What can be expected in normal aging, and where does normal aging stop and pathological neurodegeneration begin? With the slow progression of age-related dementias such as Alzheimer's disease (AD), ...it is difficult to distinguish age-related changes from effects of undetected disease. We review recent research on changes of the cerebral cortex and the hippocampus in aging and the borders between normal aging and AD. We argue that prominent cortical reductions are evident in fronto-temporal regions in elderly even with low probability of AD, including regions overlapping the default mode network. Importantly, these regions show high levels of amyloid deposition in AD, and are both structurally and functionally vulnerable early in the disease. This normalcy-pathology homology is critical to understand, since aging itself is the major risk factor for sporadic AD. Thus, rather than necessarily reflecting early signs of disease, these changes may be part of normal aging, and may inform on why the aging brain is so much more susceptible to AD than is the younger brain. We suggest that regions characterized by a high degree of life-long plasticity are vulnerable to detrimental effects of normal aging, and that this age-vulnerability renders them more susceptible to additional, pathological AD-related changes. We conclude that it will be difficult to understand AD without understanding why it preferably affects older brains, and that we need a model that accounts for age-related changes in AD-vulnerable regions independently of AD-pathology.
Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we ...introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.
Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming ...because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.
►This atlas automatically provides 74 sulco-gyral structures per hemisphere. ►Automated and manual parcellations are concordant for 85% of the cortex ►This tool is included in FreeSurfer package (http://surfer.nmr.mgh.harvard.edu/).
Abstract Age-related changes in brain structure result from a complex interplay among various neurobiological processes, which may contribute to more complex trajectories than what can be described ...by simple linear or quadratic models. We used a nonparametric smoothing spline approach to delineate cross-sectionally estimated age trajectories of the volume of 17 neuroanatomic structures in 1100 healthy adults (18–94 years). Accelerated estimated decline in advanced age characterized some structures, for example hippocampus, but was not the norm. For most areas, 1 or 2 critical ages were identified, characterized by changes in the estimated rate of change. One-year follow-up data from 142 healthy older adults (60–91 years) confirmed the existence of estimated change from the cross-sectional analyses for all areas except 1 (caudate). The cross-sectional and the longitudinal analyses agreed well on the rank order of age effects on specific brain structures (Spearman ρ = 0.91). The main conclusions are that most brain structures do not follow a simple path throughout adult life and that accelerated decline in high age is not the norm of healthy brain aging.
Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of ...considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
Schizophrenia is a severe psychiatric disorder with considerable morbidity and mortality. Although the past two decades have seen limited improvement in the treatment of schizophrenia, research into ...the genetic causes of this condition has made important advances that offer new insights into the aetiology of schizophrenia. This Review summarizes the evidence for a polygenic architecture of schizophrenia that involves a large number of risk alleles across the whole range of population frequencies. These genetic risk loci implicate biological processes related to neurodevelopment, neuronal excitability, synaptic function and the immune system in the pathogenesis of schizophrenia. Mathematical models also suggest a substantial overlap between schizophrenia and psychiatric, behavioural and cognitive traits, a situation that has implications for understanding its clinical epidemiology, psychiatric nosology and pathobiology. Looking ahead, further genetic discoveries are expected to lead to clinically relevant predictive approaches for identifying high-risk individuals, improved diagnostic accuracy, increased yield from drug development programmes and improved stratification strategies to address the heterogeneous disease course and treatment responses observed among affected patients.
Recent advances in optical technologies such as multi-photon microscopy and optogenetics have revolutionized our ability to record and manipulate neuronal activity. Combining optical techniques with ...electrical recordings is of critical importance to connect the large body of neuroscience knowledge obtained from animal models to human studies mainly relying on electrophysiological recordings of brain-scale activity. However, integration of optical modalities with electrical recordings is challenging due to generation of light-induced artifacts. Here we report a transparent graphene microelectrode technology that eliminates light-induced artifacts to enable crosstalk-free integration of 2-photon microscopy, optogenetic stimulation, and cortical recordings in the same in vivo experiment. We achieve fabrication of crack- and residue-free graphene electrode surfaces yielding high optical transmittance for 2-photon imaging down to ~ 1 mm below the cortical surface. Transparent graphene microelectrode technology offers a practical pathway to investigate neuronal activity over multiple spatial scales extending from single neurons to large neuronal populations.
Understanding how brain development normally proceeds is a premise of understanding neurodevelopmental disorders. This has sparked a wealth of magnetic resonance imaging (MRI) studies. Unfortunately, ...they are in marked disagreement on how the cerebral cortex matures. While cortical thickness increases for the first 8-9 years of life have repeatedly been reported, others find continuous cortical thinning from early childhood, at least from age 3 or 4 years. We review these inconsistencies, and discuss possible reasons, including the use of different scanners, recording parameters and analysis tools, and possible effects of variables such as head motion. When tested on the same subsample, 2 popular thickness estimation methods (CIVET and FreeSurfer) both yielded a continuous thickness decrease from 3 years. Importantly, MRI-derived measures of cortical development are merely our best current approximations, hence the term "apparent cortical thickness" may be preferable. We recommend strategies for reaching consensus in the field, including multimodal neuroimaging to measure phenomena using different techniques, for example, the use of T1/T2 ratio, and data sharing to allow replication across analysis methods. As neurodevelopmental origins of early- and late-onset disease are increasingly recognized, resolving inconsistencies in brain maturation trajectories is important.
Alzheimer's disease (AD) has a slow onset, so it is challenging to distinguish brain changes in healthy elderly persons from incipient AD. One-year brain changes with a distinct frontotemporal ...pattern have been shown in older adults. However, it is not clear to what extent these changes may have been affected by undetected, early AD. To address this, we estimated 1-year atrophy by magnetic resonance imaging (MRI) in 132 healthy elderly persons who had remained free of diagnosed mild cognitive impairment or AD for at least 3 years. We found significant volumetric reductions throughout the brain. The sample was further divided into low-risk groups based on clinical, biomarker, genetic, or cognitive criteria. Although sample sizes varied, significant reductions were observed in all groups, with rates and topographical distribution of atrophy comparable to that of the full sample. Volume reductions were especially pronounced in the default mode network, closely matching the previously described frontotemporal pattern of changes in healthy aging. Atrophy in the hippocampus predicted change in memory, with no additional default mode network contributions. In conclusion, reductions in regional brain volumes can be detected over the course of 1 year even in older adults who are unlikely to be in a presymptomatic stage of AD.
Does accelerated cortical atrophy in aging, especially in areas vulnerable to early Alzheimer's disease (AD), unequivocally signify neurodegenerative disease or can it be part of normal aging? We ...addressed this in 3 ways. First, age trajectories of cortical thickness were delineated cross-sectionally (n = 1100) and longitudinally (n = 207). Second, effects of undetected AD on the age trajectories were simulated by mixing the sample with a sample of patients with very mild to moderate AD. Third, atrophy in AD-vulnerable regions was examined in older adults with very low probability of incipient AD based on 2-year neuropsychological stability, CSF Aβ(1-42) levels, and apolipoprotein ε4 negativity. Steady decline was seen in most regions, but accelerated cortical thinning in entorhinal cortex was observed across groups. Very low-risk older adults had longitudinal entorhinal atrophy rates similar to other healthy older adults, and this atrophy was predictive of memory change. While steady decline in cortical thickness is the norm in aging, acceleration in AD-prone regions does not uniquely signify neurodegenerative illness but can be part of healthy aging. The relationship between the entorhinal changes and changes in memory performance suggests that non-AD mechanisms in AD-prone areas may still be causative for cognitive reductions.
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