaded the GWAS summary statistics of 3,143 brain imaging-derived phenotypes (IDPs) from the UK Biobank, provided by the Oxford Brain Imaging Genetics (BIG) Server (Elliott et al. 2018). Probabilistic independent component analysis (ICA) was used to extract two hundred independent genomic components from the matrix of SNP-effect sizes. We qualitatively describe the distribution of the latent component’s loadings in the neuroimaging and the genomic dimensions. Gene-wide statistics were calculated for each genomic component. We tested the genomic component’s enrichment for molecular pathways using MSigDB, and for single-cell RNA-sequencing of adult and foetal brain cells.Results200 components explained 80% of the variance in SNP-effects sizes. Each MRI modality and data processing method projected the imaging data into a clearly distinct cluster in the genomic component embedded space. Among the 200 genomic components, 157 were clearly driven by a single locus, while 39 were highly polygenic. Together, these 39 components were significantly enriched for 2,274 MSigDB gene sets (fully corrected for multiple testing across gene-sets and components). Several components were sensitive to molecular pathways, single cell expression profiles, and brain traits in patterns consistent with knowledge across these biological levels. To illustrate this, we highlight a component that implicated axonal regeneration pathways, which was specifically enriched for gene expression in oligodendrocyte precursors, microglia and astrocytes, and loaded highly on white matter neuroimaging traits. We highlight a second component that implicated synaptic function and neuron projection organization pathways that was specifically enriched for neuronal cell transcriptomes.ConclusionWe propose genomic ICA as a new method to identify latent genetic factors influencing brain structure and function by multimodal MRI. The derived latent genomic dimensions are highly sensitive to known molecular pathways and cell-specific gene expression profiles. Genomic ICA may help to disentangle the many different biological routes by which the genome defines the inter-individual variation of the brain. Future research is aimed at using this method to profile individual subjects’ genomic data along the new latent dimensions and evaluating the utility of these dimensions in stratifying heterogeneous patient populations.

Highlights Reduced volume in frontal lobes, striatum, and their interconnecting white matter in ADHD. Cross-cultural and age-independent validity of the study including two independent cohorts. Data driven approach using ICA of whole-brain morphometry images.

23All of the drug trials of the Alzheimer's disease (AD) have failed to slow progression of 24 dementia in phase III studies, and the most effective therapeutic approach still remains 25 controversial due to our incomplete understanding of AD pathophysiology. Amyloid beta (Aβ) 26 and its cascade have been the primary focus of drug design efforts for more than a decade. 27However, mounting evidence indicates that mechanisms of AD etiopathogenesis are probably 28 more complex than the previous reductionist models. 29Several genome-wide association studies (GWAS) have recently shed light on dark aspects of 30AD from a hypothesis-free point of view. While the newly-identified AD risk genes rather raise 31 more questions than they answer in deciphering the amyloid cascade, as a potentially overlooked 32 finding, many of them code for receptors and transducers of cell adhesion signaling cascades. 33Remarkably, the hallmark genetic factors of AD, including the amyloid precursor protein (APP), 34 presenilins (PSEN) and APOE also take part in highly similar pathways of cell adhesion 35 regulation and coordinate contact-guidance of neuronal growth cones in brain development, 36 albeit these Aβ-independent roles remain highly underexplored. 37Here, we have revisited function of 27 AD risk genes in pathways of normal cell physiology. Our

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