The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Level I PD-MCI Using Global Cognitive Tests and the Risk for Parkinson's Disease Dementia

Background The 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. Methods The 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. Results PD-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. Conclusion MMSE and MoCA predict conversion to PDD. However, Level II neuropsychological assessment seems the preferred assessment for PD-MCI

Level I PD-MCI Using Global Cognitive Tests and the Risk for Parkinson's Disease Dementia.

BACKGROUND: The 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. METHODS: The 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. RESULTS: PD-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. CONCLUSION: MMSE and MoCA predict conversion to PDD. However, Level II neuropsychological assessment seems the preferred assessment for PD-MCI

Risk of Parkinson's disease dementia related to level I MDS PD-MCI.

BackgroundThe International Parkinson and Movement Disorders Society criteria for mild cognitive impairment in PD need validation. The objectives of this present study were to evaluate prognostic validity of level I (abbreviated) International Parkinson and Movement Disorders Society mild cognitive impairment in PD criteria for development of PD dementia and compared them with level II (comprehensive) criteria.MethodsWe analyzed data from 8 international studies (1045 patients) from our consortium that included baseline data on demographics, motor signs, depression, detailed neuropsychological testing, and longitudinal follow-up for conversion to Parkinson's disease dementia. Survival analysis evaluated their contribution to the hazard of Parkinson's disease dementia.ResultsLevel I mild cognitive impairment in PD, increasing age, male sex, and severity of PD motor signs independently increased the hazard of Parkinson's disease dementia. Level I and level II mild cognitive impairment in PD classification had similar discriminative ability with respect to the time to Parkinson's disease dementia.ConclusionsLevel I mild cognitive impairment in PD classification independently contributes to the hazard of Parkinson's disease dementia. This finding supports the prognostic validity of the abbreviated mild cognitive impairment in PD criteria. © 2019 International Parkinson and Movement Disorder Society

Risk of Parkinson's disease dementia related to level I MDS PD-MCI.

BackgroundThe International Parkinson and Movement Disorders Society criteria for mild cognitive impairment in PD need validation. The objectives of this present study were to evaluate prognostic validity of level I (abbreviated) International Parkinson and Movement Disorders Society mild cognitive impairment in PD criteria for development of PD dementia and compared them with level II (comprehensive) criteria.MethodsWe analyzed data from 8 international studies (1045 patients) from our consortium that included baseline data on demographics, motor signs, depression, detailed neuropsychological testing, and longitudinal follow-up for conversion to Parkinson's disease dementia. Survival analysis evaluated their contribution to the hazard of Parkinson's disease dementia.ResultsLevel I mild cognitive impairment in PD, increasing age, male sex, and severity of PD motor signs independently increased the hazard of Parkinson's disease dementia. Level I and level II mild cognitive impairment in PD classification had similar discriminative ability with respect to the time to Parkinson's disease dementia.ConclusionsLevel I mild cognitive impairment in PD classification independently contributes to the hazard of Parkinson's disease dementia. This finding supports the prognostic validity of the abbreviated mild cognitive impairment in PD criteria. © 2019 International Parkinson and Movement Disorder Society

Level I PD-MCI Using Global Cognitive Tests and the Risk for Parkinson's Disease Dementia

BackgroundThe 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&nbsp;=&nbsp;467). Risk for developing PDD was examined using a Cox model. Global cognitive tests were compared to neuropsychological test batteries (Level I&amp;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&nbsp;=&nbsp;2.57, P&nbsp;=&nbsp;0.001; HR&nbsp;=&nbsp;4.14, P&nbsp;=&nbsp;&lt;0.001). The C-statistics for MMSE (0.72) and MoCA (0.70) were lower than those based on neuropsychological tests (Level I&nbsp;=&nbsp;0.82; Level II&nbsp;=&nbsp;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