ABSTRACTBackgroundThe criteria for PD‐MCI allow the use of global cognitive tests. Their predictive value for conversion from PD‐MCI to PDD, especially compared to comprehensive neuropsychological assessment, is unknown.MethodsThe MDS PD‐MCI Study Group combined four datasets containing global cognitive tests as well as a comprehensive neuropsychological assessment to define PD‐MCI (n = 467). Risk for developing PDD was examined using a Cox model. Global cognitive tests were compared to neuropsychological test batteries (Level I&II) in determining risk for PDD.ResultsPD‐MCI based on a global cognitive test (MMSE or MoCA) increases the hazard for developing PDD (respectively HR = 2.57, P = 0.001; HR = 4.14, P = <0.001). The C‐statistics for MMSE (0.72) and MoCA (0.70) were lower than those based on neuropsychological tests (Level I = 0.82; Level II = 0.81). Sensitivity, specificity and diagnostic accuracy balance was best in Level II.ConclusionMMSE and MoCA predict conversion to PDD. However, Level II neuropsychological assessment seems the preferred assessment for PD‐MCI.
The genetic architecture of the human cerebral cortex
INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS
Risk of Parkinson's disease dementia related to level I MDS PD‐MCI
ObjectiveThe relationship between Parkinson disease (PD), PD with dementia (PDD), and dementia with Lewy bodies (DLB) has long been debated. Although PD is primarily considered a motor disorder, cognitive impairment is often present at diagnosis, and only ∼20% of patients remain cognitively intact in the long term. Alpha‐synuclein (SNCA) was first implicated in the pathogenesis of the disease when point mutations and locus multiplications were identified in familial parkinsonism with dementia. In worldwide populations, SNCA genetic variability remains the most reproducible risk factor for idiopathic PD. However, few investigators have looked at SNCA variability in terms of cognitive outcomes.MethodsWe have used targeted high‐throughput sequencing to characterize the 135kb SNCA locus in a large multinational cohort of patients with PD, PDD, and DLB and healthy controls.ResultsAn analysis of 43 tagging single nucleotide polymorphisms across the SNCA locus shows 2 distinct association profiles for symptoms of parkinsonism and/or dementia, respectively, toward the 3′ or the 5′ of the SNCA gene. In addition, we define a specific haplotype in intron 4 that is directly associated with PDD. The PDD risk haplotype has been interrogated at single nucleotide resolution and is uniquely tagged by an expanded TTTCn repeat.InterpretationOur data show that PD, PDD, and DLB, rather than a disease continuum, have distinct genetic etiologies albeit within one genomic locus. Such results may serve as prognostic biomarkers to these disorders, to inform physicians and patients, and to assist in the design and stratification of clinical trials aimed at disease modification. Ann Neurol 2016;79:991–999