Gene Network Effects on Brain Microstructure and Intellectual Performance Identified in 472 Twins

Chiang, Ming‐Chang; Barysheva, Marina; McMahon, Katie L.; Zubicaray, Greig I. de; Johnson, Kori; Montgomery, Grant W.; Martin, Nicholas G.; Toga, Arthur W.; Wright, Margaret J.; Shapshak, Paul; Thompson, Paul M.

doi:10.1523/jneurosci.5993-11.2012

Cited by 54 publications

(55 citation statements)

References 75 publications

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“…As imaging genetic studies of WM indicated that several genes are associated with WM microstructure (35,36), WM-related genes likely have effects across all WM fibers. These possibilities, in turn, suggest that factor analysis or PCA may be more informative for imaging genetics studies.…”

Section: Discussionmentioning

confidence: 99%

Common and heritable components of white matter microstructure predict cognitive function at 1 and 2 y

Lee

Steiner

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Previous studies indicate that the microstructure of individual white matter (WM) tracts is related to cognitive function. More recent studies indicate that the microstructure of individual tracts is highly correlated and that a property common across WM is related to overall cognitive function in adults. However, little is known about whether these common WM properties exist in early childhood development or how they are related to cognitive development. In this study, we used diffusion tensor imaging (DTI) to investigate common underlying factors in 12 fiber tracts, their relationship with cognitive function, and their heritability in a longitudinal sample of healthy children at birth (n = 535), 1 y (n = 322), and 2 y (n = 244) of age. Our data show that, in neonates, there is a highly significant correlation between major WM tracts that decreases from birth to 2 y of age. Over the same period, the factor structure increases in complexity, from one factor at birth to three factors at age 2 y, which explain 50% of variance. The identified common factors of DTI metrics in each age group are significantly correlated with general cognitive scores and predict cognitive ability in later childhood. These factors are moderately heritable. These findings illustrate the anatomical differentiation of WM fiber from birth to 2 y of age that correlate with cognitive development. Our results also suggest that the common factor approach is an informative way to study WM development and its relationship with cognition and is a useful approach for future imaging genetic studies.factor analysis | white matter | DTI | cognition | heritability I ndividual differences in white matter (WM) microstructure have been shown to be related to cognitive functions in adults (1, 2) and children (3-5). Diffusion tensor imaging (DTI) parameters in different WM regions or tracts are variably associated with processing speed, general intelligence, and higher-order cognitive abilities (1-5).To date, the majority of DTI studies of WM microstructure have analyzed each tract or WM region independently. However, more recent studies clearly demonstrate that fractional anisotropy (FA) and other DTI metrics of WM microstructure correlate highly across WM tracts and across a wide age range, including neonates, older children, and adults (6-8). Furthermore, studies indicate that a general factor derived by principal component analyses (PCA) describing common variance in FA across WM tracts is associated with information processing speed (9) as well as general intelligence (10), at least in older adults. In children ages 8 to 16 y, three principal components of FA have been identified; the one corresponding to the corpus callosum was significantly related to IQ (11).The identification of one or more common WM factors that influence variation in multiple WM tracts or regions is important for several reasons. Such factors could prove to be a good target for imaging genetic studies, as one would expect genetic variants influencing WM development and function ...

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Section: Discussionmentioning

confidence: 99%

Common and heritable components of white matter microstructure predict cognitive function at 1 and 2 y

Lee

Steiner

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…CN subjects were recruited via print and electronic media, and noticeboards in universities and local communities. Sample ascertainment and separate clinical assessments for younger (15)(16)(17)(18)(19)(20)(21) and older (22)(23)(24)(25)(26)(27)(28)(29)(30) age categories are provided in Perich et al 36 and in Supplementary Information 1.1. We pooled data across both age cohorts unless there was a specific correlation between a brain network measure and an age-specific clinical variable in either group.…”

Section: Materials and Methods Participantsmentioning

confidence: 99%

“…15 Given the strong heritability of WM morphology, 16,17 studying individuals at familial risk of developing BD may help identify neurobiological factors that pre-empt the development of BD, as well as factors associated with resilience. This objective is particularly pertinent in young first-degree relatives who have not yet passed the peak age of illness onset.…”

Section: Introductionmentioning

confidence: 99%

784. Structural Dysconnectivity of Key Cognitive and Emotional Hubs in Young People at High Genetic Risk for Bipolar Disorder

Mitchell

Breakspear

Roberts

et al. 2017

Biological Psychiatry

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“…ASPM is located on chromosome 1, band q31 (1q31). Defective forms of the ASPM gene are associated with autosomal recessive primary microcephaly, this gene is expressed in the primary sites of prenatal cerebral cortical neurogenesis (YOUSEM, YASSA, CRISTINZIO et al, 2009;CHIANG, BARYSHEVA, MCMAHON et al, 2012). Chiang MC et al found 24 polymorphisms in 6 genes considered responsible for the integrity of the white matter, with direct correlation to the cognitive performance.…”

Section: Genetic Influence In the Global Intelligencementioning

confidence: 99%

“…Chiang MC et al found 24 polymorphisms in 6 genes considered responsible for the integrity of the white matter, with direct correlation to the cognitive performance. These genes are related to the maintenance of synaptic integrity of the white matter, activity of neuronal plasmatic membrane and maintenance of sodium channels (CHIANG, BARYSHEVA, MCMAHON et al, 2012).…”

Section: Genetic Influence In the Global Intelligencementioning

confidence: 99%

Intelligence neurocircuitry: cortical and subcortical structures

2017

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The authors describe the brain regions involved in the process of intelligence using as a basis, the models of the theory of frontoparietal integration (P-FIT Model). They also correlate the model described with functional areas of Brodmann, integrating them into the tertiary brain areas and address the subcortical structures involved in cognitive processes, including the memory. The studies performed by functional magnetic resonance, also unmask various regions related with intelligence, neither previously described by Brodmann nor even in conventional models of learning. The anterior insular cortex presents itself as the most recent tertiary area to be considered. Subcortical structures, when injured, mimick injuries to the cerebral cortex, demonstrating their great participation in cognition. The topographies of aphasia and the functioning mechanisms of the bearers of learning disorders, including dyslexic, dysgraphia and dyscalculic should be reconsidered. A better understanding of this topographic anatomy may clarify the mechanisms used in those individuals with cerebral lesions.

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Gene Network Effects on Brain Microstructure and Intellectual Performance Identified in 472 Twins

Cited by 54 publications

References 75 publications

Common and heritable components of white matter microstructure predict cognitive function at 1 and 2 y

Common and heritable components of white matter microstructure predict cognitive function at 1 and 2 y

784. Structural Dysconnectivity of Key Cognitive and Emotional Hubs in Young People at High Genetic Risk for Bipolar Disorder

Intelligence neurocircuitry: cortical and subcortical structures

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