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1. Brain charts for the human lifespan.

Author

Bethlehem, RAI, 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, Landeau, B, Bethlehem, RAI, 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

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. 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 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

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

3. Brain charts for the human lifespan.

Author

Bethlehem, RAI, 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, Landeau, B, Bethlehem, RAI, 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

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

4. 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

5. Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants

Author

Krishnan, ML, Wang, Z, Silver, M, Boardman, JP, Ball, G, Counsell, SJ, Walley, AJ, Montana, G, Edwards, AD, Krishnan, ML, Wang, Z, Silver, M, Boardman, JP, Ball, G, Counsell, SJ, Walley, AJ, Montana, G, and Edwards, AD

Abstract

BACKGROUND: The consequences of preterm birth are a major public health concern with high rates of ensuing multisystem morbidity, and uncertain biological mechanisms. Common genetic variation may mediate vulnerability to the insult of prematurity and provide opportunities to predict and modify risk. OBJECTIVE: To gain novel biological and therapeutic insights from the integrated analysis of magnetic resonance imaging and genetic data, informed by prior knowledge. METHODS: We apply our previously validated pathway-based statistical method and a novel network-based method to discover sources of common genetic variation associated with imaging features indicative of structural brain damage. RESULTS: Lipid pathways were highly ranked by Pathways Sparse Reduced Rank Regression in a model examining the effect of prematurity, and PPAR (peroxisome proliferator-activated receptor) signaling was the highest ranked pathway once degree of prematurity was accounted for. Within the PPAR pathway, five genes were found by Graph Guided Group Lasso to be highly associated with the phenotype: aquaporin 7 (AQP7), malic enzyme 1, NADP(+)-dependent, cytosolic (ME1), perilipin 1 (PLIN1), solute carrier family 27 (fatty acid transporter), member 1 (SLC27A1), and acetyl-CoA acyltransferase 1 (ACAA1). Expression of four of these (ACAA1, AQP7, ME1, and SLC27A1) is controlled by a common transcription factor, early growth response 4 (EGR-4). CONCLUSIONS: This suggests an important role for lipid pathways in influencing development of white matter in preterm infants, and in particular a significant role for interindividual genetic variation in PPAR signaling.

Published
2016

6. Testing the Sensitivity of Tract-Based Spatial Statistics to Simulated Treatment Effects in Preterm Neonates

Author

Harel, N, Ball, G, Boardman, JP, Arichi, T, Merchant, N, Rueckert, D, Edwards, AD, Counsell, SJ, Harel, N, Ball, G, Boardman, JP, Arichi, T, Merchant, N, Rueckert, D, Edwards, AD, and Counsell, SJ

Abstract

Early neuroimaging may provide a surrogate marker for brain development and outcome after preterm birth. Tract-Based Spatial Statistics (TBSS) is an advanced Diffusion Tensor Image (DTI) analysis technique that is sensitive to the effects of prematurity and may provide a quantitative marker for neuroprotection following perinatal brain injury or preterm birth. Here, we test the sensitivity of TBSS to detect diffuse microstructural differences in the developing white matter of preterm infants at term-equivalent age by modelling a 'treatment' effect as a global increase in fractional anisotropy (FA). As proof of concept we compare these simulations to a real effect of increasing age at scan. 3-Tesla, 15-direction diffusion tensor imaging (DTI) was acquired from 90 preterm infants at term-equivalent age. Datasets were randomly assigned to 'treated' or 'untreated' groups of increasing size and voxel-wise increases in FA were used to simulate global treatment effects of increasing magnitude in all 'treated' maps. 'Treated' and 'untreated' FA maps were compared using TBSS. Predictions from simulated data were then compared to exemplar TBSS group comparisons based on increasing postmenstrual age at scan. TBSS proved sensitive to global differences in FA within a clinically relevant range, even in relatively small group sizes, and simulated data were shown to predict well a true biological effect of increasing age on white matter development. These data confirm that TBSS is a sensitive tool for detecting global group-wise differences in FA in this population.

Published
2013

7. The Effect of Preterm Birth on Thalamic and Cortical Development

Author

Ball, G, Boardman, JP, Rueckert, D, Aljabar, P, Arichi, T, Merchant, N, Gousias, IS, Edwards, AD, Counsell, SJ, Ball, G, Boardman, JP, Rueckert, D, Aljabar, P, Arichi, T, Merchant, N, Gousias, IS, Edwards, AD, and Counsell, SJ

Abstract

Preterm birth is a leading cause of cognitive impairment in childhood and is associated with cerebral gray and white matter abnormalities. Using multimodal image analysis, we tested the hypothesis that altered thalamic development is an important component of preterm brain injury and is associated with other macro- and microstructural alterations. T(1)- and T(2)-weighted magnetic resonance images and 15-direction diffusion tensor images were acquired from 71 preterm infants at term-equivalent age. Deformation-based morphometry, Tract-Based Spatial Statistics, and tissue segmentation were combined for a nonsubjective whole-brain survey of the effect of prematurity on regional tissue volume and microstructure. Increasing prematurity was related to volume reduction in the thalamus, hippocampus, orbitofrontal lobe, posterior cingulate cortex, and centrum semiovale. After controlling for prematurity, reduced thalamic volume predicted: lower cortical volume; decreased volume in frontal and temporal lobes, including hippocampus, and to a lesser extent, parietal and occipital lobes; and reduced fractional anisotropy in the corticospinal tracts and corpus callosum. In the thalamus, reduced volume was associated with increased diffusivity. This demonstrates a significant effect of prematurity on thalamic development that is related to abnormalities in allied brain structures. This suggests that preterm delivery disrupts specific aspects of cerebral development, such as the thalamocortical system.

Published
2012

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