HCP Young Adult

Components of the Human Connectome Project - Behavioral Testing

The primary goal of the Human Connectome Project is to understand the typical patterns of structural and functional connectivity in the healthy adult human brain. However, as we attempt to define "typical," we know that there are important individual differences in such patterns of connectivity even among persons with no diagnosable neurological or psychiatric disorders. One clue as to the importance of these individual differences may lie in their relationship to behavior.  

Why is behavioral testing useful?

There is increasing evidence that these individual differences in brain connectivity are associated with variability in important cognitive and behavioral functions that constrain real world function. For example, higher IQ among healthy adults is associated with more effective and more efficient connectivity in the human brain. As another example, developmental research suggests that maturation of functional and structural networks in the human brain contributes to improvements in cognitive and emotional function as children grow older. Thus, measuring behavior while also mapping these structural and functional networks in our participants provides an important set of data that can be used to help understand variations in "typical" brain function.

How will we use the behavioral data?

The data collected on healthy adults in the HCP will provide an invaluable starting point for future studies that examine how variation in human structural and functional connectivity play a role in both adult and child neurological and psychiatric disorders. These disorders represent an enormous public health burden and a huge economic cost (e.g., estimated $320 billion annually in the U.S.).

An extensive empirical literature demonstrates impairments in both structural and functional connectivity in psychiatric disorders such as autism, schizophrenia, bipolar disorder, and addiction; neurological disorders such as Tourette syndrome and multiple sclerosis; and the cognitive consequences of prematurity. The data collected in the HCP will allow us to understand how variation in human brain connectivity relates to variation in behavior, memory, thinking and emotion. In turn, this will provide clues as to how impairments in brain connectivity contribute to the cognitive, emotional and behavioral symptoms that define these psychiatric and neurological illnesses.

How will we measure behavior?

To better understand the relationship between brain connectivity and behavior, we will use a reliable and well-validated battery of measures that assess a wide range of human functions. The core of our battery is comprised of the tools and methods developed by the NIH Toolbox for Assessment of Neurological and Behavioral function.  This Toolbox, funded by the NIH Blueprint, provides an efficient and comprehensive battery of assessment tools for projects such as the Human Connectome Project. 

The NIH Toolbox includes measures of cognitive, emotional, motor and sensory processes in healthy individuals.  These measures were selected using a consensus building process, and were developed and validated using state-of-the-art assessment methodologies, including item response theory (IRT) and Computer Adaptive Testing (CAT). 

For the HCP, we are adding measures of five important areas not fully covered by the Toolbox:

  1. Additional measures of visual processing;
  2. Personality and adaptive function;
  3. Delay discounting (as a measure of self-regulation and neuroeconomic decision making);
  4. Fluid intelligence (as a measure of higher order relational reasoning); and
  5. Behavioral measures of emotion processing.

Where possible, we are making efforts to harmonize our behavioral data collection with the methods being used by other large scale imaging acquisition efforts.  

Further Reading:

  1. Bassett, D. S., Bullmore, E. T., Meyer-Lindenberg, A., Apud, J. A., Weinberger, D. R., & Coppola, R.Cognitive fitness of cost-efficient brain functional networks. Proc Natl Acad Sci U S A. 2009; 106(28), 11747-11752.
  2. Calhoun, V. D., Eichele, T., & Pearlson, G. Functional brain networks in schizophrenia: A review. Front Hum Neurosci. 2009; 3, 17.
  3. Cheung, C., Chua, S. E., Cheung, V., Khong, P. L., Tai, K. S., Wong, T. K., et al. White matter fractional anisotrophy differences and correlates of diagnostic symptoms in autism. J Child Psychol Psychiatry. 2009; 50(9), 1102-1112.
  4. Church, J. A., Fair, D. A., Dosenbach, N. U., Cohen, A. L., Miezin, F. M., Petersen, S. E., et al. Control networks in paediatric tourette syndrome show immature and anomalous patterns of functional connectivity. Brain. 2009; 132(Pt 1), 225-238.
  5. Coben, R., Clarke, A. R., Hudspeth, W., & Barry, R. J. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol. 2008; 119(5), 1002-1009.
  6. Constable, R. T., Ment, L. R., Vohr, B. R., Kesler, S. R., Fulbright, R. K., Lacadie, C., et al. Prematurely born children demonstrate white matter microstructural differences at 12 years of age, relative to term control subjects: An investigation of group and gender effects. Pediatrics. 2008; 121(2), 306-316.
  7. Fair, D. A., Cohen, A. L., Church, J. A., Miezin, F. M., Barch, D., Raichle, M. E., et al. The maturing architecture of the brain's default network. Proceedings of the National Academy of Sciences. 2008; 105, 4028-4035.
  8. Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U., Church, J. A., Miezin, F. M., et al. Functional brain networks develop from a "Local to distributed" Organization. PLoS Comput Biol. 2009; 5(5), e1000381.
  9. Fair, D. A., Dosenbach, N. U., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., et al. (2007). Development of distinct control networks through segregation and integration. Proceedings of the National Academy of Sciences. 2007; 104(33), 13507-13512.
  10. Gozzo, Y., Vohr, B., Lacadie, C., Hampson, M., Katz, K. H., Maller-Kesselman, J., et al. Alterations in neural connectivity in preterm children at school age. Neuroimage. 2009; 48(2), 458-463.
  11. He, Y., Dagher, A., Chen, Z., Charil, A., Zijdenbos, A., Worsley, K., et al. Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain. 2009 Dec;132(Pt 12):3366-79.
  12. Insel, T. R. Assessing the economic costs of serious mental illness. American Journal of Psychiatry.2008; 165, 663-665.
  13. Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., et al. Brain anatomical network and intelligence. PLoS Comput Biol. 2009; 5(5), e1000395.
  14. Liu, J., Liang, J., Qin, W., Tian, J., Yuan, K., Bai, L., et al. Dysfunctional connectivity patterns in chronic heroin users: An fMRI study. Neurosci Lett. 2009; 460(1), 72-77.
  15. Ma, N., Liu, Y., Li, N., Wang, C. X., Zhang, H., Jiang, X. F., et al. Addiction related alteration in resting-state brain connectivity. Neuroimage. 2010 Jan 1;49(1):738-44.
  16. Rich, B. A., Fromm, S. J., Berghorst, L. H., Dickstein, D. P., Brotman, M. A., Pine, D. S., et al. Neural connectivity in children with bipolar disorder: Impairment in the face emotion processing circuit. J Child Psychol Psychiatry. 2008; 49(1), 88-96.
  17. Rocca, M. A., Absinta, M., Valsasina, P., Ciccarelli, O., Marino, S., Rovira, A., et al. Abnormal connectivity of the sensorimotor network in patients with MS: A multicenter fMRI study. Hum Brain Mapp. 2009; 30(8), 2412-2425.
  18. Sahyoun, C. P., Belliveau, J. W., Soulieres, I., Schwartz, S., & Mody, M. Neuroimaging of the functional and structural networks underlying visuospatial vs. Linguistic reasoning in high-functioning autism. Neuropsychologia. 2010 Jan;48(1):86-95 
  19. Schafer, R. J., Lacadie, C., Vohr, B., Kesler, S. R., Katz, K. H., Schneider, K. C., et al. Alterations in functional connectivity for language in prematurely born adolescents. Brain. 2009; 132(3), 661-670.
  20. Solomon, M., Ozonoff, S. J., Ursu, S., Ravizza, S., Cummings, N., Ly, S., et al. The neural substrates of cognitive control deficits in autism spectrum disorders. Neuropsychologia. 2009; 47(12), 2515-2526.
  21. Stevens, M. C., Skudlarski, P., Pearlson, G. D., & Calhoun, V. D. Age-related cognitive gains are mediated by the effects of white matter development on brain network integration. Neuroimage. 2009; 48(4), 738-746.
  22. White, T., Kendi, A. T., Lehericy, S., Kendi, M., Karatekin, C., Guimaraes, A., et al. Disruption of hippocampal connectivity in children and adolescents with schizophrenia--a voxel-based diffusion tensor imaging study. Schizophr Res. 2007;90(1-3), 302-307.
  23. Whitfield-Gabrieli, S., Thermenos, H. W., Milanovic, S., Tsuang, M. T., Faraone, S. V., McCarley, R. W., et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A. 2009; 106(4), 1279-1284.

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The Human Connectome Project and Connectome Coordination Facility are funded by the National Institutes of Health, and all information in this site is available to the public domain. No Protected Health Information has been published on this site. Last updated 05/19/2016 15:50:08.