Hostname: page-component-7c8c6479df-5xszh Total loading time: 0 Render date: 2024-03-28T07:01:11.639Z Has data issue: false hasContentIssue false

Imaging the “At-Risk” Brain: Future Directions

Published online by Cambridge University Press:  18 February 2016

Maki S. Koyama
Affiliation:
Child Mind Institute, New York, New York Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
Adriana Di Martino
Affiliation:
The Child Study Center at NYU Langone Medical Center, New York, New York
Francisco X. Castellanos
Affiliation:
Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York The Child Study Center at NYU Langone Medical Center, New York, New York
Erica J. Ho
Affiliation:
Child Mind Institute, New York, New York
Enitan Marcelle
Affiliation:
Child Mind Institute, New York, New York
Bennett Leventhal
Affiliation:
Department of PsychiatryUniversity of California–San Francisco, San Francisco, California
Michael P. Milham*
Affiliation:
Child Mind Institute, New York, New York Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
*
Correspondence and reprint requests to: Michale P. Milham, Child Mind Institute, Nathan S. Kline Institute for Psychiatric Research, 455 Park Avenue, New York, New York 10022. E-mail: michael.milham@childmind.org

Abstract

Objectives: Clinical neuroscience is increasingly turning to imaging the human brain for answers to a range of questions and challenges. To date, the majority of studies have focused on the neural basis of current psychiatric symptoms, which can facilitate the identification of neurobiological markers for diagnosis. However, the increasing availability and feasibility of using imaging modalities, such as diffusion imaging and resting-state fMRI, enable longitudinal mapping of brain development. This shift in the field is opening the possibility of identifying predictive markers of risk or prognosis, and also represents a critical missing element for efforts to promote personalized or individualized medicine in psychiatry (i.e., stratified psychiatry). Methods: The present work provides a selective review of potentially high-yield populations for longitudinal examination with MRI, based upon our understanding of risk from epidemiologic studies and initial MRI findings. Results: Our discussion is organized into three topic areas: (1) practical considerations for establishing temporal precedence in psychiatric research; (2) readiness of the field for conducting longitudinal MRI, particularly for neurodevelopmental questions; and (3) illustrations of high-yield populations and time windows for examination that can be used to rapidly generate meaningful and useful data. Particular emphasis is placed on the implementation of time-appropriate, developmentally informed longitudinal designs, capable of facilitating the identification of biomarkers predictive of risk and prognosis. Conclusions: Strategic longitudinal examination of the brain at-risk has the potential to bring the concepts of early intervention and prevention to psychiatry. (JINS, 2016, 22, 164–179)

Type
Critical Reviews
Copyright
Copyright © The International Neuropsychological Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aarnoudse-Moens, C.S., Weisglas-Kuperus, N., van Goudoever, J.B., & Oosterlaan, J. (2009). Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics, 124(2), 717728. doi:10.1542/peds.2008-2816 CrossRefGoogle ScholarPubMed
Alexander, A.L., Hurley, S.A., Samsonov, A.A., Adluru, N., Hosseinbor, A.P., Mossahebi, P.,& Field, A.S. (2011). Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connectivity, 1(6), 423446. doi:10.1089/brain.2011.0071 Google Scholar
Allin, M., Walshe, M., Fern, A., Nosarti, C., Cuddy, M., Rifkin, L., & Wyatt, J. (2008). Cognitive maturation in preterm and term born adolescents. Journal of Neurology, Neurosurgery, & Psychiatry, 79(4), 381386. doi:10.1136/jnnp.2006.110858 Google Scholar
Almli, C.R., Rivkin, M.J., & McKinstry, R.C., & Brain Development Cooperative, Group. (2007). The NIH MRI study of normal brain development (Objective-2): Newborns, infants, toddlers, and preschoolers. Neuroimage, 35(1), 308325. doi:10.1016/j.neuroimage.2006.08.058 CrossRefGoogle ScholarPubMed
Ameis, S.H., & Catani, M. (2015). Altered white matter connectivity as a neural substrate for social impairment in Autism Spectrum Disorder. Cortex, 62, 158181. doi:10.1016/j.cortex.2014.10.014 CrossRefGoogle ScholarPubMed
Anderson, A.L., & Thomason, M.E. (2013). Functional plasticity before the cradle: A review of neural functional imaging in the human fetus. Neuroscience & Biobehavioral Reviews, 37(9 Pt B), 22202232. doi:10.1016/j.neubiorev.2013.03.013 CrossRefGoogle ScholarPubMed
Anderson, D.K., Lord, C., Risi, S., DiLavore, P.S., Shulman, C., Thurm, A., & Pickles, A. (2007). Patterns of growth in verbal abilities among children with autism spectrum disorder. Journal of Consulting and Clinical Psychology, 75(4), 594604. doi:10.1037/0022-006X.75.4.594 Google Scholar
Aylward, E.H., Richards, T.L., Berninger, V.W., Nagy, W.E., Field, K.M., Grimme, A.C., & Cramer, S.C. (2003). Instructional treatment associated with changes in brain activation in children with dyslexia. Neurology, 61(2), 212219.CrossRefGoogle ScholarPubMed
Baghdadli, A., Picot, M.C., Michelon, C., Bodet, J., Pernon, E., Burstezjn, C., & Aussilloux, C. (2007). What happens to children with PDD when they grow up? Prospective follow-up of 219 children from preschool age to mid-childhood. Acta Psychiatrica Scandinavica, 115(5), 403412. doi:10.1111/j.1600-0447.2006.00898.x CrossRefGoogle ScholarPubMed
Bal, V.H., Kim, S.H., Cheong, D., & Lord, C. (2015). Daily living skills in individuals with autism spectrum disorder from 2 to 21 years of age. Autism, 19(7), 774784. doi:10.1177/1362361315575840 CrossRefGoogle Scholar
Barquero, L.A., Davis, N., & Cutting, L.E. (2014). Neuroimaging of reading intervention: A systematic review and activation likelihood estimate meta-analysis. PLoS One, 9(1), e83668. doi:10.1371/journal.pone.0083668 CrossRefGoogle ScholarPubMed
Barre, N., Morgan, A., Doyle, L.W., & Anderson, P.J. (2011). Language abilities in children who were very preterm and/or very low birth weight: A meta-analysis. Journal of Pediatrics, 158(5), 766774 e761. doi:10.1016/j.jpeds.2010.10.032 CrossRefGoogle ScholarPubMed
Barttfeld, P., Uhrig, L., Sitt, J.D., Sigman, M., Jarraya, B., & Dehaene, S. (2015). Signature of consciousness in the dynamics of resting-state brain activity. Proceedings of the National Academy of Sciences of the United States of America, 112(3), 887892. doi:10.1073/pnas.1418031112 CrossRefGoogle ScholarPubMed
Bartzokis, G. (2004). Quadratic trajectories of brain myelin content: Unifying construct for neuropsychiatric disorders. Neurobiology of Aging, 25(1), 4962. doi:http://dx.doi.org/10.1016/j.neurobiolaging.2003.08.001 CrossRefGoogle Scholar
Basser, P.J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance, Series B, 111(3), 209219.CrossRefGoogle ScholarPubMed
Beesdo, K., Pine, D.S., Lieb, R., & Wittchen, H.U. (2010). Incidence and risk patterns of anxiety and depressive disorders and categorization of generalized anxiety disorder. Archives of General Psychiatry, 67(1), 4757. doi:10.1001/archgenpsychiatry.2009.177 CrossRefGoogle ScholarPubMed
Behrman, R.E., & Butler, A.S. (2007). Preterm birth: Causes, consequences, and prevention. Washington, DC: National Academies Press.Google Scholar
Billstedt, E., Gillberg, I.C., & Gillberg, C. (2005). Autism after adolescence: Population-based 13- to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. Journal of Autism and Developmental Disorders, 35(3), 351360.CrossRefGoogle ScholarPubMed
Blasi, A., Lloyd-Fox, S., Sethna, V., Brammer, M.J., Mercure, E., Murray, L., & Johnson, M.H. (2015). Atypical processing of voice sounds in infants at risk for autism spectrum disorder. Cortex, 71, 122133. doi:10.1016/j.cortex.2015.06.015 CrossRefGoogle ScholarPubMed
Bora, E., Harrison, B.J., Davey, C.G., Yucel, M., & Pantelis, C. (2012). Meta-analysis of volumetric abnormalities in cortico-striatal-pallidal-thalamic circuits in major depressive disorder. Psychological Medicine, 42(4), 671681. doi:10.1017/S0033291711001668 Google Scholar
Botteron, K.N., Raichle, M.E., Drevets, W.C., Heath, A.C., & Todd, R.D. (2002). Volumetric reduction in left subgenual prefrontal cortex in early onset depression. Biological Psychiatry, 51(4), 342344.CrossRefGoogle ScholarPubMed
Bremner, J.D. (2004). Brain imaging in anxiety disorders. Expert Review of Neurotherapeutics, 4(2), 275284. doi:10.1586/14737175.4.2.275 CrossRefGoogle ScholarPubMed
Brown, C.J., Miller, S.P., Booth, B.G., Andrews, S., Chau, V., Poskitt, K.J., & Hamarneh, G. (2014). Structural network analysis of brain development in young preterm neonates. Neuroimage, 101, 667680. doi:10.1016/j.neuroimage.2014.07.030 Google Scholar
Brown, I.S., & Felton, R.H. (1999). Effects of instruction on beginning reading skills in children at risk for reading disability. Reading and Writing: An Interdisciplinary Journal, 2, 223241.CrossRefGoogle Scholar
Bruhl, A.B., Delsignore, A., Komossa, K., & Weidt, S. (2014). Neuroimaging in social anxiety disorder-a meta-analytic review resulting in a new neurofunctional model. Neuroscience and Biobehavioral Reviews, 47, 260280. doi:10.1016/j.neubiorev.2014.08.003 CrossRefGoogle Scholar
Buckholtz, J.W., & Meyer-Lindenberg, A. (2012). Psychopathology and the human connectome: Toward a transdiagnostic model of risk for mental illness. Neuron, 74(6), 9901004. doi:10.1016/j.neuron.2012.06.002 CrossRefGoogle Scholar
Burmeister, M., McInnis, M.G., & Zollner, S. (2008). Psychiatric genetics: Progress amid controversy. Nature Reviews. Genetics, 9(7), 527540. doi:10.1038/nrg2381 CrossRefGoogle ScholarPubMed
Buss, C., Davis, E.P., Shahbaba, B., Pruessner, J.C., Head, K., & Sandman, C.A. (2012). Maternal cortisol over the course of pregnancy and subsequent child amygdala and hippocampus volumes and affective problems. Proceedings of the National Academy of Sciences of the United States of America, 109(20), E1312E1319. doi:10.1073/pnas.1201295109 Google ScholarPubMed
Buss, C., Entringer, S., Swanson, J.M., & Wadhwa, P.D. (2012). The role of stress in brain development: The gestational environment’s long-term effects on the brain. Cerebrum, 2012, 4.Google Scholar
Carballedo, A., Scheuerecker, J., Meisenzahl, E., Schoepf, V., Bokde, A., Moller, H.J., & Frodl, T. (2011). Functional connectivity of emotional processing in depression. Journal of Affective Disorders, 134(1–3), 272279. doi:10.1016/j.jad.2011.06.021 Google Scholar
Carmody, D.P., Bendersky, M., Dunn, S.M., DeMarco, J.K., Hegyi, T., Hiatt, M., & Lewis, M. (2006). Early risk, attention, and brain activation in adolescents born preterm. Child Development, 77(2), 384394. doi:10.1111/j.1467-8624.2006.00877.x Google Scholar
Caspi, A., & Moffitt, T.E. (2006). Gene-environment interactions in psychiatry: Joining forces with neuroscience. Nature Reviews Neuroscience, 7(7), 583590. doi:10.1038/nrn1925 Google Scholar
Castellanos, F.X., Di Martino, A., Craddock, R.C., Mehta, A.D., & Milham, M.P. (2013). Clinical applications of the functional connectome. Neuroimage, 80, 527540. doi:10.1016/j.neuroimage.2013.04.083 Google Scholar
Colvert, E., Tick, B., McEwen, F., Stewart, C., Curran, S.R., Woodhouse, E., & Bolton, P. (2015). Heritability of Autism Spectrum Disorder in a UK Population-Based Twin Sample. The Journal of the American Medical Association, Psychiatry, 72(5), 415423. doi:10.1001/jamapsychiatry.2014.3028 Google Scholar
Constable, R.T., Ment, L.R., Vohr, B.R., Kesler, S.R., Fulbright, R.K., Lacadie, C., & Reiss, A.R. (2008). 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, 121(2), 306316. doi:10.1542/peds.2007-0414 CrossRefGoogle ScholarPubMed
Costa e Silva, J.A. (2013). Personalized medicine in psychiatry: New technologies and approaches. Metabolism, 62(Suppl. 1), S40S44. doi:10.1016/j.metabol.2012.08.017 CrossRefGoogle Scholar
Counsell, S.J., Edwards, A.D., Chew, A.T., Anjari, M., Dyet, L.E., Srinivasan, L., & Cowan, F.M. (2008). Specific relations between neurodevelopmental abilities and white matter microstructure in children born preterm. Brain, 131(Pt 12), 32013208. doi:10.1093/brain/awn268 Google Scholar
Cross-Disorder Group of the Psychiatric Genomics, C., Lee, S.H., Ripke, S., Neale, B.M., Faraone, S.V., Purcell, S.M., … Wray, N.R. (2013). Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nature Genetics, 45(9), 984994. doi:10.1038/ng.2711 Google Scholar
Damaraju, E., Phillips, J.R., Lowe, J.R., Ohls, R., Calhoun, V.D., & Caprihan, A. (2010). Resting-state functional connectivity differences in premature children. Frontiers in Systems Neuroscience, 4, pii:23. doi:10.3389/fnsys.2010.00023 Google ScholarPubMed
Davis, E.P., & Sandman, C.A. (2010). The timing of prenatal exposure to maternal cortisol and psychosocial stress is associated with human infant cognitive development. Child Development, 81(1), 131148. doi:10.1111/j.1467-8624.2009.01385.x Google Scholar
Dawson, G., Ashman, S.B., & Carver, L.J. (2000). The role of early experience in shaping behavioral and brain development and its implications for social policy. Development and Psychopathology, 12(4), 695712.Google Scholar
Degnan, A.J., Wisnowski, J.L., Choi, S., Ceschin, R., Bhushan, C., Leahy, R.M., & Panigrahy, A. (2015). Altered structural and functional connectivity in late preterm preadolescence: An anatomic seed-based study of resting state networks related to the posteromedial and lateral parietal cortex. PLoS One, 10(6), e0130686. doi:10.1371/journal.pone.0130686 Google Scholar
Deoni, S.C., Dean, D.C. III, Remer, J., Dirks, H., & O’Muircheartaigh, J. (2015). Cortical maturation and myelination in healthy toddlers and young children. Neuroimage, 115, 147161. doi:10.1016/j.neuroimage.2015.04.058 CrossRefGoogle ScholarPubMed
Di Martino, A., Fair, D.A., Kelly, C., Satterthwaite, T.D., Castellanos, F.X., Thomason, M.E., & Milham, M.P. (2014). Unraveling the miswired connectome: A developmental perspective. Neuron, 83(6), 13351353. doi:10.1016/j.neuron.2014.08.050 CrossRefGoogle ScholarPubMed
Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., & Milham, M.P. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659667. doi:10.1038/mp.2013.78 CrossRefGoogle ScholarPubMed
Drevets, W.C., Savitz, J., & Trimble, M. (2008). The subgenual anterior cingulate cortex in mood disorders. CNS Spectrums, 13(8), 663681.Google Scholar
Dubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Huppi, P.S., & Hertz-Pannier, L. (2014). The early development of brain white matter: A review of imaging studies in fetuses, newborns and infants. Neuroscience, 276, 4871. doi:10.1016/j.neuroscience.2013.12.044 Google Scholar
Duerden, E.G., Card, D., Lax, I.D., Donner, E.J., & Taylor, M.J. (2013). Alterations in frontostriatal pathways in children born very preterm. Developmental Medicine and Child Neurology, 55(10), 952958. doi:10.1111/dmcn.12198 CrossRefGoogle ScholarPubMed
Eckert, M.A., Leonard, C.M., Richards, T.L., Aylward, E.H., Thomson, J., & Berninger, V.W. (2003). Anatomical correlates of dyslexia: Frontal and cerebellar findings. Brain, 126(Pt 2), 482494.Google Scholar
Eden, G.F., Jones, K.M., Cappell, K., Gareau, L., Wood, F.B., Zeffiro, T.A., & Flowers, D.L. (2004). Neural changes following remediation in adult developmental dyslexia. Neuron, 44(3), 411422. doi:10.1016/j.neuron.2004.10.019 Google Scholar
Elison, J.T., Paterson, S.J., Wolff, J.J., Reznick, J.S., Sasson, N.J., Gu, H., & Network, I. (2013). White matter microstructure and atypical visual orienting in 7-month-olds at risk for autism. The American Journal of Psychiatry, 170(8), 899908. doi:10.1176/appi.ajp.2012.12091150 CrossRefGoogle ScholarPubMed
Eriksen, H.L., Kesmodel, U.S., Pedersen, L.H., & Mortensen, E.L. (2015). No association between prenatal exposure to psychotropics and intelligence at age five. Acta Obstetricia et Gynecologica Scandinavica, 94(5), 501507. doi:10.1111/aogs.12611 Google Scholar
Etkin, A., & Wager, T.D. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. The American Journal of Psychiatry, 164(10), 14761488. doi:10.1176/appi.ajp.2007.07030504 CrossRefGoogle ScholarPubMed
Fair, D.A., Posner, J., Nagel, B.J., Bathula, D., Dias, T.G., Mills, K.L., & Nigg, J.T. (2010). Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biological Psychiatry, 68(12), 10841091. doi:10.1016/j.biopsych.2010.07.003 Google Scholar
Feldman, H.M., Lee, E.S., Yeatman, J.D., & Yeom, K.W. (2012). Language and reading skills in school-aged children and adolescents born preterm are associated with white matter properties on diffusion tensor imaging. Neuropsychologia, 50(14), 33483362. doi:10.1016/j.neuropsychologia.2012.10.014 CrossRefGoogle ScholarPubMed
Ferrazzi, G., Kuklisova Murgasova, M., Arichi, T., Malamateniou, C., Fox, M.J., Makropoulos, A., & Hajnal, J.V. (2014). Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction. Neuroimage, 101, 555568. doi:10.1016/j.neuroimage.2014.06.074 Google Scholar
Finke, K., Neitzel, J., Bauml, J.G., Redel, P., Muller, H.J., Meng, C., & Sorg, C. (2015). Visual attention in preterm born adults: Specifically impaired attentional sub-mechanisms that link with altered intrinsic brain networks in a compensation-like mode. Neuroimage, 107, 95106. doi:10.1016/j.neuroimage.2014.11.062 Google Scholar
Finn, E.S., Shen, X., Scheinost, D., Rosenberg, M.D., Huang, J., Chun, M.M., & Constable, R.T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18, 16641671. doi:10.1038/nn.4135 CrossRefGoogle ScholarPubMed
Foland-Ross, L.C., Gilbert, B.L., Joormann, J., & Gotlib, I.H. (2015). Neural markers of familial risk for depression: An investigation of cortical thickness abnormalities in healthy adolescent daughters of mothers with recurrent depression. Journal of Abnormal Psychology, 124(3), 476485. doi:10.1037/abn0000050 Google Scholar
Foland-Ross, L.C., Hardin, M.G., & Gotlib, I.H. (2013). Neurobiological markers of familial risk for depression. Current Topics in Behavioral Neurosciences, 14, 181206. 10.1007/7854_2012_213 Google Scholar
Foland-Ross, L.C., Sacchet, M.D., Prasad, G., Gilbert, B., Thompson, P.M., & Gotlib, I.H. (2015). Cortical thickness predicts the first onset of major depression in adolescence. International Journal of Developmental Neuroscience, 46, 125131. doi:10.1016/j.ijdevneu.2015.07.007 CrossRefGoogle ScholarPubMed
Frye, R.E., Malmberg, B., Desouza, L., Swank, P., Smith, K., & Landry, S. (2009). Increased prefrontal activation in adolescents born prematurely at high risk during a reading task. Brain Research, 1303, 111119. doi:10.1016/j.brainres.2009.09.091 Google Scholar
Fukunaga, M., Horovitz, S.G., van Gelderen, P., de Zwart, J.A., Jansma, J.M., Ikonomidou, V.N., & Duyn, J.H. (2006). Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages. Magnetic Resonance Imaging, 24(8), 979992. doi:10.1016/j.mri.2006.04.018 Google Scholar
Gao, W., Alcauter, S., Elton, A., Hernandez-Castillo, C.R., Smith, J.K., Ramirez, J., & Lin, W. (2015). Functional network development during the first year: Relative sequence and socioeconomic correlations. Cerebral Cortex, 25(9), 29192928. doi:10.1093/cercor/bhu088 Google Scholar
Gaugler, T., Klei, L., Sanders, S.J., Bodea, C.A., Goldberg, A.P., Lee, A.B., & Buxbaum, J.D. (2014). Most genetic risk for autism resides with common variation. Nature Genetics, 46(8), 881885. doi:10.1038/ng.3039 Google Scholar
Georgiades, S., Szatmari, P., Boyle, M., Hanna, S., Duku, E., & Zwaigenbaum, L., … Pathways in ASD Study Team. (2013). Investigating phenotypic heterogeneity in children with autism spectrum disorder: A factor mixture modeling approach. Journal of Child Psychology and Psychiatry, 54(2), 206215. doi:10.1111/j.1469-7610.2012.02588.x CrossRefGoogle ScholarPubMed
Germano, E., Gagliano, A., & Curatolo, P. (2010). Comorbidity of ADHD and dyslexia. Developmental Neuropsychology, 35(5), 475493. doi:10.1080/87565641.2010.494748 CrossRefGoogle ScholarPubMed
Geschwind, D.H. (2009). Advances in autism. Annual Review of Medicine, 60, 367380. doi:10.1146/annurev.med.60.053107.121225 Google Scholar
Geschwind, N. (1965a). Disconnexion syndromes in animals and man. I. Brain, 88(2), 237294.Google Scholar
Geschwind, N. (1965b). Disconnexion syndromes in animals and man. II. Brain, 88(3), 585644.CrossRefGoogle ScholarPubMed
Glass, H.C., Costarino, A.T., Stayer, S.A., Brett, C.M., Cladis, F., & Davis, P.J. (2015). Outcomes for extremely premature infants. Anesthesia and Analgesia, 120(6), 13371351. doi:10.1213/ANE.0000000000000705 CrossRefGoogle ScholarPubMed
Glover, V. (2014). Maternal depression, anxiety and stress during pregnancy and child outcome; what needs to be done. Best Practice and Research: Clinical Obstetrics and Gynaecology, 28(1), 2535. doi:10.1016/j.bpobgyn.2013.08.017 Google Scholar
Gooch, D., Hulme, C., Nash, H.M., & Snowling, M.J. (2014). Comorbidities in preschool children at family risk of dyslexia. Journal of Child Psychology and Psychiatry, 55(3), 237246. doi:10.1111/jcpp.12139 Google Scholar
Hastings, R.S., Parsey, R.V., Oquendo, M.A., Arango, V., & Mann, J.J. (2004). Volumetric analysis of the prefrontal cortex, amygdala, and hippocampus in major depression. Neuropsychopharmacology, 29(5), 952959. doi:10.1038/sj.npp.1300371 Google Scholar
Hay, D.F., Pawlby, S., Waters, C.S., Perra, O., & Sharp, D. (2010). Mothers’ antenatal depression and their children’s antisocial outcomes. Child Development, 81(1), 149165. doi:10.1111/j.1467-8624.2009.01386.x Google Scholar
Hill, R.M., Pettit, J.W., Lewinsohn, P.M., Seeley, J.R., & Klein, D.N. (2014). Escalation to major depressive disorder among adolescents with subthreshold depressive symptoms: Evidence of distinct subgroups at risk. Journal of Affective Disorders, 158, 133138. doi:10.1016/j.jad.2014.02.011 CrossRefGoogle ScholarPubMed
Ho, T.C., Yang, G., Wu, J., Cassey, P., Brown, S.D., Hoang, N., & Yang, T.T. (2014). Functional connectivity of negative emotional processing in adolescent depression. Journal of Affective Disorders, 155, 6574. doi:10.1016/j.jad.2013.10.025 Google Scholar
Hoeft, F., McCandliss, B.D., Black, J.M., Gantman, A., Zakerani, N., Hulme, C., & Gabrieli, J.D. (2011). Neural systems predicting long-term outcome in dyslexia. Proceedings of the National Academy of Sciences of the United States of America, 108(1), 361366. doi:10.1073/pnas.1008950108 CrossRefGoogle ScholarPubMed
Holmes, A.J., Hollinshead, M.O., O’Keefe, T.M., Petrov, V.I., Fariello, G.R., Wald, L.L., & Buckner, R.L. (2015). Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Scientific Data, 2, 150031. doi:10.1038/sdata.2015.31 Google Scholar
Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with autism. Journal of Child Psychology and Psychiatry, 45(2), 212229.Google Scholar
Huizink, A.C., de Medina, P.G., Mulder, E.J., Visser, G.H., & Buitelaar, J.K. (2002). Psychological measures of prenatal stress as predictors of infant temperament. Journal of the American Academy of Child and Adolescent Psychiatry, 41(9), 10781085.Google Scholar
Jakab, A., Kasprian, G., Schwartz, E., Gruber, G.M., Mitter, C., Prayer, D., & Langs, G. (2015). Disrupted developmental organization of the structural connectome in fetuses with corpus callosum agenesis. Neuroimage, 111, 277288. doi:10.1016/j.neuroimage.2015.02.038 Google Scholar
Jakab, A., Schwartz, E., Kasprian, G., Gruber, G.M., Prayer, D., Schopf, V., & Langs, G. (2014). Fetal functional imaging portrays heterogeneous development of emerging human brain networks. Frontiers in Human Neuroscience, 8, 852. doi:10.3389/fnhum.2014.00852 CrossRefGoogle ScholarPubMed
Jernigan, T.L., Brown, T.T., Hagler, D.J. Jr., Akshoomoff, N., Bartsch, H., & Newman, E., … Pediatric Imaging Neurocognition and Genetics Study. (2015). The Pediatric Imaging, Neurocognition, and Genetics (PING) data repository. Neuroimage, 124, 11491154. doi:10.1016/j.neuroimage.2015.04.057 Google Scholar
Jeste, S.S., & Geschwind, D.H. (2014). Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nature Reviews. Neurology, 10(2), 7481. doi:10.1038/nrneurol.2013.278 Google Scholar
Johnson, S., & Marlow, N. (2011). Preterm birth and childhood psychiatric disorders. Pediatric Research, 69(5, Part 2 of 2), 11R18R.Google Scholar
Jovicich, J., Marizzoni, M., Bosch, B., Bartres-Faz, D., Arnold, J., & Benninghoff, J., … PharmaCog, Consortium. (2014). Multisite longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging of healthy elderly subjects. Neuroimage, 101, 390403. doi:10.1016/j.neuroimage.2014.06.075 Google Scholar
Kaiser, M. (2013). The potential of the human connectome as a biomarker of brain disease. Frontier in Human Neuroscience, 7, 484. doi: 10.3389/fnhum.2013.00484 Google ScholarPubMed
Kaiser, R.H., Andrews-Hanna, J.R., Wager, T.D., & Pizzagalli, D.A. (2015). Large-scale network dysfunction in major depressive disorder: A Meta-analysis of resting-state functional connectivity. Journal of the American Medical Association Psychiatry, 72(6), 603611. doi:10.1001/jamapsychiatry.2015.0071 Google ScholarPubMed
Kapur, S., Phillips, A.G., & Insel, T.R. (2012). Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular Psychiatry, 17(12), 11741179. doi:10.1038/mp.2012.105 CrossRefGoogle Scholar
Kasprian, G., Langs, G., Brugger, P.C., Bittner, M., Weber, M., Arantes, M., & Prayer, D. (2011). The prenatal origin of hemispheric asymmetry: An in utero neuroimaging study. Cerebral Cortex, 21(5), 10761083. doi:10.1093/cercor/bhq179 Google Scholar
Kelly, C., Biswal, B.B., Craddock, R.C., Castellanos, F.X., & Milham, M.P. (2012). Characterizing variation in the functional connectome: Promise and pitfalls. Trends in Cognitive Science, 16(3), 181188. doi:10.1016/j.tics.2012.02.001 Google Scholar
Kersbergen, K.J., Leemans, A., Groenendaal, F., van der Aa, N.E., Viergever, M.A., de Vries, L.S., & Benders, M.J. (2014). Microstructural brain development between 30 and 40 weeks corrected age in a longitudinal cohort of extremely preterm infants. Neuroimage, 103, 214224. doi:10.1016/j.neuroimage.2014.09.039 Google Scholar
Kessler, R.C., Avenevoli, S., & Merikangas, K.R. (2001). Mood disorders in children and adolescents: An epidemiologic perspective. Biological Psychiatry, 49(12), 10021014.CrossRefGoogle ScholarPubMed
Kingston, D., Tough, S., & Whitfield, H. (2012). Prenatal and postpartum maternal psychological distress and infant development: A systematic review. Child Psychiatry and Human Development, 43(5), 683714. doi:10.1007/s10578-012-0291-4 Google Scholar
Koyama, M.S., Di Martino, A., Kelly, C., Jutagir, D.R., Sunshine, J., Schwartz, S.J., & Milham, M.P. (2013). Cortical signatures of dyslexia and remediation: An intrinsic functional connectivity approach. PLoS One, 8(2), e55454. 10.1371/journal.pone.0055454 CrossRefGoogle ScholarPubMed
Krogsrud, S.K., Fjell, A.M., Tamnes, C.K., Grydeland, H., Mork, L., Due-Tonnessen, P., & Walhovd, K.B. (2015). Changes in white matter microstructure in the developing brain-A longitudinal diffusion tensor imaging study of children from 4 to 11years of age. Neuroimage, 124(Pt A), 473486. doi:10.1016/j.neuroimage.2015.09.017 Google Scholar
Kuhn, T., Gullett, J.M., Nguyen, P., Boutzoukas, A.E., Ford, A., Colon-Perez, L.M., & Bauer, R.M. (2015). Test-retest reliability of high angular resolution diffusion imaging acquisition within medial temporal lobe connections assessed via tract based spatial statistics, probabilistic tractography and a novel graph theory metric. Brain Imaging and Behavior. doi:10.1007/s11682-015-9425-1 Google Scholar
Kwon, S.H., Vasung, L., Ment, L.R., & Huppi, P.S. (2014). The role of neuroimaging in predicting neurodevelopmental outcomes of preterm neonates. Clinics in Perinatology, 41(1), 257283. doi:10.1016/j.clp.2013.10.003 Google Scholar
Limperopoulos, C., & Clouchoux, C. (2009). Advancing fetal brain MRI: Targets for the future. Seminars in Perinatology, 33(4), 289298. doi:10.1053/j.semperi.2009.04.002 Google Scholar
Liston, C., Chen, A.C., Zebley, B.D., Drysdale, A.T., Gordon, R., & Leuchter, B., Dubin, M.J. (2014). Default mode network mechanisms of transcranial magnetic stimulation in depression. Biological Psychiatry, 76(7), 517526. doi:10.1016/j.biopsych.2014.01.023 Google Scholar
Liu, J., Glenn, O.A., & Xu, D. (2014). Fast, free-breathing, in vivo fetal imaging using time-resolved 3D MRI technique: Preliminary results. Quantitative Imaging Medicine and Surgery, 4(2), 123128. doi:10.3978/j.issn.2223-4292.2014.04.08 Google Scholar
Loeffler, M., Engel, C., Ahnert, P., Alfermann, D., Arelin, K., Baber, R., & Thiery, J. (2015). The LIFE-Adult-Study: Objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BioMed Central Public Health, 15, 691. doi:10.1186/s12889-015-1983-z Google Scholar
Lombardo, M.V., Pierce, K., Eyler, L.T., Barnes, C.C., Ahrens-Barbeau, C., Solso, S., & Courchesne, E. (2015). Different functional neural substrates for good and poor language outcome in autism. Neuron, 86(2), 567577.CrossRefGoogle ScholarPubMed
Lubsen, J., Vohr, B., Myers, E., Hampson, M., Lacadie, C., Schneider, K.C., & Ment, L.R. (2011). Microstructural and functional connectivity in the developing preterm brain. Seminars in Perinatology, 35(1), 3443. doi:10.1053/j.semperi.2010.10.006 Google Scholar
Martin, J., O’Donovan, M.C., Thapar, A., Langley, K., & Williams, N. (2015). The relative contribution of common and rare genetic variants to ADHD. Translational Psychiatry, 5, e506. doi:10.1038/tp.2015.5 Google Scholar
Matson, J.L., & Williams, L.W. (2013). Differential diagnosis and comorbidity: Distinguishing autism from other mental health issues. Neuropsychiatry, 3(2), 233243. doi:10.2217/npy.13.1 Google Scholar
McArthur, G., Kohnen, S., Larsen, L., Jones, K., Anandakumar, T., Banales, E., & Castles, A. (2013). Getting to grips with the heterogeneity of developmental dyslexia. Cognitive Neuropsychology, 30(1), 124. doi:10.1080/02643294.2013.784192 Google Scholar
McGrath, C.L., Kelley, M.E., Dunlop, B.W., Holtzheimer, P.E. III, Craighead, W.E., & Mayberg, H.S. (2014). Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biological Psychiatry, 76(7), 527535. doi:10.1016/j.biopsych.2013.12.005 Google Scholar
Mennes, M., Biswal, B.B., Castellanos, F.X., & Milham, M.P. (2013). Making data sharing work: The FCP/INDI experience. Neuroimage, 82, 683691. doi:10.1016/j.neuroimage.2012.10.064 Google Scholar
Ment, L.R., Hirtz, D., & Huppi, P.S. (2009). Imaging biomarkers of outcome in the developing preterm brain. Lancet Neurology, 8(11), 10421055. doi:10.1016/S1474-4422(09)70257-1 CrossRefGoogle ScholarPubMed
Ment, L.R., Kesler, S., Vohr, B., Katz, K.H., Baumgartner, H., Schneider, K.C., & Reiss, A.L. (2009). Longitudinal brain volume changes in preterm and term control subjects during late childhood and adolescence. Journal of Pediatrics, 123(2), 503511. doi:10.1542/peds.2008-0025 Google Scholar
Milham, M.P., Nugent, A.C., Drevets, W.C., Dickstein, D.P., Leibenluft, E., Ernst, M., & Pine, D.S. (2005). Selective reduction in amygdala volume in pediatric anxiety disorders: A voxel-based morphometry investigation. Biological Psychiatry, 57(9), 961966. doi:10.1016/j.biopsych.2005.01.038 Google Scholar
Miller, C.H., Hamilton, J.P., Sacchet, M.D., & Gotlib, I.H. (2015). Meta-analysis of functional neuroimaging of major depressive disorder in youth. Journal of the American Medical Association Psychiatry, 72(10), 10451053. doi:10.1001/jamapsychiatry.2015.1376 Google Scholar
Monk, C., Spicer, J., & Champagne, F.A. (2012). Linking prenatal maternal adversity to developmental outcomes in infants: The role of epigenetic pathways. Development and Psychopathology, 24(4), 13611376. doi:10.1017/S0954579412000764 Google Scholar
Mueller, S., Wang, D., Fox, M.D., Pan, R., Lu, J., Li, K., & Liu, H. (2015). Reliability correction for functional connectivity: Theory and implementation. Human Brain Mapping, 36(11), 46644680. doi:10.1002/hbm.22947 Google Scholar
Muhle, R., Trentacoste, S.V., & Rapin, I. (2004). The genetics of autism. Journal of Pediatrics, 113(5), e472e486.Google Scholar
Mullen, K.M., Vohr, B.R., Katz, K.H., Schneider, K.C., Lacadie, C., Hampson, M., & Ment, L.R. (2011). Preterm birth results in alterations in neural connectivity at age 16 years. Neuroimage, 54(4), 25632570. doi:10.1016/j.neuroimage.2010.11.019 Google Scholar
Myers, E.H., Hampson, M., Vohr, B., Lacadie, C., Frost, S.J., Pugh, K.R., & Ment, L.R. (2010). Functional connectivity to a right hemisphere language center in prematurely born adolescents. Neuroimage, 51(4), 14451452. doi:10.1016/j.neuroimage.2010.03.049 Google Scholar
NessAiver, A., NessAiver, M., Harms, M., & Xu, J. (2015). FBIRN-X: An updated fBIRN quality assurance protocol for slice accelerated fMRI. Retrieved from https://ww4.aievolution.com/hbm1501/files/content/abstracts/44138/1686_NessAiver.pdf.Google Scholar
Nooner, K.B., Colcombe, S.J., Tobe, R.H., Mennes, M., Benedict, M.M., Moreno, A.L., & Milham, M.P. (2012). The NKI-Rockland Sample: A model for accelerating the pace of discovery science in psychiatry. Frontiers in Neuroscience, 6, 152. doi:10.3389/fnins.2012.00152 Google Scholar
O’Connor, T.G., Monk, C., & Fitelson, E.M. (2014). Practitioner review: Maternal mood in pregnancy and child development--implications for child psychology and psychiatry. Journal of Child Psychology and Psychiatry, 55(2), 99111. doi:10.1111/jcpp.12153 Google Scholar
Odegard, T.N., Ring, J., Smith, S., Biggan, J., & Black, J. (2008). Differentiating the neural response to intervention in children with developmental dyslexia. Annals of Dyslexia, 58(1), 114. doi:10.1007/s11881-008-0014-5 Google Scholar
Odsbu, I., Skurtveit, S., Selmer, R., Roth, C., Hernandez-Diaz, S., & Handal, M. (2015). Prenatal exposure to anxiolytics and hypnotics and language competence at 3 years of age. European Journal Clinical Pharmacology, 71(3), 283291. doi:10.1007/s00228-014-1797-4 Google Scholar
Ornoy, A., & Koren, G. (2014). Selective serotonin reuptake inhibitors in human pregnancy: On the way to resolving the controversy. Seminars in Fetal and Neonatal Medicine, 19(3), 188194. doi:10.1016/j.siny.2013.11.007 Google Scholar
Ozonoff, S., Young, G.S., Carter, A., Messinger, D., Yirmiya, N., Zwaigenbaum, L., & Stone, W.L. (2011). Recurrence risk for autism spectrum disorders: A Baby Siblings Research Consortium study. Journal of Pediatrics, 128(3), e488e495. doi:10.1542/peds.2010-2825 Google Scholar
Perez-Edgar, K., & Fox, N.A. (2005). Temperament and anxiety disorders. Child and Adolescent Psychiatric Clinics of North America, 14(4), 681706, viii. doi:10.1016/j.chc.2005.05.008 Google Scholar
Petzoldt, J., Wittchen, H.U., Wittich, J., Einsle, F., Hofler, M., & Martini, J. (2014). Maternal anxiety disorders predict excessive infant crying: A prospective longitudinal study. Archives of Disease in Childhood, 99(9), 800806. doi:10.1136/archdischild-2013-305562 CrossRefGoogle ScholarPubMed
Pezawas, L., Meyer-Lindenberg, A., Drabant, E.M., Verchinski, B.A., Munoz, K.E., Kolachana, B.S., & Weinberger, D.R. (2005). 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: A genetic susceptibility mechanism for depression. Nature Neuroscience, 8(6), 828834. doi:10.1038/nn1463 CrossRefGoogle ScholarPubMed
Pickles, A., Anderson, D.K., & Lord, C. (2014). Heterogeneity and plasticity in the development of language: A 17-year follow-up of children referred early for possible autism. Journal of Child Psychology and Psychiatry, 55(12), 13541362. doi:10.1111/jcpp.12269 CrossRefGoogle Scholar
Pine, D.S., Cohen, E., Cohen, P., & Brook, J. (1999). Adolescent depressive symptoms as predictors of adult depression: Moodiness or mood disorder? American Journal of Psychiatry, 156(1), 133135.Google Scholar
Pine, D.S., Cohen, P., Gurley, D., Brook, J., & Ma, Y. (1998). The risk for early-adulthood anxiety and depressive disorders in adolescents with anxiety and depressive disorders. Archives of General Psychiatry, 55(1), 5664.Google Scholar
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., & Petersen, S.E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 21422154. doi:10.1016/j.neuroimage.2011.10.018 Google Scholar
Price, J.L., & Drevets, W.C. (2012). Neural circuits underlying the pathophysiology of mood disorders. Trends in Cognitive Sciences, 16(1), 6171. doi:10.1016/j.tics.2011.12.011 Google Scholar
Qin, S., Young, C.B., Duan, X., Chen, T., Supekar, K., & Menon, V. (2014). Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood. Biological Psychiatry, 75(11), 892900. doi:10.1016/j.biopsych.2013.10.006 Google Scholar
Qiu, A., Rifkin-Graboi, A., Chen, H., Chong, Y.S., Kwek, K., Gluckman, P.D., & Meaney, M.J. (2013). Maternal anxiety and infants’ hippocampal development: Timing matters. Translational Psychiatry, 3, e306. doi:10.1038/tp.2013.79 Google Scholar
Radua, J., Via, E., Catani, M., & Mataix-Cols, D. (2011). Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls. Psychological Medicine, 41(7), 15391550. doi:10.1017/S0033291710002187 Google Scholar
Rafii, M.S., Wishnek, H., Brewer, J.B., Donohue, M.C., Ness, S., Mobley, W.C., & Rissman, R.A. (2015). The down syndrome biomarker initiative (DSBI) pilot: Proof of concept for deep phenotyping of Alzheimer’s disease biomarkers in down syndrome. Frontiers in Behavioral Neuroscience, 9, 239. doi:10.3389/fnbeh.2015.00239 Google Scholar
Ramenghi, L.A., Rutherford, M., Fumagalli, M., Bassi, L., Messner, H., Counsell, S., & Mosca, F. (2009). Neonatal neuroimaging: Going beyond the pictures. Early Human Development, 85(10 Suppl.), S75S77. doi:10.1016/j.earlhumdev.2009.08.022 Google Scholar
Ramus, F., Rosen, S., Dakin, S.C., Day, B.L., Castellote, J.M., White, S., & Frith, U. (2003). Theories of developmental dyslexia: Insights from a multiple case study of dyslexic adults. Brain, 126(Pt 4), 841865.Google Scholar
Raschle, N.M., Chang, M., & Gaab, N. (2011). Structural brain alterations associated with dyslexia predate reading onset. Neuroimage, 57(3), 742749. doi:10.1016/j.neuroimage.2010.09.055 CrossRefGoogle ScholarPubMed
Raschle, N.M., Stering, P.L., Meissner, S.N., & Gaab, N. (2014). Altered neuronal response during rapid auditory processing and its relation to phonological processing in prereading children at familial risk for dyslexia. Cerebral Cortex, 24(9), 24892501. doi:10.1093/cercor/bht104 Google Scholar
Ray, R.D., & Zald, D.H. (2012). Anatomical insights into the interaction of emotion and cognition in the prefrontal cortex. Neuroscience and Biobehavioral Reviews, 36(1), 479501. doi:10.1016/j.neubiorev.2011.08.005 Google Scholar
Reddy, U.M., Abuhamad, A.Z., Levine, D., Saade, G.R., & Fetal Imaging Workshop Invited Paraticipants. (2014). Fetal imaging: Executive summary of a Joint Eunice Kennedy Shriver National Institute of Child Health and Human Development, Society for Maternal-Fetal Medicine, American Institute of Ultrasound in Medicine, American College of Obstetricians and Gynecologists, American College of Radiology, Society for Pediatric Radiology, and Society of Radiologists in Ultrasound Fetal Imaging Workshop. American Journal of Obstetrics and Gynecology, 210(5), 387397. doi:10.1016/j.ajog.2014.02.028 Google Scholar
Richlan, F., Kronbichler, M., & Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. Neuroimage, 56(3), 17351742. doi:10.1016/j.neuroimage.2011.02.040 Google Scholar
Richlan, F., Kronbichler, M., & Wimmer, H. (2013). Structural abnormalities in the dyslexic brain: A meta-analysis of voxel-based morphometry studies. Human Brain Mapping, 34(11), 30553065. doi:10.1002/hbm.22127 Google Scholar
Rifkin-Graboi, A., Bai, J., Chen, H., Hameed, W.B., Sim, L.W., Tint, M.T., & Qiu, A. (2013). Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth. Biological Psychiatry, 74(11), 837844. doi:10.1016/j.biopsych.2013.06.019 CrossRefGoogle ScholarPubMed
Robinson, O.J., Krimsky, M., Lieberman, L., Allen, P., Vytal, K., & Grillon, C. (2014). Towards a mechanistic understanding of pathological anxiety: The dorsal medial prefrontal-amygdala ‘aversive amplification’ circuit in unmedicated generalized and social anxiety disorders. Lancet Psychiatry, 1(4), 294302. doi:10.1016/S2215-0366(14)70305-0 Google Scholar
Rose, J., Butler, E.E., Lamont, L.E., Barnes, P.D., Atlas, S.W., & Stevenson, D.K. (2009). Neonatal brain structure on MRI and diffusion tensor imaging, sex, and neurodevelopment in very-low-birthweight preterm children. Developmental Medicine and Child Neurology, 51(7), 526535. doi:10.1111/j.1469-8749.2008.03231.x Google Scholar
Roy, A.K., Fudge, J.L., Kelly, C., Perry, J.S., Daniele, T., Carlisi, C., & Ernst, M. (2013). Intrinsic functional connectivity of amygdala-based networks in adolescent generalized anxiety disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 52(3), 290299 e292. doi:10.1016/j.jaac.2012.12.010 Google Scholar
Roy, A.K., Shehzad, Z., Margulies, D.S., Kelly, A.M., Uddin, L.Q., Gotimer, K., & Milham, M.P. (2009). Functional connectivity of the human amygdala using resting state fMRI. Neuroimage, 45(2), 614626. doi:10.1016/j.neuroimage.2008.11.030 Google Scholar
Sadeghi, N., Prastawa, M., Fletcher, P.T., Wolff, J., Gilmore, J.H., & Gerig, G. (2013). Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain. Neuroimage, 68, 236247. doi:10.1016/j.neuroimage.2012.11.040 Google Scholar
Saleem, S.N. (2014). Fetal MRI: An approach to practice: A review. Journal of Advanced Research, 5(5), 507523. doi:10.1016/j.jare.2013.06.001 Google Scholar
Sandin, S., Lichtenstein, P., Kuja-Halkola, R., Larsson, H., Hultman, C.M., & Reichenberg, A. (2014). The familial risk of autism. Journal of the American Medical Association, 311(17), 17701777. doi:10.1001/jama.2014.4144 Google Scholar
Sandman, C.A., Buss, C., Head, K., & Davis, E.P. (2015). Fetal exposure to maternal depressive symptoms is associated with cortical thickness in late childhood. Biological Psychiatry, 77(4), 324334. doi:10.1016/j.biopsych.2014.06.025 Google Scholar
Satterthwaite, T.D., Elliott, M.A., Gerraty, R.T., Ruparel, K., Loughead, J., Calkins, M.E., & Wolf, D.H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage, 64, 240256. doi:10.1016/j.neuroimage.2012.08.052 CrossRefGoogle ScholarPubMed
Saygin, Z.M., Norton, E.S., Osher, D.E., Beach, S.D., Cyr, A.B., Ozernov-Palchik, O., & Gabrieli, J.D. (2013). Tracking the roots of reading ability: White matter volume and integrity correlate with phonological awareness in prereading and early-reading kindergarten children. Journal of Neuroscience, 33(33), 1325113258. doi:10.1523/JNEUROSCI.4383-12.2013 CrossRefGoogle ScholarPubMed
Schmaal, L., Veltman, D.J., van Erp, T.G., Samann, P.G., Frodl, T., Jahanshad, N., & Hibar, D.P. (2015). Subcortical brain alterations in major depressive disorder: Findings from the ENIGMA Major Depressive Disorder working group. Molecular Psychiatry. doi:10.1038/mp.2015.69 Google Scholar
Schopf, V., Kasprian, G., Brugger, P.C., & Prayer, D. (2012). Watching the fetal brain at ‘rest’. International Journal of Developmental Neuroscience, 30(1), 1117. doi:10.1016/j.ijdevneu.2011.10.006 CrossRefGoogle ScholarPubMed
Schumann, G., Binder, E.B., Holte, A., de Kloet, E.R., Oedegaard, K.J., Robbins, T.W., & Wittchen, H.U. (2014). Stratified medicine for mental disorders. European Neuropsychopharmacology, 24(1), 550. doi:10.1016/j.euroneuro.2013.09.010 Google Scholar
Seshamani, S., Cheng, X., Fogtmann, M., Thomason, M.E., & Studholme, C. (2014). A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain. Medical Image Analysis, 18(2), 285300. doi:10.1016/j.media.2013.10.011 Google Scholar
Shaywitz, B.A., Shaywitz, S.E., Blachman, B.A., Pugh, K.R., Fulbright, R.K., Skudlarski, P., & Gore, J.C. (2004). Development of left occipitotemporal systems for skilled reading in children after a phonologically-based intervention. Biological Psychiatry, 55(9), 926933. doi:10.1016/j.biopsych.2003.12.019 Google Scholar
Shen, M.D., Nordahl, C.W., Young, G.S., Wootton-Gorges, S.L., Lee, A., Liston, S.E., & Amaral, D.G. (2013). Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain, 136(Pt 9), 28252835. doi:10.1093/brain/awt166 CrossRefGoogle ScholarPubMed
Silberg, J., Rutter, M., Neale, M., & Eaves, L. (2001). Genetic moderation of environmental risk for depression and anxiety in adolescent girls. The British Journal of Psychiatry, 179(2), 116121.CrossRefGoogle ScholarPubMed
Silk, J.S., Davis, S., McMakin, D.L., Dahl, R.E., & Forbes, E.E. (2012). Why do anxious children become depressed teenagers? The role of social evaluative threat and reward processing. Psychological Medicine, 42(10), 20952107.Google Scholar
Simos, P.G., Fletcher, J.M., Sarkari, S., Billingsley-Marshall, R., Denton, C.A., & Papanicolaou, A.C. (2007). Intensive instruction affects brain magnetic activity associated with oral word reading in children with persistent reading disabilities. Journal of Learning Disabilities, 40(1), 3748.Google Scholar
Smith, L.K., Draper, E.S., & Field, D. (2014). Long-term outcome for the tiniest or most immature babies: Survival rates. Seminars in Fetal and Neonatal Medicine, 19(2), 7277. doi:10.1016/j.siny.2013.11.002 Google Scholar
Smyser, C.D., Inder, T.E., Shimony, J.S., Hill, J.E., Degnan, A.J., Snyder, A.Z., & Neil, J.J. (2010). Longitudinal analysis of neural network development in preterm infants. Cerebral Cortex, 20(12), 28522862. doi:10.1093/cercor/bhq035 Google Scholar
Smyser, C.D., Snyder, A.Z., & Neil, J.J. (2011). Functional connectivity MRI in infants: Exploration of the functional organization of the developing brain. Neuroimage, 56(3), 14371452. doi:10.1016/j.neuroimage.2011.02.073 Google Scholar
Stepniak, B., Papiol, S., Hammer, C., Ramin, A., Everts, S., Hennig, L., & Ehrenreich, H. (2014). Accumulated environmental risk determining age at schizophrenia onset: A deep phenotyping-based study. Lancet Psychiatry, 1(6), 444453. doi:10.1016/S2215-0366(14)70379-7 Google Scholar
Szatmari, P., Merette, C., Bryson, S.E., Thivierge, J., Roy, M.A., Cayer, M., & Maziade, M. (2002). Quantifying dimensions in autism: A factor-analytic study. Journal of the American Academy of Child and Adolescent Psychiatry, 41(4), 467474. doi:10.1097/00004583-200204000-00020 Google Scholar
Tau, G.Z., & Peterson, B.S. (2010). Normal development of brain circuits. Neuropsychopharmacology, 35(1), 147168. doi:10.1038/npp.2009.115 Google Scholar
Temple, E., Deutsch, G.K., Poldrack, R.A., Miller, S.L., Tallal, P., Merzenich, M.M., &Gabrieli, J.D. (2003). Neural deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 100(5), 28602865. doi:10.1073/pnas.0030098100 Google Scholar
Thomason, M.E., Brown, J.A., Dassanayake, M.T., Shastri, R., Marusak, H.A., Hernandez-Andrade, E., & Romero, R. (2014). Intrinsic functional brain architecture derived from graph theoretical analysis in the human fetus. PLoS One, 9(5), e94423. doi:10.1371/journal.pone.0094423 Google Scholar
Thomason, M.E., Dassanayake, M.T., Shen, S., Katkuri, Y., Alexis, M., Anderson, A.L., & Romero, R. (2013). Cross-hemispheric functional connectivity in the human fetal brain. Science Translational Medicine, 5(173), 173ra124. doi:10.1126/scitranslmed.3004978 Google Scholar
Thomason, M.E., Grove, L.E., Lozon, T.A. Jr., Vila, A.M., Ye, Y., Nye, M.J., & Romero, R. (2015). Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero. Developmental Cognitive Neuroscience, 11, 96104. doi:10.1016/j.dcn.2014.09.001 Google Scholar
Thompson, P.A., Hulme, C., Nash, H.M., Gooch, D., Hayiou-Thomas, E., & Snowling, M.J. (2015). Developmental dyslexia: Predicting individual risk. Journal of Child Psychology and Psychiatry, 56(9), 976987. doi:10.1111/jcpp.12412 Google Scholar
Torgesen, J.K. (2000). Individual differences in response to early interventions in reading: The lingering problem of treatment resisters. Learning Disabilities Research & Practice, 15(1), 5564.Google Scholar
Torgesen, J.K., Wagner, R.K., Rashotte, C.A., Rose, E., Lindamood, P., Conway, T., & Garvan, C. (1999). Preventing reading failure in young children with phonological processing disabilities: Group and individual responses to instruction. Journal of Educational Psychology, 91, 115.Google Scholar
Treyvaud, K., Ure, A., Doyle, L.W., Lee, K.J., Rogers, C.E., Kidokoro, H., & Anderson, P.J. (2013). Psychiatric outcomes at age seven for very preterm children: Rates and predictors. Journal of Child Psychology and Psychiatry, 54(7), 772779. doi:10.1111/jcpp.12040 Google Scholar
Uehara, T., Yamasaki, T., Okamoto, T., Koike, T., Kan, S., Miyauchi, S., & Tobimatsu, S. (2014). Efficiency of a “small-world” brain network depends on consciousness level: A resting-state FMRI study. Cerebral Cortex, 24(6), 15291539. doi:10.1093/cercor/bht004 Google Scholar
Van den Bergh, B.R., & Marcoen, A. (2004). High antenatal maternal anxiety is related to ADHD symptoms, externalizing problems, and anxiety in 8- and 9-year-olds. Child Development, 75(4), 10851097. doi:10.1111/j.1467-8624.2004.00727.x Google Scholar
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., & WU-Minn HPC Consortium (2013). The WU-Minn HPC Consortium. Neuroimage, 80, 6279. doi:10.1016/j.neuroimage.2013.05.041 Google Scholar
Vellutino, F.R., Scanlon, D.M., Sipay, E.R., Small, S.G., Pratt, A., Chen, R., & Dencla, M.B. (1996). Cognitive profiles of difficult-to-remediate and readily remediated poor readers: Early interventions as a vehicle for distinguishing between cognitive and experiential deficits as basic causes of specific reading disability. Journal of Educational Psychology, 88, 601638.Google Scholar
Walker, L., Chang, L.C., Nayak, A., Irfanoglu, M.O., Botteron, K.N., McCracken, J., … Brain Development Cooperative Group. (2016). The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI). Neuroimage, 124, 11251130. doi:10.1016/j.neuroimage.2015.05.083 Google Scholar
Wang, X.H., Li, L., Xu, T., & Ding, Z. (2015). Investigating the temporal patterns within and between intrinsic connectivity networks under eyes-open and eyes-closed resting states: A dynamical functional connectivity study based on phase synchronization. PLoS One, 10(10), e0140300. doi:10.1371/journal.pone.0140300 Google Scholar
Wang, Z., Fernandez-Seara, M., Alsop, D.C., Liu, W.C., Flax, J.F., Benasich, A.A., & Detre, J.A. (2008). Assessment of functional development in normal infant brain using arterial spin labeled perfusion MRI. Neuroimage, 39(3), 973978. doi:10.1016/j.neuroimage.2007.09.045 Google Scholar
Watanabe, T., Kessler, D., Scott, C., Angstadt, M., & Sripada, C. (2014). Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. Neuroimage, 96, 183202. doi:10.1016/j.neuroimage.2014.03.067 Google Scholar
Weissman, M.M., Fendrich, M., Warner, V., & Wickramaratne, P. (1992). Incidence of psychiatric disorder in offspring at high and low risk for depression. Journal of the American Academy of Child & Adolescent Psychiatry, 31(4), 640648.CrossRefGoogle ScholarPubMed
Weissman, M.M., Wickramaratne, P., Nomura, Y., Warner, V., Verdeli, H., Pilowsky, D.J., & Bruder, G. (2005). Families at high and low risk for depression: A 3-generation study. Archives of General Psychiatry, 62(1), 2936.Google Scholar
Weissman, M.M., Wolk, S., Wickramaratne, P., Goldstein, R.B., Adams, P., Greenwald, S., & Steinberg, D. (1999). Children with prepubertal-onset major depressive disorder and anxiety grown up. Archives of General Psychiatry, 56(9), 794801.Google Scholar
Welsh, R.C., Nemec, U., & Thomason, M.E. (2011). Fetal magnetic resonance imaging at 3.0 T. Topics in Magnetic Resonance Imaging, 22(3), 119131. doi:10.1097/RMR.0b013e318267f932 CrossRefGoogle ScholarPubMed
Werner, E.A., Myers, M.M., Fifer, W.P., Cheng, B., Fang, Y., Allen, R., & Monk, C. (2007). Prenatal predictors of infant temperament. Developmental Psychobiology, 49(5), 474484. doi:10.1002/dev.20232 Google Scholar
White, T., Nelson, M., & Lim, K.O. (2008). Diffusion tensor imaging in psychiatric disorders. Topics in Magnetic Resonance Imaging, 19(2), 97109. doi:10.1097/RMR.0b013e3181809f1e Google Scholar
Whitfield-Gabrieli, S., Thermenos, H.W., Milanovic, S., Tsuang, M.T., Faraone, S.V., McCarley, R.W., & Seidman, L.J. (2009). Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 106(4), 12791284. doi:10.1073/pnas.0809141106 Google Scholar
Wiggins, L.D., Robins, D.L., Adamson, L.B., Bakeman, R., & Henrich, C.C. (2012). Support for a dimensional view of autism spectrum disorders in toddlers. Journal of Autism and Developmental Disorders, 42(2), 191200. doi:10.1007/s10803-011-1230-0 Google Scholar
Willcutt, E.G., Betjemann, R.S., McGrath, L.M., Chhabildas, N.A., Olson, R.K., DeFries, J.C., & Pennington, B.F. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46(10), 13451361. doi:10.1016/j.cortex.2010.06.009 CrossRefGoogle ScholarPubMed
Willcutt, E.G., Petrill, S.A., Wu, S., Boada, R., Defries, J.C., Olson, R.K., & Pennington, B.F. (2013). Comorbidity between reading disability and math disability: Concurrent psychopathology, functional impairment, and neuropsychological functioning. Journal of Learning Disabilities, 46(6), 500516. doi:10.1177/0022219413477476 Google Scholar
Wolff, J.J., Botteron, K.N., Dager, S.R., Elison, J.T., Estes, A.M., Gu, H., & Network, I. (2014). Longitudinal patterns of repetitive behavior in toddlers with autism. Journal of Child Psychology and Psychiatry, 55(8), 945953. doi:10.1111/jcpp.12207 Google Scholar
Wolff, J.J., Gerig, G., Lewis, J.D., Soda, T., Styner, M.A., Vachet, C., IBIS Network. (2015). Altered corpus callosum morphology associated with autism over the first 2 years of life. Brain, 138(Pt 7), 20462058. doi:10.1093/brain/awv118 Google Scholar
Wolff, J.J., Gu, H., Gerig, G., Elison, J.T., Styner, M., Gouttard, S., … IBIS Network. (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. American Journal of Psychiatry, 169(6), 589600. doi:10.1176/appi.ajp.2011.11091447 Google Scholar
Woodward, L.J., & Fergusson, D.M. (2001). Life course outcomes of young people with anxiety disorders in adolescence. Journal of the American Academy of Child and Adolescent Psychiatry, 40(9), 10861093. doi:10.1097/00004583-200109000-00018 Google Scholar
Yan, C.G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R.C., Di Martino, A., & Milham, M.P. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage, 76(0), 183201. doi:http://dx.doi.org/10.1016/j.neuroimage.2013.03.004 Google Scholar
Yan, C.G., Craddock, R.C., He, Y., & Milham, M.P. (2013). Addressing head motion dependencies for small-world topologies in functional connectomics. Frontiers in Human Neuroscience, 7, 910. doi:10.3389/fnhum.2013.00910 CrossRefGoogle ScholarPubMed
Yan, C.G., Craddock, R.C., Zuo, X.-N., Zang, Y.-F., & Milham, M.P. (2013). Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage, 80(0), 246262. doi:http://dx.doi.org/10.1016/j.neuroimage.2013.04.081 CrossRefGoogle ScholarPubMed
Young, J.M., Powell, T.L., Morgan, B.R., Card, D., Lee, W., Smith, M.L., & Taylor, M.J. (2015). Deep grey matter growth predicts neurodevelopmental outcomes in very preterm children. Neuroimage, 111, 360368. doi:10.1016/j.neuroimage.2015.02.030 Google Scholar
Zuo, X.N., Anderson, J.S., Bellec, P., Birn, R.M., Biswal, B.B., Blautzik, J., & Milham, M.P. (2014). An open science resource for establishing reliability and reproducibility in functional connectomics. Scientific Data, 1, 140049. doi:10.1038/sdata.2014.49 Google Scholar