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Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment.

Moon SW, Dinov ID, Zamanyan A, Shi R, Genco A, Hobel S, Thompson PM, Toga AW, Alzheimer's Disease Neuroimaging Initiative (ADN - Psychiatry Investig (2015)

Bottom Line: For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume).For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Konkuk University School of Medicine, Chungju, Republic of Korea.

ABSTRACT

Objective: This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.

Methods: Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.

Results: We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).

Conclusion: We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

No MeSH data available.


Related in: MedlinePlus

Individual brain parcellation and LONI Probabilistic Brain Atlas (LPBA40) atlas.
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Figure 2: Individual brain parcellation and LONI Probabilistic Brain Atlas (LPBA40) atlas.

Mentions: From the collection of 280 imaging markers (56 ROIs x five shape measures), we chose the 15 most significant neuroimaging biomarkers that provided the highest discrimination between the EO-AD and EO-MCI groups. We selected 15 neuroimaging phenotypes using t-tests that compared the EO-AD and EO-MCI groups (with an a priori false-positive rate of 0.05). These biomarkers are described in Table 1. The 15 neuroimaging biomarkers were derived from the structural imaging data using the GSA workflow and are based on the automated ROI extractions generated by BrainParser.14,15Figure 2 illustrates the LPBA40 atlas, an example of the 3D reconstruction of the BrainParser output for one subject, and the names of the 56 ROIs are shown in Table 2.


Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment.

Moon SW, Dinov ID, Zamanyan A, Shi R, Genco A, Hobel S, Thompson PM, Toga AW, Alzheimer's Disease Neuroimaging Initiative (ADN - Psychiatry Investig (2015)

Individual brain parcellation and LONI Probabilistic Brain Atlas (LPBA40) atlas.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4310910&req=5

Figure 2: Individual brain parcellation and LONI Probabilistic Brain Atlas (LPBA40) atlas.
Mentions: From the collection of 280 imaging markers (56 ROIs x five shape measures), we chose the 15 most significant neuroimaging biomarkers that provided the highest discrimination between the EO-AD and EO-MCI groups. We selected 15 neuroimaging phenotypes using t-tests that compared the EO-AD and EO-MCI groups (with an a priori false-positive rate of 0.05). These biomarkers are described in Table 1. The 15 neuroimaging biomarkers were derived from the structural imaging data using the GSA workflow and are based on the automated ROI extractions generated by BrainParser.14,15Figure 2 illustrates the LPBA40 atlas, an example of the 3D reconstruction of the BrainParser output for one subject, and the names of the 56 ROIs are shown in Table 2.

Bottom Line: For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume).For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Konkuk University School of Medicine, Chungju, Republic of Korea.

ABSTRACT

Objective: This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers.

Methods: Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers.

Results: We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area).

Conclusion: We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

No MeSH data available.


Related in: MedlinePlus