Skip to main content Accessibility help
×
Home

Imaging the “At-Risk” Brain: Future Directions

  • Maki S. Koyama (a1) (a2), Adriana Di Martino (a3), Francisco X. Castellanos (a2) (a3), Erica J. Ho (a1), Enitan Marcelle (a1), Bennett Leventhal (a4) and Michael P. Milham (a1) (a2)...

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)

Copyright

Corresponding author

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

References

Hide All
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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.
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
Behrman, R.E., & Butler, A.S. (2007). Preterm birth: Causes, consequences, and prevention. Washington, DC: National Academies Press.
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.
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
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
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.
Bremner, J.D. (2004). Brain imaging in anxiety disorders. Expert Review of Neurotherapeutics, 4(2), 275284. doi:10.1586/14737175.4.2.275
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
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.
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
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
Burmeister, M., McInnis, M.G., & Zollner, S. (2008). Psychiatric genetics: Progress amid controversy. Nature Reviews. Genetics, 9(7), 527540. doi:10.1038/nrg2381
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
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.
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
Drevets, W.C., Savitz, J., & Trimble, M. (2008). The subgenual anterior cingulate cortex in mood disorders. CNS Spectrums, 13(8), 663681.
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Germano, E., Gagliano, A., & Curatolo, P. (2010). Comorbidity of ADHD and dyslexia. Developmental Neuropsychology, 35(5), 475493. doi:10.1080/87565641.2010.494748
Geschwind, D.H. (2009). Advances in autism. Annual Review of Medicine, 60, 367380. doi:10.1146/annurev.med.60.053107.121225
Geschwind, N. (1965a). Disconnexion syndromes in animals and man. I. Brain, 88(2), 237294.
Geschwind, N. (1965b). Disconnexion syndromes in animals and man. II. Brain, 88(3), 585644.
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
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
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
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
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
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
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
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
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
Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2004). Adult outcome for children with autism. Journal of Child Psychology and Psychiatry, 45(2), 212229.
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.
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
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
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
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
Johnson, S., & Marlow, N. (2011). Preterm birth and childhood psychiatric disorders. Pediatric Research, 69(5, Part 2 of 2), 11R18R.
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
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
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
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
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
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
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
Kessler, R.C., Avenevoli, S., & Merikangas, K.R. (2001). Mood disorders in children and adolescents: An epidemiologic perspective. Biological Psychiatry, 49(12), 10021014.
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
Muhle, R., Trentacoste, S.V., & Rapin, I. (2004). The genetics of autism. Journal of Pediatrics, 113(5), e472e486.
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
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
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.
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
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
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
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
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
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
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
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
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
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
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.
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.
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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.
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.
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
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
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
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
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
Tau, G.Z., & Peterson, B.S. (2010). Normal development of brain circuits. Neuropsychopharmacology, 35(1), 147168. doi:10.1038/npp.2009.115
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
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
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
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
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
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.
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.
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
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
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
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
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.
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
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
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
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
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.
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

Keywords

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed