Biomarkers of behavior and psychiatric illness for cognitive and clinical neuroscience remain out of reach. Suboptimal reliability of biological measurements, such as functional magnetic resonance imaging (fMRI), is increasingly cited as a primary culprit for discouragingly large sample size requirements and poor reproducibility of brain-based biomarker discovery. In response, steps are being taken towards optimizing MRI reliability and increasing sample sizes, though this will not be enough. Optimizing biological measurement reliability and increasing sample sizes are necessary but insufficient steps for biomarker discovery; this focus has overlooked the ″other side of the equation″ — the reliability of clinical and cognitive assessments — which are often suboptimal or unassessed. Through a combination of simulation analysis and empirical studies using neuroimaging data, we demonstrate that the joint reliability of both biological and clinical/cognitive phenotypic measurements must be optimized in order to ensure biomarkers are reproducible and accurate. Even with best–case scenario high reliability neuroimaging measurements and large sample sizes, we show that suboptimal reliability of phenotypic data (i.e., clinical diagnosis, behavioral and cognitive measurements) will continue to impede meaningful biomarker discovery for the field. Improving reliability through development of novel assessments of phenotypic variation is needed, but it is not the sole solution. We emphasize the potential to improve the reliability of established phenotypic methods through aggregation across multiple raters and/or measurements, which is becoming increasingly feasible with recent innovations in data acquisition (e.g., web– and smart–phone-based administration, ecological momentary assessment, burst sampling, wearable devices, multimodal recordings). We demonstrate that such aggregation can achieve better biomarker discovery for a fraction of the cost engendered by large–scale samples. Although the current study has been motivated by ongoing developments in neuroimaging, the prioritization of reliable phenotyping will revolutionize neurobiological and clinical endeavors that are focused on brain and behavior.
Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns, which are increasingly manifested throughout the disease course, driven by underlying neuropathologic processes. Herein, we show that manifestations of these brain changes in early asymptomatic stages can be detected via a novel deep semi-supervised representation learning method. We first identified two dominant dimensions of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the diffuse-AD (R1) dimension shows widespread brain atrophy, and the MTL-AD (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with known genetic risk factors (e.g., APOE4) of AD in MCI and AD patients at baseline. We then showed that brain changes along these two dimensions were independently detected in early stages in a cohort representative of the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were also enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). The longitudinal progression of R1 and R2 in the cognitively unimpaired populations, as well as in individuals with MCI and AD, showed variable associations with established AD risk factors, including APOE4, tau, and amyloid. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction, driven by genes different from APOE, which collectively contribute to the early pathogenesis of AD.
The complex biological mechanisms underlying human brain aging remain incompletely understood. To investigate this, we utilized multimodal magnetic resonance imaging and artificial intelligence (AI) to examine the genetic heterogeneity of the brain age gap (BAG) derived from gray matter volume (GM-BAG), white matter tract (WM-BAG), and functional connectivity (FC-BAG). Sixteen significant genomic loci were identified, with GM-BAG loci showing abundant associations with neurodegenerative and neuropsychiatric traits, WM-BAG for cancer and Alzheimer's disease (AD), and FC-BAG for only insomnia. The gene-drug-disease network further corroborated these associations by highlighting genes linked to GM-BAG for the treatment of neurodegenerative and neuropsychiatric disorders, and WM-BAG genes for cancer therapy. GM-BAG showed the highest enrichment of heritability in conserved regions, while in WM-BAG, the 5' untranslated regions exhibited the highest heritability enrichment; oligodendrocytes and astrocytes showed significant heritability enrichment in WM and FC-BAG, respectively. Notably, Mendelian randomization identified risk causal effects of triglyceride-to-lipid ratio in VLDL and type 2 diabetes on GM-BAG, and AD on WM-BAG. These findings suggest that interventions targeting these factors and diseases may ameliorate human brain health. Overall, our results provide valuable insights into the genetic heterogeneity of human brain aging, with potential implications for lifestyle and therapeutic interventions.
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and brain diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies, 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are novel, and 72% were independently replicated. Key pathways influencing PSCs involved reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using machine learning, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate new genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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