Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI

By Jason P. Lerch, Keith J. Worsley, W. Philip Shaw, Deanna K. Greenstein, Rhoshel K. Lenroot, Jay N. Giedd, Alan C. Evans. NeuroImage, 2006

540 citations recorded by the source corpus.

We introduce MACACC-Mapping Anatomical Correlations Across Cerebral Cortex-to study correlated changes within and across different cortical networks. The principal topic of investigation is whether the thickness of one area of the cortex changes in a statistically correlated fashion with changes in thickness of other cortical regions. We further extend these methods by introducing techniques to test whether different population groupings exhibit significantly varying MACACC patterns. The methods are described in detail and applied to a normal childhood development population (n = 292), and show that association cortices have the highest correlation strengths. Taking Brodmann Area (BA) 44 as a seed region revealed MACACC patterns strikingly similar to tractography maps obtained from diffusion tensor imaging. Furthermore, the MACACC map of BA 44 changed with age, older subjects featuring tighter correlations with BA 44 in the anterior portions of the superior temporal gyri. Lastly, IQ-dependent MACACC differences were investigated, revealing steeper correlations between BA 44 and multiple frontal and parietal regions for the higher IQ group, most significantly (t = 4.0) in the anterior cingulate.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/03adca66d741d9194aeb228bb478e0e49e7f0f2e

How researchers see this paper

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In Cognitive Neuroscience

1. Yong He
2. E. Bullmore
3. Alan C. Evans
4. D. Bassett
5. Olaf Sporns
6. Jinhui Wang
7. Gaolang Gong
8. Zhang John Chen

In Radiology, Nuclear Medicine and Imaging

1. Yong He
2. Alan C. Evans
3. E. Bullmore
4. D. Bassett
5. Olaf Sporns
6. Gaolang Gong
7. Zhang John Chen
8. Jinhui Wang

Citations in the corpus

• Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain (2013, methodology): “Structural hubs with higher SCS were mainly distributed in the PCC/PCu, mPFC, anterior cingulate cortex (ACC), medial temporal lobe, and lateral temporal and parietal cortices that comprise the DMN, insula, lateral-prefrontal cortex (lPFC), and visual cortex (Fig.”
• Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory (2008, unknown): “This is based on studies suggesting that interregional statistical associations in cortical thickness provide important connectivity information (Worsley et al., 2005; Lerch et al., 2006).”
• Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “Table 1 indicates that an ideal node definition for the human connectome should define functionally homogeneous nodes, represent functional heterogeneity across nodes, and account for spatial relationships.”
• Structural connectomics in brain diseases (2013, unknown): “Such connectivity can be quantified by measuring statistical dependence (correlation) of imaging morphometric features between cortical areas (Lerch et al., 2006).”
• Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, methodology): “For example, it has been assumed that reciprocal afferent connections have a mutually trophic effect on the growth and maintenance of both connected regions leading to positively correlated volumes in adult brains [13,62].”
• Brain graphs: graphical models of the human brain connectome. (2011, methodology): “The correlation or partial correlation of these regional morphometric variables over subjects provides an association matrix (right), which can be used as a measure of anatomical connectivity (Lerch et al. 2006). www.annualreviews.org • Brain Graphs C-3 A nn u.”
• Neurodegenerative diseases target large-scale human brain networks (2009, background): “, 2008) and remodel itself (Kelly et al., 2008; Lerch et al., 2006) in response to core inherited projection map formation scripts (Ruthazer and Cline, 2004) that interact with the environment to drive individual differences in cognitive, emotional, and motor function (Seeley et al.”
• Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, methodology): “For instance, Lerch et al. (2006) showed that the pattern of thickness correlation for Brodmman area 44 is similar to the tractography map obtained from previous diffusion tensor imaging (DTI) studies.”
• Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, unknown): “areas [7]).”
• Models of the Aging Brain Structure and Individual Decline (2012, background): “EMERGENT PROPERTIES IN REGIONAL PATTERNS Recent studies have emphasized that there is a consistent pattern of inter-regional covariance of brain structure (Mechelli et al., 2005; Lerch et al., 2006; Colibazzi et al., 2008; Nosarti et al., 2010; Eyler et al., 2011).”
• Networks of anatomical covariance (2013, background): “…et al., 2008, 2011a, 2011b; Gong et al., 2009b, 2011, 2012; He and Evans, 2010; He et al., 2007, 2008, 2009a, 2009b; Khundrakpam et al., in press; Lerch et al., 2006; Lo et al., 2011; Mechelli et al., 2005; Raj et al., 2012; Ramnani et al., 2004; Raznahan et al., 2011a; Reid and Evans, 2013;…”
• Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease (2008, background): “Previous work from our laboratory has demonstrated that interregional coordinated variations in cortical thickness are approximately associated with the known neuroanatomical pathways in the human brain (Lerch et al., 2006; He et al., 2007b).”
• Microstructural maturation of the human brain from childhood to adulthood (2008, background): “Studies of cortical gray matter development have shown regional patterns of brain maturation, with distinct areas developing at different rates (Sowell et al., 2004; Lerch et al., 2006; Whitford et al., 2007).”
• Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia (2008, background): “Similarly, correlations between left inferior frontal and superior temporal regions in cortical thickness measurements were validated previously as indirect markers of anatomical connectivity by direct comparison with DTI measurements of the arcuate fasciculus (Lerch et al., 2006).”
• GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, result): “Previous study revealed that some of the tractography maps obtained from diffusion tensor imaging are strikingly similar to pattern of correlations in cortical thickness [44].”