Showing papers by "Fabrice Crivello published in 2012"
University of Washington1, University of California, Davis2, University of Iceland3, Erasmus University Rotterdam4, Centre national de la recherche scientifique5, University of Bordeaux6, University of Texas Health Science Center at Houston7, Boston University8, French Institute of Health and Medical Research9, Harvard University10, Broad Institute11, Medical University of Graz12, Monash University13, University of Tasmania14, Rush University Medical Center15, university of lille16, Mayo Clinic17, Université de Montréal18, Wake Forest University19, University of Queensland20, Illinois Institute of Technology21, Leiden University22, University of Mississippi23, Delft University of Technology24, University of California, Los Angeles25, QIMR Berghofer Medical Research Institute26, Radboud University Nijmegen27, National Institutes of Health28, Royal Children's Hospital29, University of Pittsburgh30, University of Bonn31, German Center for Neurodegenerative Diseases32
TL;DR: In this article, a genome-wide association study (GWAS) of dementia-free persons (n = 9,232) identified 46 SNPs at four loci with P values of <4.0 × 10(-7).
Abstract: Aging is associated with reductions in hippocampal volume that are accelerated by Alzheimer's disease and vascular risk factors. Our genome-wide association study (GWAS) of dementia-free persons (n = 9,232) identified 46 SNPs at four loci with P values of <4.0 × 10(-7). In two additional samples (n = 2,318), associations were replicated at 12q14 within MSRB3-WIF1 (discovery and replication; rs17178006; P = 5.3 × 10(-11)) and at 12q24 near HRK-FBXW8 (rs7294919; P = 2.9 × 10(-11)). Remaining associations included one SNP at 2q24 within DPP4 (rs6741949; P = 2.9 × 10(-7)) and nine SNPs at 9p33 within ASTN2 (rs7852872; P = 1.0 × 10(-7)); along with the chromosome 12 associations, these loci were also associated with hippocampal volume (P < 0.05) in a third younger, more heterogeneous sample (n = 7,794). The SNP in ASTN2 also showed suggestive association with decline in cognition in a largely independent sample (n = 1,563). These associations implicate genes related to apoptosis (HRK), development (WIF1), oxidative stress (MSR3B), ubiquitination (FBXW8) and neuronal migration (ASTN2), as well as enzymes targeted by new diabetes medications (DPP4), indicating new genetic influences on hippocampal size and possibly the risk of cognitive decline and dementia.
238 citations
TL;DR: Evidence is provided that modulation of spontaneous low-frequency fluctuations in the brain is at least partially explained by spontaneous conscious cognition while at rest, and mind wandering can be characterized by widespread modular segregation.
104 citations
TL;DR: The amygdala, which develops neuropathology in the early stage of AD and is involved in the pathogenesis of depression, may be an important brain structure involved inThe association between EPA and cognitive decline and depressive symptoms.
Abstract: Objective: The long-chain -3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are potential candidates for interventions to delay Alzheimer disease (AD), but evidence from clinical studies is mixed. We aimed at determining whether plasma levels of EPA or DHA predict atrophy of medial temporal lobe (MTL) gray matter regions in older subjects. Methods: A total of 281 community dwellers from the Three-City Study, aged 65 years or older, had plasma fatty acid measurements at baseline and underwent MRI examinations at baseline and at 4 years. We studied the association between plasma EPA and DHA and MTL gray matter volume change at 4 years. Results: Higher plasma EPA, but not DHA, was associated with lower gray matter atrophy of the right hippocampal/parahippocampal area and of the right amygdala (p 0.05, familywise error corrected). Based on a mean right amygdala volume loss of 6.0 mm 3 /y (0.6%) ,a1S Dhigher plasma EPA (0.64% of total plasma fatty acids) at baseline was related to a 1.3 mm 3 smaller gray matter loss per year in the right amygdala. Higher atrophy of the right amygdala was associated with greater 4-year decline in semantic memory performances and more depressive symptoms. Conclusion: The amygdala, which develops neuropathology in the early stage of AD and is involved in the pathogenesis of depression, may be an important brain structure involved in the association between EPA and cognitive decline and depressive symptoms. Neurology ® 2012;79:642–650
92 citations
University of Washington1, University of California, Davis2, University of Iceland3, Erasmus University Rotterdam4, Centre national de la recherche scientifique5, University of Bordeaux6, University of Texas Health Science Center at Houston7, Boston University8, French Institute of Health and Medical Research9, Harvard University10, Broad Institute11, Medical University of Graz12, Monash University13, University of Tasmania14, Rush University Medical Center15, university of lille16, Mayo Clinic17, Université de Montréal18, Wake Forest University19, University of Queensland20, Illinois Institute of Technology21, Leiden University22, University of Mississippi23, Delft University of Technology24, University of California, Los Angeles25, QIMR Berghofer Medical Research Institute26, Radboud University Nijmegen27, National Institutes of Health28, Royal Children's Hospital29, University of Pittsburgh30, German Center for Neurodegenerative Diseases31, University of Bonn32
TL;DR: These associations implicate genes related to apoptosis (HRK), development (WIF1), oxidative stress (MSR3B), ubiquitination (FBXW8) and neuronal migration (ASTN2), as well as enzymes targeted by new diabetes medications (DPP4), indicating new genetic influences on hippocampal size and possibly the risk of cognitive decline and dementia.
Abstract: Aging is associated with reductions in hippocampal volume that are accelerated by Alzheimer's disease and vascular risk factors. Our genome-wide association study (GWAS) of dementia-free persons ( n = 9,232) identified 46 SNPs at four loci with P values of −7 . In two additional samples ( n = 2,318), associations were replicated at 12q14 within MSRB3 - WIF1 (discovery and replication; rs17178006; P = 5.3 × 10 −11 ) and at 12q24 near HRK - FBXW8 (rs7294919; P = 2.9 × 10 −11 ). Remaining associations included one SNP at 2q24 within DPP4 (rs6741949; P = 2.9 × 10 −7 ) and nine SNPs at 9p33 within ASTN2 (rs7852872; P = 1.0 × 10 −7 ); along with the chromosome 12 associations, these loci were also associated with hippocampal volume ( P n = 7,794). The SNP in ASTN2 also showed suggestive association with decline in cognition in a largely independent sample ( n = 1,563). These associations implicate genes related to apoptosis ( HRK ), development ( WIF1) , oxidative stress ( MSR3B ), ubiquitination ( FBXW8 ) and neuronal migration ( ASTN2 ), as well as enzymes targeted by new diabetes medications ( DPP4 ), indicating new genetic influences on hippocampal size and possibly the risk of cognitive decline and dementia.
29 citations
TL;DR: This work proposes a method based on group analysis of individual ICA components, using a multi-scale clustering (MICCA), which proved to be reproducible in a random splitting of the data sample and more robust than the classical concatenation method.
Abstract: Functional connectivity-based analysis of functional magnetic resonance imaging data (fMRI) is an emerging technique for human brain mapping. One powerful method for the investigation of functional connectivity is independent component analysis (ICA) of concatenated data. However, this research field is evolving toward processing increasingly larger database taking into account inter-individual variability. Concatenated data analysis only handles these features using some additional procedures such as bootstrap or including a model of between-subject variability during the preprocessing step of the ICA. In order to alleviate these limitations, we propose a method based on group analysis of individual ICA components, using a multi-scale clustering (MICCA). MICCA start with two steps repeated several times: 1) single subject data ICA followed by 2) clustering of all subject independent components according to a spatial similarity criterion. A final third step consists in selecting reproducible clusters across the repetitions of the two previous steps. The core of the innovation lies in the multi-scale and unsupervised clustering algorithm built as a chain of three processes: robust proto-cluster creation, aggregation of the proto-clusters, and cluster consolidation. We applied MICCA to the analysis of 310 fMRI resting state dataset. MICCA identified 28 resting state brain networks. Overall, the cluster neuroanatomical substrate included 98% of the cerebrum gray matter. MICCA results proved to be reproducible in a random splitting of the data sample and more robust than the classical concatenation method.
17 citations
1 citations