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1. Brain charts for the human lifespan

Author: Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cábez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, and Landeau, B
Subjects: Biological Psychology, Psychology, Aging, Clinical Research, Neurosciences, Brain Disorders, Biomedical Imaging, Neurological, Mental health, Body Height, Brain, Humans, Longevity, Magnetic Resonance Imaging, Neuroimaging, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, Mayo Clinic Study of Aging, NSPN, POND, PREVENT-AD Research Group, VETSA, General Science & Technology
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Published: 2022

2. Publisher Correction: Brain charts for the human lifespan

Author: Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.
Published: 2022
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3. Heritable influences on amygdala and orbitofrontal cortex contribute to genetic variation in core dimensions of personality

Author: Lewis, GJ, Panizzon, MS, Eyler, L, Fennema-Notestine, C, Chen, C-H, Neale, MC, Jernigan, TL, Lyons, MJ, Dale, AM, Kremen, WS, and Franz, CE
Subjects: Biomedical and Clinical Sciences, Health Sciences, Neurosciences, Genetics, 1.1 Normal biological development and functioning, Mental health, Amygdala, Female, Frontal Lobe, Genetic Variation, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Organ Size, Personality, Phenotype, MPQ, MRI, Orbitofrontal cortex, Twins, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract: While many studies have reported that individual differences in personality traits are genetically influenced, the neurobiological bases mediating these influences have not yet been well characterized. To advance understanding concerning the pathway from genetic variation to personality, here we examined whether measures of heritable variation in neuroanatomical size in candidate regions (amygdala and medial orbitofrontal cortex) were associated with heritable effects on personality. A sample of 486 middle-aged (mean=55 years) male twins (complete MZ pairs=120; complete DZ pairs=84) underwent structural brain scans and also completed measures of two core domains of personality: positive and negative emotionality. After adjusting for estimated intracranial volume, significant phenotypic (r(p)) and genetic (r(g)) correlations were observed between left amygdala volume and positive emotionality (r(p)=.16, p
Published: 2014

4. A functional magnetic resonance imaging study of cortical asymmetry in bipolar disorder

Author: Caligiuri, Michael P, Brown, G G, Meloy, M J, Eyler, L T, Kindermann, S S, Eberson, S, Frank, L R, and Lohr, J B
Subjects: bipolar disorder, fMRI, laterality, mood stabilizers, reaction time, supplementary motor area
Abstract: Objectives: Individuals with bipolar disorder (BPD) exhibit motor, perceptual, and cognitive disturbances involving predominantly right hemisphere dysfunction. This asymmetry has been used to advance the hypothesis that the pathogenesis of bipolar disorder may be related to disturbances of the right cerebral hemisphere. We employed functional magnetic resonance imaging to examine hemispheric asymmetries in manic and depressed BPD. A secondary goal of the Study was to examine effects of psychotropic medications on blood Volume changes in the motor cortices. Methods: We studied 18 right-handed BPD and 13 right-handed normal healthy comparison subjects. Blood oxygen level dependent (BOLD) responses in the primary motor area (M1) and Supplementary motor area (SMA) of both hemispheres were elicited during reaction time (RT) tasks. Results: Healthy subjects activated the SMA in a reciprocal fashion with significantly greater activity in the left SMA for right hand trials and the right SMA for left hand trials. Depressed BPD subjects failed to show this normal reciprocity indicating a failure to Suppress unwanted activity in the ipsilateral right SMA, whereas manic BPD subjects failed to suppress unwanted ipsilateral SMA activity in both hemispheres. Manic and depressed BPD subjects exhibited greater activity in the left primary motor area suggesting increased cortical excitability. BPD subjects treated with antipsychotics or mood-stabilizing medications exhibited longer RTs, lower BOLD responses in M1 and SMA, and a loss of normal hemispheric asymmetry in the SMA than untreated subjects. Conclusions: The presence of a right hemisphere disturbance in BPD is consistent with the hypothesis that the right hemisphere may be dominant in mood regulation. The presence of both left and right hemisphere disturbances in mania may explain the coexisting psychotic and affective symptoms observed in this condition.
Published: 2004

5. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals

Author: Marchetto, M C, Belinson, H, Tian, Y, Freitas, B C, Fu, C, Vadodaria, K C, Beltrao-Braga, P C, Trujillo, C A, Mendes, A P D, Padmanabhan, K, Nunez, Y, Ou, J, Ghosh, H, Wright, R, Brennand, K J, Pierce, K, Eichenfield, L, Pramparo, T, Eyler, L T, Barnes, C C, Courchesne, E, Geschwind, D H, Gage, F H, Wynshaw-Boris, A, and Muotri, A R
Published: 2017
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6. Predictors of Functional Impairment in Bipolar Disorder: Results from 13 Cohorts from Seven Countries by The Global Bipolar Cohort Collaborative

Author: Burdick, KE, Millett, CE, Yocum, AK, Altimus, CM, Andreassen, OA, Aubin, V, Belzeaux, R, Berk, Michael, Biernacka, JM, Blumberg, HP, Cleare, AJ, Diaz‐Byrd, C, Dubertret, C, Etain, B, Eyler, L, Forester, BP, Fullerton, JM, Frye, MA, Gard, S, Godin, O, Haffen, E, Klaus, F, Lagerberg, TV, Leboyer, M, Martinez‐Aran, A, McElroy, S, Mitchell, PB, Olie, E, Olorunfemi, P, Passerieux, C, Peters, A, Pham, DL, Polosan, M, Potter, JR, Sajatovic, M, Samalin, L, Schwan, R, Shanahan, M, Solé, B, Strawbridge, R, Stuart, Amanda, Torres, I, Ueland, T, Vieta, E, Williams, Lana, Wrobel, Anna, Yatham, LN, Young, AH, Nierenberg, A, McInnis, MG, Burdick, KE, Millett, CE, Yocum, AK, Altimus, CM, Andreassen, OA, Aubin, V, Belzeaux, R, Berk, Michael, Biernacka, JM, Blumberg, HP, Cleare, AJ, Diaz‐Byrd, C, Dubertret, C, Etain, B, Eyler, L, Forester, BP, Fullerton, JM, Frye, MA, Gard, S, Godin, O, Haffen, E, Klaus, F, Lagerberg, TV, Leboyer, M, Martinez‐Aran, A, McElroy, S, Mitchell, PB, Olie, E, Olorunfemi, P, Passerieux, C, Peters, A, Pham, DL, Polosan, M, Potter, JR, Sajatovic, M, Samalin, L, Schwan, R, Shanahan, M, Solé, B, Strawbridge, R, Stuart, Amanda, Torres, I, Ueland, T, Vieta, E, Williams, Lana, Wrobel, Anna, Yatham, LN, Young, AH, Nierenberg, A, and McInnis, MG
Published: 2022

7. Brain charts for the human lifespan (vol 604, pg 525, 2022)

Author: Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, Alexander-Bloch, AF, Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, and Alexander-Bloch, AF
Published: 2022

8. Brain charts for the human lifespan

Author: British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, Bethlehem, Richard A. I., Seidlitz, J., White, S. R., Vogel, Jacob W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-González, A., Astle, D. E., Auyeung, Bonnie, Ayub, Muhammad, Bae, J., Ball, G, Baron-Cohen, Simon, Beare, R., Bedford, S. A., Benegal, V., Beyer, Frauke, Blangero, John, Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, Aiden, Costantino, M., Courchesne, E., Crivello, Fabrice, Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, Sylvane, Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, Gary, Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galán-García, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, David C., Goodyer, I. M., Grant, P. E., Groenewold, Nynke A., Gunning, F. M., Gur, Rachel E., Gur, Ruben C., Hammill, C. F., Hansson, Oskar, Hedden, T., Heinz, Andreas, Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, Avram J., Holt, Rosemary, Huang, Hailiang, Im, K., Ipser, J., Jack, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, David T. W., Kahn, René S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, Silke, Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, Michael, V., Lv, J., Lynch, Christopher D., Mallard, T. T., Marcelis, Machteld, Markello, R. D., Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, M. J., Mechelli, Andrea, Medic, N., Misic, B., Morgan, S. E., Mothersill, David O., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, Rik, Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, Christos, Park, M. M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, D., Pichler, Alexander, Pierce, K., Qian, X., Qiu, Jing, Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero García, Rafael, Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, Theodore D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, Gunter, Schöll, Michael, Sharp, D., Shinohara, R. T., Skoog, Ingmar, Smyser, C. D., Sperling, R. A., Stein, Dan J., Stolicyn, A., Suckling, John, Sullivan, G., Taki, Y., Thyreau, B., Toro, Raquel del, Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, Arno, Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, Eric, Westwater, M. L., Whalley, Heather, Witte, A. Veronica, Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, Anna, Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., Alexander-Bloch, A. F., British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, Bethlehem, Richard A. I., Seidlitz, J., White, S. R., Vogel, Jacob W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-González, A., Astle, D. E., Auyeung, Bonnie, Ayub, Muhammad, Bae, J., Ball, G, Baron-Cohen, Simon, Beare, R., Bedford, S. A., Benegal, V., Beyer, Frauke, Blangero, John, Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, Aiden, Costantino, M., Courchesne, E., Crivello, Fabrice, Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, Sylvane, Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, Gary, Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galán-García, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, David C., Goodyer, I. M., Grant, P. E., Groenewold, Nynke A., Gunning, F. M., Gur, Rachel E., Gur, Ruben C., Hammill, C. F., Hansson, Oskar, Hedden, T., Heinz, Andreas, Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, Avram J., Holt, Rosemary, Huang, Hailiang, Im, K., Ipser, J., Jack, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, David T. W., Kahn, René S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, Silke, Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, Michael, V., Lv, J., Lynch, Christopher D., Mallard, T. T., Marcelis, Machteld, Markello, R. D., Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, M. J., Mechelli, Andrea, Medic, N., Misic, B., Morgan, S. E., Mothersill, David O., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, Rik, Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, Christos, Park, M. M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, D., Pichler, Alexander, Pierce, K., Qian, X., Qiu, Jing, Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero García, Rafael, Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, Theodore D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, Gunter, Schöll, Michael, Sharp, D., Shinohara, R. T., Skoog, Ingmar, Smyser, C. D., Sperling, R. A., Stein, Dan J., Stolicyn, A., Suckling, John, Sullivan, G., Taki, Y., Thyreau, B., Toro, Raquel del, Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, Arno, Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, Eric, Westwater, M. L., Whalley, Heather, Witte, A. Veronica, Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, Anna, Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Published: 2022

9. Concurrent antiepileptic and antipsychotic use moderates lithium’s effects on regional brain volumes: a mega-analysis from the ENIGMA-Bipolar Disorder Working Group

Author: King, S., Tronchin, G., Nabulsi, L., Thomopoulos, S.I., Fontana, E., Radua, J., Sim, K., Gruber, O., Yatham, L., Dannlowski, U., Kircher, T., Nenadic, I., Stein, F., Brosch, K., Howells, F., Haarman, B.C.M., Pomarol-Clotet, E., Vieta, E., Landen, M., Cannon, D., Alnæs, D., Westlye, L.T., Jaramillo, C. López, Soeiro-de-Souza, M. Gerhardt, Berk, M., Elvsåshagen, T., Roberts, G., Mitchell, P.B., Fullerton, J.M., Green, M.J., Quidé, Y., Hermesdorf, M., Berger, K., Soares, J., Satterthwaite, T., Savitz, J., Benedetti, F., Glahn, D., Hajek, T., Kuplicki, R., Gotlib, I.H., Amoretti, S., Sacchet, M., Favre, P., Van Rheenen, T., Karantonis, J. Anthony, Furlong, L., Forte, F., Rossell, S., Goldstein, B., Kennedy, K., Canales-Rodriguez, E., Lahud, E., Mwangi, B., Rodriguez-Cano, E., Salvador, R., Wu, M.-J., Houenou, J., Rodrigue, A., Melloni, E.M.T., Sponheim, S., Urosevic, S., Demro, C., Goya-Maldonado, R., Eyler, L., Thompson, P.M., Andreassan, O.A., Ching, C.R.K., and McDonald, C.
Published: 2022
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10. Correction: Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals (Neuropsychopharmacology, (2019), 44, 13, (2285-2293), 10.1038/s41386-019-0485-6)

Author: Favre P., Pauling M., Stout J., Hozer F., Sarrazin S., Abe C., Alda M., Alloza C., Alonso-Lana S., Andreassen O. A., Baune B. T., Benedetti F., Busatto G. F., Canales-Rodriguez E. J., Caseras X., Chaim-Avancini T. M., Ching C. R. K., Dannlowski U., Deppe M., Eyler L. T., Fatjo-Vilas M., Foley S. F., Grotegerd D., Hajek T., Haukvik U. K., Howells F. M., Jahanshad N., Kugel H., Lagerberg T. V., Lawrie S. M., Linke J. O., McIntosh A., Melloni E. M. T., Mitchell P. B., Polosan M., Pomarol-Clotet E., Repple J., Roberts G., Roos A., Rosa P. G. P., Salvador R., Sarro S., Schofield P. R., Serpa M. H., Sim K., Stein D. J., Sussmann J. E., Temmingh H. S., Thompson P. M., Verdolini N., Vieta E., Wessa M., Whalley H. C., Zanetti M. V., Leboyer M., Mangin J. -F., Henry C., Duchesnay E., Houenou J., Favre, P., Pauling, M., Stout, J., Hozer, F., Sarrazin, S., Abe, C., Alda, M., Alloza, C., Alonso-Lana, S., Andreassen, O. A., Baune, B. T., Benedetti, F., Busatto, G. F., Canales-Rodriguez, E. J., Caseras, X., Chaim-Avancini, T. M., Ching, C. R. K., Dannlowski, U., Deppe, M., Eyler, L. T., Fatjo-Vilas, M., Foley, S. F., Grotegerd, D., Hajek, T., Haukvik, U. K., Howells, F. M., Jahanshad, N., Kugel, H., Lagerberg, T. V., Lawrie, S. M., Linke, J. O., Mcintosh, A., Melloni, E. M. T., Mitchell, P. B., Polosan, M., Pomarol-Clotet, E., Repple, J., Roberts, G., Roos, A., Rosa, P. G. P., Salvador, R., Sarro, S., Schofield, P. R., Serpa, M. H., Sim, K., Stein, D. J., Sussmann, J. E., Temmingh, H. S., Thompson, P. M., Verdolini, N., Vieta, E., Wessa, M., Whalley, H. C., Zanetti, M. V., Leboyer, M., Mangin, J. -F., Henry, C., Duchesnay, E., and Houenou, J.
Abstract: This Article was originally published under NPG's License to Publish, but has now been made available under a [CC BY 4.0] license. The PDF and HTML versions of the Article have been modified accordingly.
Published: 2019

11. Call to action regarding the vascular-bipolar link: A report from the Vascular Task Force of the International Society for Bipolar Disorders

Author: Goldstein, BI, Baune, BT, Bond, DJ, Chen, P-H, Eyler, L, Fagiolini, A, Gomes, F, Hajek, T, Hatch, J, McElroy, SL, McIntyre, RS, Prieto, M, Sylvia, LG, Tsai, S-Y, Kcomt, A, Fiedorowicz, JG, Goldstein, BI, Baune, BT, Bond, DJ, Chen, P-H, Eyler, L, Fagiolini, A, Gomes, F, Hajek, T, Hatch, J, McElroy, SL, McIntyre, RS, Prieto, M, Sylvia, LG, Tsai, S-Y, Kcomt, A, and Fiedorowicz, JG
Abstract: OBJECTIVES: The association of bipolar disorder with early and excessive cardiovascular disease was identified over a century ago. Nonetheless, the vascular-bipolar link remains underrecognized, particularly with regard to how this link can contribute to our understanding of pathogenesis and treatment. METHODS: An international group of experts completed a selective review of the literature, distilling core themes, identifying limitations and gaps in the literature, and highlighting future directions to bridge these gaps. RESULTS: The association between bipolar disorder and vascular disease is large in magnitude, consistent across studies, and independent of confounding variables where assessed. The vascular-bipolar link is multifactorial and is difficult to study given the latency between the onset of bipolar disorder, often in adolescence or early adulthood, and subsequent vascular disease, which usually occurs decades later. As a result, studies have often focused on risk factors for vascular disease or intermediate phenotypes, such as structural and functional vascular imaging measures. There is interest in identifying the most relevant mediators of this relationship, including lifestyle (eg, smoking, diet, exercise), medications, and systemic biological mediators (eg, inflammation). Nonetheless, there is a paucity of treatment studies that deliberately engage these mediators, and thus far no treatment studies have focused on engaging vascular imaging targets. CONCLUSIONS: Further research focused on the vascular-bipolar link holds promise for gleaning insights regarding the underlying causes of bipolar disorder, identifying novel treatment approaches, and mitigating disparities in cardiovascular outcomes for people with bipolar disorder.
Published: 2020

12. A multivariate twin study of hippocampal volume, self-esteem and well-being in middle-aged men

Author: Kubarych, T. S., Prom-Wormley, E. C., Franz, C. E., Panizzon, M. S., Dale, A. M., Fischl, B., Eyler, L. T., Fennema-Notestine, C., Grant, M. D., Hauger, R. L., Hellhammer, D. H., Jak, A. J., Jernigan, T. L., Lupien, S. J., Lyons, M. J., Mendoza, S. P., Neale, M. C., Seidman, L. J., Tsuang, M. T., and Kremen, W. S.
Published: 2012
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13. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals

Author: Marchetto, M C, primary, Belinson, H, additional, Tian, Y, additional, Freitas, B C, additional, Fu, C, additional, Vadodaria, K C, additional, Beltrao-Braga, P C, additional, Trujillo, C A, additional, Mendes, A P D, additional, Padmanabhan, K, additional, Nunez, Y, additional, Ou, J, additional, Ghosh, H, additional, Wright, R, additional, Brennand, K J, additional, Pierce, K, additional, Eichenfield, L, additional, Pramparo, T, additional, Eyler, L T, additional, Barnes, C C, additional, Courchesne, E, additional, Geschwind, D H, additional, Gage, F H, additional, Wynshaw-Boris, A, additional, and Muotri, A R, additional
Published: 2016
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14. Reverse translation of the rodent 5C-CPT reveals that the impaired attention of people with schizophrenia is similar to scopolamine-induced deficits in mice

Author: Young, J W, primary, Geyer, M A, additional, Rissling, A J, additional, Sharp, R F, additional, Eyler, L T, additional, Asgaard, G L, additional, and Light, G A, additional
Published: 2013
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15. Multimedia Consent for Research in People With Schizophrenia and Normal Subjects: a Randomized Controlled Trial

Author: Jeste, D. V., primary, Palmer, B. W., additional, Golshan, S., additional, Eyler, L. T., additional, Dunn, L. B., additional, Meeks, T., additional, Glorioso, D., additional, Fellows, I., additional, Kraemer, H., additional, and Appelbaum, P. S., additional
Published: 2008
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16. Reverse translation of the rodent 5C-CPT reveals that the impaired attention of people with schizophrenia is similar to scopolamineinduced deficits in mice.

Author: Young, J. W., Geyer, M. A., Rissling, A. J., Sharp, R. F., Eyler, L. T., Asgaard, G. L., and Light, G. A.
Published: 2013
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17. Non-Isothermal Mercury Pipe Flow Turbulent Characteristics

Author: Flaherty, T. W., Eyler, L. L., Sesonske, A., Flaherty, T. W., Eyler, L. L., and Sesonske, A.

18. Brain charts for the human lifespan

Author: Armin Raznahan, Eric Courchesne, Andrea Parolin Jackowski, Kamen A. Tsvetanov, Cameron T. Ellis, R.C. Gur, Bin Bae J, Park Mtm, Pedro A. Valdes-Sosa, Simon N. Vandekar, Jacob W. Vogel, Juan Zhou, Machteld Marcelis, Kiho Im, Patricia Ellen Grant, Minhui Ouyang, Blesa Cabez M, Michael V. Lombardo, Sarah E. Morgan, James P. Boardman, Adamson C, Calhoun Vd, Delarue M, James H. Cole, Pichet Binette A, Roberto Toro, David H. Rowitch, Nynke A. Groenewold, Kevin M. Anderson, David T.W. Jones, Michael Schöll, Wang Ys, Aiden Corvin, R.E. Gur, Damien A. Fair, Gareth Ball, Herma Lina Schaare, Andrew Zalesky, Evdokia Anagnostou, Michael J. Meaney, Taki Y, Gareth J. Sullivan, Warrier, Petra E. Vértes, Chixiang Chen, Lisa T. Eyler, Wei Liao, Tomáš Paus, Jeremy A. Elman, Phillip McGuire, Hisham Ziauddeen, William S. Kremen, Etienne Vachon-Presseau, E.T. Bullmore, Christophe Tzourio, White, Hammill Cf, Mothersill D, Richard N. Henson, Jiang Qiu, Duncan E. Astle, Fabrice Crivello, Paul C. Fletcher, Chertavian C, Kim K, Jennifer Crosbie, Russell Schachar, Gabriel A. Devenyi, Manfred G. Kitzbichler, Tianye Jia, Trey Hedden, Sang Jae Lee, Ross D. Markello, Silke Kern, Ian M. Goodyer, Keith A. Johnson, Frauke Beyer, Bernard Mazoyer, A. Heinz, Sylvane Desrivières, Rosenberg, Gary Donohoe, Ong Mq, Alexander D. Edwards, Dan J. Stein, Nenad Medic, Zuo Xn, Travis T. Mallard, Peter Fonagy, Lindsay W. Victoria, Ingmar Skoog, Avram J. Holmes, Jason P. Lerch, Jed T. Elison, Jianfu Li, John H. Gilmore, Rosemary Holt, Caitlin K. Rollins, Carol E. Franz, Pedro Mario Pan, Saashi A Bedford, Yang N, Jonathan C Ipser, Richard A. I. Bethlehem, Tuulari Jj, Stolicyn A, Hua Huang, Bratislav Misic, Conor Liston, Ayub M, Lisa Ronan, Yeo Bt, Sophie Adler, Charles J. Lynch, Faith M. Gunning, Konrad Wagstyl, M. Mallar Chakravarty, John Suckling, Theodore D. Satterthwaite, Bharath Holla, Yap Seng Chong, Jinglei Lv, Jakob Seidlitz, Niall J Bourke, Xinlei Qian, Simon Baron-Cohen, Cynthia M. Ortinau, Deirel Paz Linares, Thyreau B, René S. Kahn, Aaron P. Schultz, Vanessa Cropley, Eric Westman, Mitchell Valdés-Sosa, Rik Ossenkoppele, André Zugman, Hasse Karlsson, Sylvia Villeneuve, Katja Heuer, Di Biase Ma, Margaret L. Westwater, Sofie L. Valk, David J. Sharp, Brigitte Landeau, Matthew Borzage, Kirsten A. Donald, Timothy Rittman, Richard Beare, Giovanni Abrahão Salum, Gunter Schumann, Ryuta Kawashima, Romero-Garcia R, John Blangero, Yun Hj, Russel T. Shinohara, Nicolas Crossley, Simon K. Warfield, Karen Pierce, George S. Alexopoulos, Katharine Dunlop, David C. Glahn, Francois Lalonde, Anqi Qiu, Lana Vasung, Gaël Chételat, Lídice Galán-García, Clifford R. Jack, Reisa A. Sperling, Anna Zettergren, Elizabeth Kelley, Arno Villringer, Andrea Mechelli, Benegal, Aaron Alexander-Bloch, Nicholas B. Turk-Browne, van Amelsvoort T, John D. Lewis, Heather C. Whalley, A. V. Witte, Zdenka Pausova, Joel T. Nigg, Heather J. Zar, Raymond J. Dolan, Christopher D. Smyser, Jay N. Giedd, Lena Palaniyappan, Ali Gholipour, Areces-Gonzalez A, Peter B. Jones, Jacqueline Hoare, Oskar Hansson, Linnea Karlsson, C Pantelis, Paly L, Bonnie Auyeung, Jorge Bosch-Bayard, Bethlehem, Richard [0000-0002-0714-0685], White, Simon [0000-0001-8642-7037], Astle, Duncan [0000-0002-7042-5392], Baron-Cohen, Simon [0000-0001-9217-2544], Henson, Rik [0000-0002-0712-2639], Jones, Peter [0000-0002-0387-880X], Kitzbichler, Manfred [0000-0002-4494-0753], Rittman, Timothy [0000-0003-1063-6937], Rowitch, David [0000-0002-0079-0060], Tsvetanov, Kamen A. [0000-0002-3178-6363], Westwater-Wozniak, Margaret [0000-0002-2918-0979], Ziauddeen, Hisham [0000-0003-4044-1719], Apollo - University of Cambridge Repository, British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, University of Cambridge [UK] (CAM), University of Pennsylvania, Yale University [New Haven], Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Child and Adolescent Psychiatry Department [AP- HP Hôpital Robert Debré], AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Neuroscience - Department of Neuroscience, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Neurology, Amsterdam Neuroscience - Neurodegeneration, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND, The PREVENT-AD Research Group, VETSA, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Auyeung, B.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Baron-Cohen, S.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bedford, S. A.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Holt, R.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Lombardo, M. V.] Univ Cambridge, Dept Psychiat, Autism Res Ctr, Cambridge, England, [Bethlehem, R. A. I.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Brain Mapping Unit, Cambridge, England, [Seidlitz, J.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Vogel, J. W.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. E.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Gur, R. C.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Jackowski, A. P.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Univ Penn, Dept Psychiat, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia, Dept Child & Adolescent Psychiat & Behav Sci, Philadelphia, PA 19104 USA, [Seidlitz, J.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Chertavian, C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. E.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Gur, R. C.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [Alexander-Bloch, A. F.] Childrens Hosp Philadelphia & Penn Med, Lifespan Brain Inst, Philadelphia, PA USA, [White, S. R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Goodyer, I. M.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Henson, R. N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Jones, P. B.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Kitzbichler, M. G.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Medic, N.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Morgan, S. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Romero-Garcia, R.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ronan, L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Suckling, J.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Vertes, P. E.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Warrier, V.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Westwater, M. L.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Ziauddeen, H.] Univ Cambridge, Dept Psychiat, Cambridge, England, [Bullmore, E. T.] Univ Cambridge, Dept Psychiat, Cambridge, England, [White, S. R.] Univ Cambridge, MRC Biostat Unit, Cambridge, England, [Vogel, J. W.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Anderson, K. M.] Yale Univ, Dept Psychol, New Haven, CT USA, [Ellis, C. T.] Yale Univ, Dept Psychol, New Haven, CT USA, [Turk-Browne, N. B.] Yale Univ, Dept Psychol, New Haven, CT USA, [Adamson, C.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Ball, G.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Beare, R.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Jackowski, A. 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E.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Henson, R. N.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Whalley, H. C.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Auyeung, B.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Pausova, Z.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Ayub, M.] Queens Univ, Dept Psychiat, Ctr Neurosci Studies, Kingston, ON, Canada, [Ayub, M.] UCL, Mental Hlth Neurosci Res Dept, Div Psychiat, London, England, [Bae, J.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Ball, G.] Univ Melbourne, Dept Paediat, Melbourne, Vic, Australia, [Baron-Cohen, S.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge Lifetime Asperger Syndrome Serv CLASS, Cambridge, England, [Benegal, V.] Natl Inst Mental Hlth & Neurosci NIMHANS, Ctr Addict Med, Bengaluru, India, [Beyer, F.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Villringer, A.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Witte, A. V.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Blangero, J.] Univ Texas Rio Grande Valley, South Texas Diabet & Obes Inst, Dept Human Genet, Edinburg, TX USA, [Blesa Cabez, M.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Boardman, J. P.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Sullivan, G.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Borzage, M.] Univ Southern Calif, Childrens Hosp Los Angeles, Keck Sch Med, Fetal & Neonatal Inst,Div Neonatol,Dept Pediat, Los Angeles, CA 90007 USA, [Bosch-Bayard, J. F.] Montreal Neurol Inst, Ludmer Ctr Neuroinformat & Mental Hlth, McGill Ctr Integrat Neurosci, Montreal, PQ, Canada, [Bosch-Bayard, J. F.] McGill Univ, Montreal, PQ, Canada, [Chakravarty, M. 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A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Lv, J.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Zalesky, A.] Univ Melbourne, Melbourne Neuropsychiat Ctr, Melbourne, Vic, Australia, [Hammill, C. F.] Hosp Sick Children, Toronto, ON, Canada, [Schachar, R. 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A.] Queens Univ, Ctr Neurosci Studies, Dept Psychiat, Kingston, ON, Canada, [Kern, S.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Skoog, I.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Zettergren, A.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Kern, S.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Skoog, I.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Kim, K. W.] Seoul Natl Univ, Dept Brain & Cognit Sci, Coll Nat Sci, Seoul, South Korea, [Kim, K. W.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Kim, K. W.] Seoul Natl Univ, Dept Psychiat, Coll Med, Seoul, South Korea, [Kim, K. W.] SNU MRC, Inst Human Behav Med, Seoul, South Korea, [Lalonde, F.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Raznahan, A.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Lee, S.] Seoul Natl Univ, Coll Nat Sci, Dept Brain & Cognit Sci, Seoul, South Korea, [Lerch, J.] Univ Toronto, Dept Med Biophys, Toronto, ON, Canada, [Lerch, J.] Univ Oxford, Nuffield Dept Clin Neurosci, FMRIB, Wellcome Ctr Integrat Neuroimaging, Oxford, England, [Lewis, J. D.] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada, [Li, J.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liao, W.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Valdes-Sosa, P. 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E.] Alan Turing Inst, London, England, [Mothersill, D.] Natl Coll Ireland, Sch Business, Dept Psychol, Dublin, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Sch Psychol, Galway, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Ctr Neuroimaging & Cognit Genom, Galway, Ireland, [Mothersill, D.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Nigg, J.] Oregon Hlth & Sci Univ, Dept Psychiat, Sch Med, Portland, OR 97201 USA, [Ong, M. Q. W.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Qian, X.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Zhou, J. 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M.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Zugman, A.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Pantelis, C.] Univ Melbourne, Dept Psychiat, Melbourne Neuropsychiat Ctr, Carlton, Vic, Australia, [Pantelis, C.] Melbourne Hlth, Carlton, Vic, Australia, [Pantelis, C.] Univ Melbourne, Melbourne Sch Engn, Parkville, Vic, Australia, [Pantelis, C.] Florey Inst Neurosci & Mental Hlth, Parkville, Vic, Australia, [Park, M. M.] Western Univ, Schulich Sch Med & Dent, Dept Psychiat, London, ON, Canada, [Rollins, C. K.] Univ Montreal, Dept Psychiat, Fac Med, Montreal, PQ, Canada, [Rollins, C. K.] Univ Montreal, CHU St Justine, Montreal, PQ, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychiat, Toronto, ON, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Physiol, Toronto, ON, Canada, [Rosenberg, M. 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D.] Univ Chicago, Dept Psychol, 5848 S Univ Ave, Chicago, IL 60637 USA, [Rosenberg, M. D.] Univ Chicago, Inst Neurosci, Chicago, IL USA, [Rowitch, D. H.] Univ Cambridge, Dept Paediat, Cambridge, England, [Rowitch, D. H.] Univ Cambridge, Wellcome MRC Cambridge Stem Cell Inst, Cambridge, England, [Salum, G. A.] Univ Fed Rio Grande Sul UFRGS, Hosp Clin Porto Alegre, Dept Psychiat, Porto Alegre, RS, Brazil, [Salum, G. A.] Natl Inst Dev Psychiat INPD, Sao Paulo, Brazil, [Schaare, H. L.] Max Planck Inst Human Cognit & Brain Sci, Otto Hahn Grp Cognit Neurogenet, Leipzig, Germany, [Schaare, H. L.] Res Ctr Juelich, Inst Neurosci & Med INM 7 Brain & Behav, Julich, Germany, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Charlestown, MA USA, [Schumann, G.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Ctr Populat Neurosci & Stratified Med PONS, Shanghai, Peoples R China, [Schumann, G.] Charite Campus Mitte, Dept Psychiat & Psychotherapy, Charite Mental Hlth, PONS Ctr, Berlin, Germany, [Scholl, M.] Univ Gothenburg, Wallenberg Ctr Mol & Translat Med, Gothenburg, Sweden, [Scholl, M.] Univ Gothenburg, Dept Psychiat & Neurochem, Gothenburg, Sweden, [Scholl, M.] UCL, Queens Sq Inst Neurol, Dementia Res Ctr, London, England, [Sharp, D.] UK Dementia Res Inst, Care Res & Technol Ctr, London, England, [Shinohara, R. T.] Univ Penn, Perelman Sch Med, Dept Radiol, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Neurol, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Pediat, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Radiol, St Louis, MO 63110 USA, [Stein, D. J.] Univ Cape Town, Dept Psychiat, SA MRC Unit Risk & Resilience Mental Disorders, Cape Town, South Africa, [Stein, D. J.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Stolicyn, A.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Whalley, H. C.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Toro, R.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Univ Paris 05, Ctr Res & Interdisciplinar CRI, Paris, France, [Tsvetanov, K. A.] Univ Cambridge, Dept Psychol, Cambridge, England, [Turk-Browne, N. B.] Yale Univ, Wu Tsai Inst, New Haven, CT USA, [Tuulari, J. J.] Univ Turku, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Turku Coll Sci Med & Technol, Turku, Finland, [Tzourio, C.] Univ Bordeaux, Bordeaux Populat Hlth Res Ctr, CHU Bordeaux, U1219,INSERM, Bordeaux, France, [Vachon-Presseau, E.] McGill Univ, Fac Dent Med & Oral Hlth Sci, Montreal, PQ, Canada, [Valdes-Sosa, P. A.] McGill Univ, Alan Edwards Ctr Res Pain AECRP, Montreal, PQ, Canada, [Valk, S. L.] Forschungszentrum Julich, Inst Neurosci & Med 7, Julich, Germany, [Valk, S. L.] Max Planck Inst Human Cognit & Brain Sci, Leipzig, Germany, [van Amelsvoort, T.] Maastricht Univ, Dept Psychiat & Neurosychol, Maastricht, Netherlands, [Vandekar, S. N.] Vanderbilt Univ, Dept Biostat, 221 Kirkland Hall, Nashville, TN 37235 USA, [Villeneuve, S.] Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA, [Villringer, A.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Witte, A. V.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Zuo, X. N.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Wang, Y. S.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yeo, B.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Wang, Y. S.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Wang, Y. S.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Yang, N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Zuo, X. N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Wang, Y. S.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Yang, N.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Westman, E.] Karolinska Inst, Ctr Alzheimer Res, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden, [Witte, A. V.] Univ Leipzig, CRC 1052 Obes Mech, Fac Med, Leipzig, Germany, [Zhou, J. H.] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat MR Res, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, N1 Inst Hlth, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Inst Digital Med, Singapore, Singapore, [Yun, H.] Natl Univ Singapore, Integrat Sci & Engn Programme ISEP, Singapore, Singapore, [Zar, H. J.] Univ Melbourne, Dept Biomed Engn, Melbourne, Vic, Australia, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat Magnet Resonance Res, Singapore, Singapore, [Ziauddeen, H.] Univ Cambridge, Wellcome Trust MRC Inst Metab Sci, Cambridge, England, [Zugman, A.] NIMH, NIH, Bethesda, MD 20892 USA, [Zugman, A.] Escola Paulista Med, Dept Psychiat, Sao Paulo, Brazil, [Zuo, X. N.] Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning, Peoples R China, British Academy Postdoctoral fellowship, NIMH, UKRI Medical Research Council, NIHR Cambridge Biomedical Research Centre, NIHR Senior Investigator award, MRC research infrastructure award, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Ontario Brain Institute
Subjects: 631/378/2649, OpenPain Project, KNE96, Growth, Psychiatric-disorders, DISEASE, 3R-BRAIN, Brain charts, MRI Brain, OASIS-3, Disease, CCNP, 631/378/2571, UMN BCP, Multidisciplinary, medicine.diagnostic_test, PSYCHIATRIC-DISORDERS, article, Brain, Human brain, ASSOCIATION, Magnetic Resonance Imaging, Harvard Aging Brain Study, The Mayo Clinic Study of Aging, NSPN, medicine.anatomical_structure, GROWTH, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ddc:500, BURDEN, WHITE-MATTER, FinnBrain, Harvard Aging Brain Study, Organization, Mri, MRI, medicine.medical_specialty, Concurrent validity, MODELS, Cam-CAN, Longevity, CALM Team, POND, Neuroimaging, Burden, ORGANIZATION, AIBL, The PREVENT-AD Research Group, VETSA, Cortical thickness, Association, Physical medicine and rehabilitation, FinnBrain, IMAGEN, KNE96, White-matter, medicine, Humans, ASRB, 631/378/1689, COBRE, business.industry, 631/378/2611, Brain morphometry, Neurosciences, Alzheimer’s Disease Repository Without Borders Investigators, Magnetic resonance imaging, Alzheimer’s Disease Neuroimaging Initiative, Anthropometry, Body Height, Brain growth, Birth, 59/57, Normative, IMAGEN, ENIGMA Developmental Brain Age working group, NSPN, business, CCNP, 3R-BRAIN, CORTICAL THICKNESS, Developing Human Connectome Project, ENIGMA Developmental Brain Age working group, The PREVENT-AD Research Group, VETSA, Bullmore, E.T
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.
Published: 2022

19. What we learn about bipolar disorder from large-scale neuroimaging

Author: Christian K. Tamnes, Bartholomeus C M Haarman, Jair C. Soares, Ole A. Andreassen, Viola Oertel, Theodore D. Satterthwaite, G. Tronchin, Michael Stäblein, Bradley J. MacIntosh, Melissa Pauling, Christopher R.K. Ching, Daniel H. Wolf, Dick J. Veltman, Ingrid Agartz, Bernhard T. Baune, Salvador Sarró, Mon-Ju Wu, Scott C Fears, Eduard Vieta, Melissa J. Green, Neeltje E.M. van Haren, Yann Quidé, Erlend Bøen, Yash Patel, Igor Nenadic, Martin Alda, Lisa T. Eyler, Arnaud Pouchon, Danai Dima, Tomáš Paus, Irene Bollettini, Torbjørn Elvsåshagen, Rachel M. Brouwer, Lakshmi N. Yatham, Michael Bauer, Caterina del Mar Bonnín, C. McDonald, Udo Dannlowski, Bronwyn Overs, Edith Pomarol-Clotet, Cristian Vargas Upegui, Oliver Gruber, Henricus G. Ruhé, Márcio Gerhardt Soeiro-de-Souza, Edouard Duchesnay, Hilary P. Blumberg, Tilo Kircher, Miho Ota, Michael Berk, Christoph Abé, Andreas Jansen, Kang Sim, Heather C. Whalley, Derrek P. Hibar, Roel A. Ophoff, Georgios V Thomaidis, Henrik Walter, Sophia Frangou, Michèle Wessa, Dara M. Cannon, Cara M. Altimus, Allison C. Nugent, Rodrigo Machado-Vieira, Orwa Dandash, Marcella Bellani, Unn K. Haukvik, Philip B. Mitchell, Ling-Li Zeng, Christian Knöchel, Jose Manuel Goikolea, Sonja M C de Zwarte, Francesco Benedetti, Sara Poletti, Janice M. Fullerton, Carlos A. Zarate, Aart H. Schene, Dan J. Stein, Chantal Henry, Tristram A. Lett, Mikael Landén, Daniel L Pham, Paolo Brambilla, Silvia Alonso-Lana, Sophia I. Thomopoulos, Carlos López-Jaramillo, Tomas Hajek, Bernd Kramer, G. Delvecchio, Maria M. Rive, Lars T. Westlye, Erick J. Canales-Rodríguez, Victoria L. Ives-Deliperi, Dominik Grotegerd, Beny Lafer, Abraham Nunes, Carrie E. Bearden, Raymond Salvador, Joaquim Radua, Amy C Bilderbeck, Xavier Caseras, Paul M. Thompson, Jorge R. C. Almeida, Pauline Favre, Gloria Roberts, David C. Glahn, Dag Alnæs, Julian A Pineda-Zapata, Tiril P. Gurholt, Mircea Polosan, Josselin Houenou, Fabiano G. Nery, Leila Nabulsi, Mary L. Phillips, Fleur M. Howells, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Ching, C. R. K., Hibar, D. P., Gurholt, T. P., Nunes, A., Thomopoulos, S. I., Abe, C., Agartz, I., Brouwer, R. M., Cannon, D. M., de Zwarte, S. M. C., Eyler, L. T., Favre, P., Hajek, T., Haukvik, U. K., Houenou, J., Landen, M., Lett, T. A., Mcdonald, C., Nabulsi, L., Patel, Y., Pauling, M. E., Paus, T., Radua, J., Soeiro-de-Souza, M. G., Tronchin, G., van Haren, N. E. M., Vieta, E., Walter, H., Zeng, L. -L., Alda, M., Almeida, J., Alnaes, D., Alonso-Lana, S., Altimus, C., Bauer, M., Baune, B. T., Bearden, C. E., Bellani, M., Benedetti, F., Berk, M., Bilderbeck, A. C., Blumberg, H. P., Boen, E., Bollettini, I., del Mar Bonnin, C., Brambilla, P., Canales-Rodriguez, E. J., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Diaz-Zuluaga, A. M., Dima, D., Duchesnay, E., Elvsashagen, T., Fears, S. C., Frangou, S., Fullerton, J. M., Glahn, D. C., Goikolea, J. M., Green, M. J., Grotegerd, D., Gruber, O., Haarman, B. C. M., Henry, C., Howells, F. M., Ives-Deliperi, V., Jansen, A., Kircher, T. T. J., Knochel, C., Kramer, B., Lafer, B., Lopez-Jaramillo, C., Machado-Vieira, R., Macintosh, B. J., Melloni, E. M. T., Mitchell, P. B., Nenadic, I., Nery, F., Nugent, A. C., Oertel, V., Ophoff, R. A., Ota, M., Overs, B. J., Pham, D. L., Phillips, M. L., Pineda-Zapata, J. A., Poletti, S., Polosan, M., Pomarol-Clotet, E., Pouchon, A., Quide, Y., Rive, M. M., Roberts, G., Ruhe, H. G., Salvador, R., Sarro, S., Satterthwaite, T. D., Schene, A. H., Sim, K., Soares, J. C., Stablein, M., Stein, D. J., Tamnes, C. K., Thomaidis, G. V., Upegui, C. V., Veltman, D. J., Wessa, M., Westlye, L. T., Whalley, H. C., Wolf, D. H., Wu, M. -J., Yatham, L. N., Zarate, C. A., Thompson, P. M., and Andreassen, O. A.
Subjects: mega-analysis, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], cortical surface area, Review Article, 0302 clinical medicine, Manic-depressive illness, Multicenter Studies as Topic, Spectrum disorder, Review Articles, bipolar disorder, Cerebral Cortex, Trastorn bipolar, neuroimaging, Radiological and Ultrasound Technology, 05 social sciences, ENIGMA, HUMAN BRAIN, Magnetic Resonance Imaging, psychiatry, 3. Good health, Neurology, Meta-analysis, Scale (social sciences), Anatomy, Psychology, Clinical risk factor, Clinical psychology, MRI, MAJOR PSYCHIATRIC-DISORDERS, Schizoaffective disorder, 050105 experimental psychology, 03 medical and health sciences, Magnetic resonance imaging, Neuroimaging, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, Imatges per ressonància magnètica, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Bipolar disorder, HIPPOCAMPAL VOLUMES, mega‐analysis, GRAY-MATTER VOLUME, SPECTRUM DISORDER, volume, DIABETES-MELLITUS, cortical thickness, COGNITIVE IMPAIRMENT, medicine.disease, Mental illness, meta-analysis, meta‐analysis, RC0321, Neurology (clinical), SCHIZOAFFECTIVE DISORDER, PSYCHOTIC FEATURES, 030217 neurology & neurosurgery
Abstract: MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.
Published: 2022

20. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals

Author: Eduard Vieta, Jose Manuel Goikolea, Joaquim Raduà, Janice M. Fullerton, Lakshmi N. Yatham, Peter R. Schofield, Carlos López-Jaramillo, Tomas Hajek, Edith Pomarol-Clotet, Henk Temmingh, Francesco Benedetti, Ulrik Fredrik Malt, Erlend Bøen, Roel A. Ophoff, Bartholomeus C M Haarman, Cristian Vargas, Kang Sim, Katharina Thiel, Ole A. Andreassen, Tim Hahn, Lisa T. Eyler, Philip B. Mitchell, Christopher R.K. Ching, Axel Krug, Jonathan Repple, Annabel Vreeker, Dara M. Cannon, Sandra Meier, Colm McDonald, Holly Van Gestel, Hannah Lemke, Maike Richter, Caterina del Mar Bonnín, Udo Dannlowski, Tilo Kircher, Martin Alda, Mikael Landén, Janik Goltermann, Torbjørn Elvsåshagen, Genevieve McPhilemy, Jonathan Savitz, Susanne Meinert, Igor Nenadic, Simon Schmitt, Bronwyn Overs, Katharina Brosch, Dan J. Stein, Raymond Salvador, Dominik Grotegerd, Nils Opel, Martin Ingvar, Sean R. McWhinney, Erick J. Canales-Rodríguez, Elena Mazza, Gloria Roberts, Paul M. Thompson, Neeltje E.M. van Haren, Tiana Borgers, Fiona M. Martyn, Frederike Stein, Julia-Katharina Pfarr, Benny Liberg, Julian A Pineda-Zapata, Christoph Abé, Lena Waltemate, Tina Meller, Kai Ringwald, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Rayus Kuplicki, Leila Nabulsi, Fleur M. Howells, Psychiatry, Child and Adolescent Psychiatry / Psychology, Mcwhinney, S. R., Abe, C., Alda, M., Benedetti, F., Boen, E., del Mar Bonnin, C., Borgers, T., Brosch, K., Canales-Rodriguez, E. J., Cannon, D. M., Dannlowski, U., Diaz-Zuluaga, A. M., Elvsashagen, T., Eyler, L. T., Fullerton, J. M., Goikolea, J. M., Goltermann, J., Grotegerd, D., Haarman, B. C. M., Hahn, T., Howells, F. M., Ingvar, M., Kircher, T. T. J., Krug, A., Kuplicki, R. T., Landen, M., Lemke, H., Liberg, B., Lopez-Jaramillo, C., Malt, U. F., Martyn, F. M., Mazza, E., Mcdonald, C., Mcphilemy, G., Meier, S., Meinert, S., Meller, T., Melloni, E. M. T., Mitchell, P. B., Nabulsi, L., Nenadic, I., Opel, N., Ophoff, R. A., Overs, B. J., Pfarr, J. -K., Pineda-Zapata, J. A., Pomarol-Clotet, E., Radua, J., Repple, J., Richter, M., Ringwald, K. G., Roberts, G., Salvador, R., Savitz, J., Schmitt, S., Schofield, P. R., Sim, K., Stein, D. J., Stein, F., Temmingh, H. S., Thiel, K., van Haren, N. E. M., Gestel, H. V., Vargas, C., Vieta, E., Vreeker, A., Waltemate, L., Yatham, L. N., Ching, C. R. K., Andreassen, O., Thompson, P. M., Hajek, T., and Clinical Cognitive Neuropsychiatry Research Program (CCNP)
Subjects: medicine.medical_specialty, Bipolar Disorder, Hippocampus, Amygdala, Article, Body Mass Index, Cellular and Molecular Neuroscience, Lateral ventricles, SDG 3 - Good Health and Well-being, Neuroimaging, Internal medicine, medicine, Humans, Risk factor, Molecular Biology, business.industry, Brain, medicine.disease, Magnetic Resonance Imaging, Obesity, Comorbidity, Psychiatry and Mental health, medicine.anatomical_structure, Cardiology, business, Body mass index, Neuroscience
Abstract: Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.
Published: 2021

21. Modeling and analysis of ORNL horizontal storage tank mobilization and mixing

Author: Eyler, L
Published: 1994
Full Text: View/download PDF

22. Computer modeling of ORNL storage tank sludge mobilization and mixing

Author: Eyler, L
Published: 1993
Full Text: View/download PDF

23. Predictions of convective losses from a solar cavity receiver

Author: Eyler, L.
Published: 1979

24. Evaluation of coarse coal hydrotransport data and predictive models

Author: Eyler, L.
Published: 1980

25. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author: Jess E. Sussmann, Unn K. Haukvik, Michèle Wessa, Pedro G.P. Rosa, Philip B. Mitchell, Trine Vik Lagerberg, Henk Temmingh, Christoph Abé, Tomas Hajek, Edith Pomarol-Clotet, Sonya Foley, Chantal Henry, Neda Jahanshad, Martin Alda, Franz Hozer, Harald Kugel, Mircea Polosan, Jacques Stout, Erick J. Canales-Rodríguez, Pauline Favre, Peter R. Schofield, Josselin Houenou, Michael Deppe, Gloria Roberts, Heather C. Whalley, Tiffany M. Chaim-Avancini, Norma Verdolini, Clara Alloza, Ole A. Andreassen, Marcus V. Zanetti, Salvador Sarró, Mauricio H. Serpa, Melissa Pauling, Jonathan Repple, R. Salvador, Geraldo F. Busatto, Annerine Roos, Christopher R.K. Ching, Dominik Grotegerd, Marion Leboyer, Udo Dannlowski, Lisa T. Eyler, Xavier Caseras, Francesco Benedetti, Kang Sim, Silvia Alonso-Lana, Samuel Sarrazin, Julia Linke, Bernhard T. Baune, Jean-François Mangin, Andrew M. McIntosh, Mar Fatjó-Vilas, Edouard Duchesnay, Eduard Vieta, Dan J. Stein, Stephen M. Lawrie, Paul M. Thompson, Fleur M. Howells, Elisa M T Melloni, Favre, P., Pauling, M., Stout, J., Hozer, F., Sarrazin, S., Abe, C., Alda, M., Alloza, C., Alonso-Lana, S., Andreassen, O. A., Baune, B. T., Benedetti, F., Busatto, G. F., Canales-Rodriguez, E. J., Caseras, X., Chaim-Avancini, T. M., Ching, C. R. K., Dannlowski, U., Deppe, M., Eyler, L. T., Fatjo-Vilas, M., Foley, S. F., Grotegerd, D., Hajek, T., Haukvik, U. K., Howells, F. M., Jahanshad, N., Kugel, H., Lagerberg, T. V., Lawrie, S. M., Linke, J. O., Mcintosh, A., Melloni, E. M. T., Mitchell, P. B., Polosan, M., Pomarol-Clotet, E., Repple, J., Roberts, G., Roos, A., Rosa, P. G. P., Salvador, R., Sarro, S., Schofield, P. R., Serpa, M. H., Sim, K., Stein, D. J., Sussmann, J. E., Temmingh, H. S., Thompson, P. M., Verdolini, N., Vieta, E., Wessa, M., Whalley, H. C., Zanetti, M. V., Leboyer, M., Mangin, J. -F., Henry, C., Duchesnay, E., and Houenou, J.
Subjects: Pathology, medicine.medical_specialty, Corpus callosum, Article, White matter, 03 medical and health sciences, 0302 clinical medicine, Fractional anisotropy, Cingulum (brain), Medicine, Manic-depressive illness, Bipolar disorder, Pharmacology, Trastorn bipolar, business.industry, Diagnostic markers, Anisotropia, Translational research, medicine.disease, 030227 psychiatry, Psychiatry and Mental health, medicine.anatomical_structure, Meta-analysis, Anisotropy, business, 030217 neurology & neurosurgery, Diffusion MRI, Tractography
Abstract: Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD);\ud however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical\ud heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with\ud BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered\ud individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean\ud fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and\ud meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD\ud compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr < 0.001) and\ud cingulum (right: R2 = 0.041, left: R2 = 0.040, Pcorr < 0.001). Lithium medication, later onset and short disease duration were related to\ud higher FA along multiple ROIs. Results of the meta-analysis showed similar effects. We demonstrated widespread WM abnormalities\ud in BD and highlighted that altered WM connectivity within the corpus callosum and the cingulum are strongly associated with BD.\ud These brain abnormalities could represent a biomarker for use in the diagnosis of BD. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
Published: 2019

26. TEMPEST: A three-dimensional time-dependence computer program for hydrothermal analysis: Volume 1, Numerical methods and input instructions: Revision 2

Author: Eyler, L
Published: 1989
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27. A 3D approach to understanding heterogeneity in early developing autisms.

Author: Mandelli V, Severino I, Eyler L, Pierce K, Courchesne E, and Lombardo MV
Subjects: Humans, Child, Preschool, Female, Male, Child, Phenotype, Imaging, Three-Dimensional, Autistic Disorder diagnostic imaging, Autistic Disorder diagnosis
Abstract: Background: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology., Methods: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work., Results: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms., Limitations: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures., Conclusions: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance., (© 2024. The Author(s).)
Published: 2024
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28. Differences in regional brain structure in toddlers with autism are related to future language outcomes.

Author: Duan K, Eyler L, Pierce K, Lombardo MV, Datko M, Hagler DJ, Taluja V, Zahiri J, Campbell K, Barnes CC, Arias S, Nalabolu S, Troxel J, Ji P, and Courchesne E
Subjects: Humans, Male, Female, Child, Preschool, Infant, Language, Language Development, Magnetic Resonance Imaging, Brain diagnostic imaging, Brain pathology, Autistic Disorder pathology, Autistic Disorder diagnostic imaging
Abstract: Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability., (© 2024. The Author(s).)
Published: 2024
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29. Measures of empathy and compassion: A scoping review.

Author: Vieten C, Rubanovich CK, Khatib L, Sprengel M, Tanega C, Polizzi C, Vahidi P, Malaktaris A, Chu G, Lang AJ, Tai-Seale M, Eyler L, and Bloss C
Subjects: Psychometrics, Empathy, Mindfulness
Abstract: Evidence to date indicates that compassion and empathy are health-enhancing qualities. Research points to interventions and practices involving compassion and empathy being beneficial, as well as being salient outcomes of contemplative practices such as mindfulness. Advancing the science of compassion and empathy requires that we select measures best suited to evaluating effectiveness of training and answering research questions. The objective of this scoping review was to 1) determine what instruments are currently available for measuring empathy and compassion, 2) assess how and to what extent they have been validated, and 3) provide an online tool to assist researchers and program evaluators in selecting appropriate measures for their settings and populations. A scoping review and broad evidence map were employed to systematically search and present an overview of the large and diverse body of literature pertaining to measuring compassion and empathy. A search string yielded 19,446 articles, and screening resulted in 559 measure development or validation articles reporting on 503 measures focusing on or containing subscales designed to measure empathy and/or compassion. For each measure, we identified the type of measure, construct being measured, in what context or population it was validated, response set, sample items, and how many different types of psychometrics had been assessed for that measure. We provide tables summarizing these data, as well as an open-source online interactive data visualization allowing viewers to search for measures of empathy and compassion, review their basic qualities, and access original citations containing more detail. Finally, we provide a rubric to help readers determine which measure(s) might best fit their context., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Vieten et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Published: 2024
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30. Learning to Practice Compassionate Care: Medical Students Discuss Their Most Memorable Lessons.

Author: Schairer CE, Tutjer J, Cannavino C, Mobley WC, Eyler L, and Bloss CS
Abstract: Compassion in interactions between physicians and patients can have a therapeutic effect independent of the technical medical treatment provided. However, training physicians to effectively communicate compassion is challenging. This study explores how medical students experienced training focused on interacting with patients by examining students' reports of particularly memorable lessons. Six focus groups were conducted with medical students (total n = 48) in their fourth year of training. We report on responses from students to the question, "What was the most memorable lesson you have learned about interacting with patients?" Students discussed lessons aimed at patient-centered physical navigation, interpersonal navigation, and perspective taking. Concerns were raised that navigation techniques felt inauthentic and that perspective taking was too time consuming to be sustainable in actual practice. While perspective-taking exercises should motivate medical students to treat every patient with dignity by demonstrating the complexity of others' lives, if students assume that full understanding is a prerequisite to delivery of compassionate care, they may dismiss explicit techniques of patient-centered care as inauthentic and perceive compassion and efficiency as mutually exclusive., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)
Published: 2022
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31. Covariance and Correlation Analysis of Resting State Functional Magnetic Resonance Imaging Data Acquired in a Clinical Trial of Mindfulness-Based Stress Reduction and Exercise in Older Individuals.

Author: Snyder AZ, Nishino T, Shimony JS, Lenze EJ, Wetherell JL, Voegtle M, Miller JP, Yingling MD, Marcus D, Gurney J, Rutlin J, Scott D, Eyler L, and Barch D
Abstract: We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Snyder, Nishino, Shimony, Lenze, Wetherell, Voegtle, Miller, Yingling, Marcus, Gurney, Rutlin, Scott, Eyler and Barch.)
Published: 2022
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32. Atypical genomic cortical patterning in autism with poor early language outcome.

Author: Lombardo MV, Eyler L, Pramparo T, Gazestani VH, Hagler DJ Jr, Chen CH, Dale AM, Seidlitz J, Bethlehem RAI, Bertelsen N, Barnes CC, Lopez L, Campbell K, Lewis NE, Pierce K, and Courchesne E
Abstract: Cortical regionalization develops via genomic patterning along anterior-posterior (A-P) and dorsal-ventral (D-V) gradients. Here, we find that normative A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT), present in typically developing and autistic toddlers with good early language outcome, is absent in autistic toddlers with poor early language outcome. Autistic toddlers with poor early language outcome are instead specifically characterized by a secondary and independent genomic patterning effect on CT. Genes involved in these effects can be traced back to midgestational A-P and D-V gene expression gradients and different prenatal cell types (e.g., progenitor cells and excitatory neurons), are functionally important for vocal learning and human-specific evolution, and are prominent in prenatal coexpression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be explained by atypical genomic cortical patterning starting in prenatal development, which may detrimentally affect later regional functional specialization and circuit formation.
Published: 2021
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33. Altered Neuronal Support and Inflammatory Response in Bipolar Disorder Patient-Derived Astrocytes.

Author: Vadodaria KC, Mendes APD, Mei A, Racha V, Erikson G, Shokhirev MN, Oefner R, Heard KJ, McCarthy MJ, Eyler L, Kelsoe JR, Santos R, Marchetto MC, and Gage FH
Subjects: Coculture Techniques, Humans, Induced Pluripotent Stem Cells drug effects, Induced Pluripotent Stem Cells metabolism, Induced Pluripotent Stem Cells pathology, Interleukin-1beta pharmacology, Interleukin-6 metabolism, Neuroglia drug effects, Neuroglia pathology, Neurons drug effects, Neurons metabolism, Astrocytes pathology, Bipolar Disorder pathology, Inflammation pathology, Neurons pathology
Abstract: Bipolar disorder (BD) is characterized by cyclical mood shifts. Studies indicate that BD patients have a peripheral pro-inflammatory state and alterations in glial populations in the brain. We utilized an in vitro model to study inflammation-related phenotypes of astrocytes derived from induced pluripotent stem cells (iPSCs) generated from BD patients and healthy controls. BD astrocytes showed changes in transcriptome and induced a reduction in neuronal activity when co-cultured with neurons. IL-1β-stimulated BD astrocytes displayed a unique inflammatory gene expression signature and increased secretion of IL-6. Conditioned medium from stimulated BD astrocytes reduced neuronal activity, and this effect was partially blocked by IL-6 inactivating antibody. Our results suggest that BD astrocytes are functionally less supportive of neuronal excitability and this effect is partially mediated by IL-6. We confirmed higher IL-6 in blood in a distinct cohort of BD patients, highlighting the potential role of astrocyte-mediated inflammatory signaling in BD neuropathology., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2021
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34. Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties.

Author: Lombardo MV, Eyler L, Moore A, Datko M, Carter Barnes C, Cha D, Courchesne E, and Pierce K
Subjects: Attention physiology, Autistic Disorder classification, Autistic Disorder psychology, Brain diagnostic imaging, Brain physiopathology, Brain Mapping methods, Child, Preschool, Developmental Disabilities diagnostic imaging, Developmental Disabilities physiopathology, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Autistic Disorder physiopathology, Eye Movements physiology, Nerve Net physiopathology, Neural Pathways physiology, Social Behavior
Abstract: Social visual engagement difficulties are hallmark early signs of autism (ASD) and are easily quantified using eye tracking methods. However, it is unclear how these difficulties are linked to atypical early functional brain organization in ASD. With resting state fMRI data in a large sample of ASD toddlers and other non-ASD comparison groups, we find ASD-related functional hypoconnnectivity between 'social brain' circuitry such as the default mode network (DMN) and visual and attention networks. An eye tracking-identified ASD subtype with pronounced early social visual engagement difficulties (GeoPref ASD) is characterized by marked DMN-occipito-temporal cortex (OTC) hypoconnectivity. Increased DMN-OTC hypoconnectivity is also related to increased severity of social-communication difficulties, but only in GeoPref ASD. Early and pronounced social-visual circuit hypoconnectivity is a key underlying neurobiological feature describing GeoPref ASD and may be critical for future social-communicative development and represent new treatment targets for early intervention in these individuals., Competing Interests: ML, LE, AM, MD, CC, DC, EC No competing interests declared, KP An invention disclosure form was filed by KP with the University of California, San Diego, on March 5, 2010., (© 2019, Lombardo et al.)
Published: 2019
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35. Individual differences in level of wisdom are associated with brain activation during a moral decision-making task.

Author: Thomas ML, Martin AS, Eyler L, Lee EE, Macagno E, Devereaux M, Chiong W, and Jeste DV
Subjects: Adult, Aged, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Psychometrics, Quality of Life, Brain physiology, Decision Making physiology, Individuality, Morals
Abstract: Introduction: Wisdom is reportedly associated with better health and quality of life. However, our knowledge of the neurobiology of wisdom is still in the early stages of development. We aimed to improve our understanding by correlating a psychometric measure of the trait with patterns of brain activation produced by a cognitive task theorized to be relevant to wisdom: moral decision-making. In particular, we aimed to determine whether individual differences in wisdom interact with moral task complexity in relation to brain activation., Methods: Participants were 39 community-dwelling men and women aged 27-76 years, who completed moral and nonmoral decision-making tasks while undergoing functional magnetic resonance imaging. Brain activation in select regions of interest was correlated with participants' scores on the San Diego Wisdom Scale (SD-WISE)., Results: Individual differences in wisdom were found to interact with brain response to moral versus nonmoral and moral personal versus impersonal dilemmas, particularly in regions in or near the default mode network. Persons with higher scores on the SD-WISE had less contrast between moral and nonmoral dilemmas and greater contrast between moral-personal and moral-impersonal dilemmas than individuals with lower SD-WISE scores., Conclusions: Results confirmed our hypothesis that individual differences in level of wisdom would interact with moral condition in relation to brain activation, and may underscore the relevance of considering one's own and others' actions and experiences in the context of wise thinking. Future studies are needed to replicate these findings and to examine specific neurocircuits., (© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.)
Published: 2019
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36. Optimization and validation of the EconomicClusters model for facilitating global health disparities research: Examples from Cameroon and Ghana.

Author: Eyler L, Hubbard A, and Juillard C
Subjects: Cameroon, Child, Cross-Sectional Studies, Demography, Economic Development, Ghana, Global Health, Humans, Poverty, Socioeconomic Factors, Healthcare Disparities economics, Healthcare Disparities standards, Models, Economic
Abstract: Health disparities research in low- and middle-income countries (LMICs) is hampered by the difficulty of measuring economic status in low-resource settings. We previously developed the EconomicClusters k-medoids clustering-based algorithm for defining population-specific economic models based on few Demographic and Health Surveys (DHS) assets. The algorithm previously defined a twenty-group economic model for Cameroon. The aims of this study are to optimize the functionality of our EconomicClusters algorithm and app based on collaborator feedback from early use of this twenty-group economic model, to test the validity of the model as a metric of economic status, and to assess the utility of the model in another LMIC context. We condense the twenty Cameroonian economic groups into fewer, ordinally-ranked, groups using agglomerative hierarchical clustering based on mean cluster child height-for-age Z-score (HAZ), women's literacy score, and proportion of children who are deceased. We develop an EconomicClusters model for Ghana consisting of five economic groups and rank these groups based on the same three variables. The proportion of variance in women's literacy score accounted for by the EconomicClusters model was 5-12% less than the proportion of variance accounted for by the DHS Wealth Index model. The proportion of the variance in child HAZ and proportion of children who are deceased accounted for by the EconomicClusters model was similar to (0.4-2.5% less than) the proportion of variance accounted for by the DHS Wealth Index model. The EconomicClusters model requires asking only five questions, as opposed to greater than twenty Wealth Index questions. The EconomicClusters algorithm and app could facilitate health disparities research in any country with DHS data by generating ordinally-ranked, population-specific economic models that perform nearly as well as the Wealth Index in evaluating variability in health and social outcomes based on wealth status but that are more feasible to assess in time-constrained settings., Competing Interests: The authors have declared that no competing interests exist.
Published: 2019
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37. Assessment of economic status in trauma registries: A new algorithm for generating population-specific clustering-based models of economic status for time-constrained low-resource settings.

Author: Eyler L, Hubbard A, and Juillard C
Subjects: Algorithms, Cluster Analysis, Developing Countries, Humans, Socioeconomic Factors, Wounds and Injuries therapy, Cost of Illness, Models, Theoretical, Registries, Trauma Centers economics, Wounds and Injuries economics
Abstract: Objectives: Low and middle-income countries (LMICs) and the world's poor bear a disproportionate share of the global burden of injury. Data regarding disparities in injury are vital to inform injury prevention and trauma systems strengthening interventions targeted towards vulnerable populations, but are limited in LMICs. We aim to facilitate injury disparities research by generating a standardized methodology for assessing economic status in resource-limited country trauma registries where complex metrics such as income, expenditures, and wealth index are infeasible to assess., Methods: To address this need, we developed a cluster analysis-based algorithm for generating simple population-specific metrics of economic status using nationally representative Demographic and Health Surveys (DHS) household assets data. For a limited number of variables, g, our algorithm performs weighted k-medoids clustering of the population using all combinations of g asset variables and selects the combination of variables and number of clusters that maximize average silhouette width (ASW)., Results: In simulated datasets containing both randomly distributed variables and "true" population clusters defined by correlated categorical variables, the algorithm selected the correct variable combination and appropriate cluster numbers unless variable correlation was very weak. When used with 2011 Cameroonian DHS data, our algorithm identified twenty economic clusters with ASW 0.80, indicating well-defined population clusters., Conclusions: This economic model for assessing health disparities will be used in the new Cameroonian six-hospital centralized trauma registry. By describing our standardized methodology and algorithm for generating economic clustering models, we aim to facilitate measurement of health disparities in other trauma registries in resource-limited countries., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)
Published: 2016
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38. Disrupted neural synchronization in toddlers with autism.

Author: Dinstein I, Pierce K, Eyler L, Solso S, Malach R, Behrmann M, and Courchesne E
Subjects: Adaptation, Psychological, Age Factors, Autistic Disorder complications, Case-Control Studies, Cerebral Cortex growth & development, Cerebral Cortex physiology, Child Development physiology, Child, Preschool, Humans, Infant, Language Development Disorders complications, Polysomnography, Reference Values, Sleep physiology, Autistic Disorder physiopathology, Cerebral Cortex physiopathology, Cortical Synchronization physiology, Functional Laterality physiology, Language Development Disorders physiopathology, Neurons physiology
Abstract: Autism is often described as a disorder of neural synchronization. However, it is unknown how early in development synchronization abnormalities emerge and whether they are related to the development of early autistic behavioral symptoms. Here, we show that disrupted synchronization is evident in the spontaneous cortical activity of naturally sleeping toddlers with autism, but not in toddlers with language delay or typical development. Toddlers with autism exhibited significantly weaker interhemispheric synchronization (i.e., weak "functional connectivity" across the two hemispheres) in putative language areas. The strength of synchronization was positively correlated with verbal ability and negatively correlated with autism severity, and it enabled identification of the majority of autistic toddlers (72%) with high accuracy (84%). Disrupted cortical synchronization, therefore, appears to be a notable characteristic of autism neurophysiology that is evident at very early stages of autism development., (Copyright © 2011 Elsevier Inc. All rights reserved.)
Published: 2011
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