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527 results on '"Cercignani M"'

1. UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings

Author

Abate, F., Adu-Amankwah, A., Ae-Ngibise, KA, Agbokey, F, Agyemang, VA, Agyemang, CT, Akgun, C., Ametepe, J., Arichi, T., Asante, KP, Balaji, S., Baljer, L., Basser, PJ, Beauchemin, J., Bennallick, C., Berhane, Y., Boateng-Mensah, Y., Bourke, NJ, Bradford, L., Bruchhage, MMK, Lorente, R.Cano, Cawley, P., Cercignani, M., D Sa, V., Canha, A.de, Navarro, N.de, Dean, DC, III, Delarosa, J., Donald, KA, Dvorak, A., Edwards, AD, Field, D., Frail, H., Freeman, B., George, T., Gholam, J., Guerrero-Gonzalez, J., Hajnal, JV, Haque, R., Hollander, W., Hoodbhoy, Z., Huentelman, M., Jafri, SK, Jones, DK, Joubert, F., Karaulanov, T., Kasaro, MP, Knackstedt, S., Kolind, S., Koshy, B., Kravitz, R., Lafayette, S.Lecurieux, Lee, AC, Lena, B., Lepore, N., Linguraru, M., Ljungberg, E., Lockart, Z., Loth, E., Mannam, P., Masemola, KM, Moran, R., Murphy, D., Nakwa, FL, Nankabirwa, V., Nelson, CA, North, K., Nyame, S., O Halloran, R., O'Muircheartaigh, J., Oakley, BF, Odendaal, H., Ongeti, CM, Onyango, D., Oppong, SA, Padormo, F., Parvez, D., Paus, T., Pepper, MS, Phiri, KS, Poorman, M., Ringshaw, JE, Rogers, J., Rutherford, M., Sabir, H., Sacolick, L., Seal, M., Sekoli, ML, Shama, T., Siddiqui, K., Sindano, N., Spelke, MB, Springer, PE, Suleman, FE, Sundgren, PC, Teixeira, R., Terekegn, W., Traughber, M., Tuuli, MG, Rensburg, J.van, Váša, F., Velaphi, S., Velasco, P., Viljoen, IM, Vokhiwa, M., Webb, A., Weiant, C., Wiley, N., Wintermark, P., Yibetal, K., Deoni, SCL, and Williams, SCR

Published
2024
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5. Quantitative MRI of the human brain : magnetisation transfer and magnetic field mapping

Author

Cercignani, M.

Subjects
616.8
Abstract

The ultimate goal of this thesis was to identify ways of combining the parametric maps produced by the use of multiple quantitative Magnetic Resonance Imaging (MRI) techniques. As a first stage towards this goal, this thesis focuses on magnetisation transfer (MT) imaging, and on the use of field mapping techniques to improve the reliability of other quantitative techniques such as functional MRI (fMRI) and diffusion tensor (DT) MRI. After summarising the basic principles of MRI, the MT phenomenon is described and a quantitative MT model is reviewed. A set of experiments is then described aiming at optimising the acquisition parameters for the measurement of the MT ratio (MTR). The interaction between T and MT is investigated, confirming that MTR acquisition protocols should be designed to minimize T -weighting. Next, the quantitative model of MT is used to optimise the white-to-grey matter contrast to noise ratio of a pulse sequence for MTR measurement, at both 1.5 T and 3.0 T. The following chapter is focused on the optimisation of quantitative MT for in vivo applications. First, the sensitivity to noise of the technique is investigated using simulations. Second, the implementation of a 3D pulse sequence for quantitative MT is described. The sequence is used at 1.5 T and at 3.0 T to collect data from healthy volunteers, providing normative values. Finally, the set of sampling points used to measure MT parameters is optimised using the Cramer-Rao lower bound, showing dramatic improvements in both precision and accuracy. Next, after a review of static field inhomogeneities and field mapping, the consequences of field inhomogeneities on quantitative MT are evaluated. The use of novel acquisition sequences for field mapping is investigated, the application of field-map based correction for fMRI and DT MRI data is considered, and its effects are discussed. Finally, an attempt to combine different parameters through multivariate analysis is presented, by using principal component analysis to identify patterns of association between MT parameters. Finally, an attempt to combine different tialysis is presented, by using principal componen ssociation between MT parameters.

Published
2006

8. Does peripheral neuroinflammation predict chronicity following whiplash injury? Protocol for a prospective cohort study

Author

Ridehalgh, C, Fundaun, J, Bremner, S, Cercignani, M, Young, R, Trivedy, C, Novak, A, Greening, J, Schmid, A, and Dilley, A

Subjects
General Medicine
Abstract

IntroductionWhiplash-associated disorder grade 2 (WAD2) is characterised by musculoskeletal pain/tenderness but no apparent nerve injury. However, studies have found clinical features indicative of neuropathy and neuropathic pain. These studies may indicate peripheral nerve inflammation, since preclinical neuritis models found mechanical sensitivity in inflamed, intact nociceptors. The primary aim of this study is to establish the contribution of peripheral neuroinflammation to WAD2 and its role in prognosis. Participants will be invited to participate in a sub-study investigating the contribution of cutaneous small fibre pathology to WAD2.Methods and analysis115 participants within 1 month following whiplash injury and 34 healthy control participants will be recruited and complete validated questionnaires for pain, function and psychological factors. Data collection will take place at the Universities of Sussex and Oxford, UK. Clinical examination, quantitative sensory testing and blood samples will be undertaken. MRI scans using T2-weighted and diffusion tensor images of the brachial plexus and wrist will determine nerve inflammation and nerve structural changes. Skin biopsies from a substudy will determine structural integrity of dermal and intraepidermal nerve fibres. At 6 months, we will evaluate recovery using Neck Disability Index and a self-rated global recovery question and repeat the outcome measures. Regression analysis will identify differences in MRI parameters, clinical tests and skin biopsies between participants with WAD2 and age/gender-matched controls. Linear and logistic regression analyses will assess if nerve inflammation (MRI parameters) predicts poor outcome. Mixed effects modelling will compare MRI and clinical measures between recovered and non-recovered participants over time.Ethics and disseminationEthical approval was received from London-Brighton and Sussex Research Ethics Committee (20/PR/0625) and South Central—Oxford C Ethics Committee (18/SC/0263). Written informed consent will be obtained from participants prior to participation in the study. Results will be disseminated through publications in peer-reviewed journals, presentations at national/international conferences and social media.Trial registration numberNCT04940923.

Published
2022

14. Peripheral inflammation is associated with micro-structural and functional connectivity changes in depression-related brain networks

Author

Kitzbichler, M.G., primary, Aruldass, A.R., additional, Barker, G.J., additional, Wood, T.C., additional, Dowell, N.G., additional, Hurley, S.A., additional, McLean, J., additional, Correia, M., additional, Clarke, C., additional, Pointon, L., additional, Cavanagh, J., additional, Cowen, P., additional, Pariante, C., additional, Cercignani, M., additional, and Bullmore, E.T., additional

Published
2021
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17. Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets

Author

Postema, M.C., Hoogman, M., Ambrosino, S., Asherson, P., Banaschewski, T., Bandeira, C.E., Baranov, A., Bau, C.H.D., Baumeister, S., Baur-Streubel, R., Bellgrove, Mark A., Biederman, J., Bralten, J.B., Brandeis, D., Brem, S., Buitelaar, J.K., Busatto, G.F., Castellanos, F.X., Cercignani, M., Chaim-Avancini, T.M., Chantiluke, K.C., Christakou, A., Coghill, D., Conzelmann, A., Cubillo, A.I., Cupertino, R.B., Zeeuw, P. de, Doyle, A.E., Durston, S., Earl, E.A., Epstein, J.N., Ethofer, T., Fair, D.A., Fallgatter, A.J., Faraone, S.V, Frodl, T., Gabel, M.C., Gogberashvili, T., Grevet, E.H., Haavik, J., Harrison, N.A., Hartman, Catharina A., Heslenfeld, D.J., Hoekstra, P.J., Hohmann, S., Høvik, M.F., Jernigan, T.L., Kardatzki, B., Karkashadze, G., Kelly, C., Kohls, G., Konrad, K., Kuntsi, J., Lazaro, L., Lera-Miguel, S., Lesch, K.P., Louza, M.R., Lundervold, A.J., Malpas, C.B., Mattos, P., McCarthy, H., Namazova-Baranova, L., Nicolau, R., Nigg, J.T., Novotny, S.E., Weiss, E. Oberwelland, Tuura, R.L. O'Gorman, Oosterlaan, J., Oranje, B., Paloyelis, Y., Pauli, P., Picon, F.A., Plessen, K.J., Ramos-Quiroga, J.A., Reif, A., Reneman, L., Rosa, P.G., Rubia, K., Schrantee, A., Schweren, L.J., Seitz, J., Shaw, P., Silk, T.J., Skokauskas, N., Vila, J.C. Soliva, Stevens, M.C., Sudre, G., Tamm, L., Tovar-Moll, F., Erp, T.G. van, Vance, A., Vilarroya, O., Vives-Gilabert, Y., Polier, G.G. von, Walitza, S., Yoncheva, Y.N., Zanetti, M.V., Ziegler, G.C., Glahn, D.C., Fisher, S.E., Franke, B., Francks, C., Postema, M.C., Hoogman, M., Ambrosino, S., Asherson, P., Banaschewski, T., Bandeira, C.E., Baranov, A., Bau, C.H.D., Baumeister, S., Baur-Streubel, R., Bellgrove, Mark A., Biederman, J., Bralten, J.B., Brandeis, D., Brem, S., Buitelaar, J.K., Busatto, G.F., Castellanos, F.X., Cercignani, M., Chaim-Avancini, T.M., Chantiluke, K.C., Christakou, A., Coghill, D., Conzelmann, A., Cubillo, A.I., Cupertino, R.B., Zeeuw, P. de, Doyle, A.E., Durston, S., Earl, E.A., Epstein, J.N., Ethofer, T., Fair, D.A., Fallgatter, A.J., Faraone, S.V, Frodl, T., Gabel, M.C., Gogberashvili, T., Grevet, E.H., Haavik, J., Harrison, N.A., Hartman, Catharina A., Heslenfeld, D.J., Hoekstra, P.J., Hohmann, S., Høvik, M.F., Jernigan, T.L., Kardatzki, B., Karkashadze, G., Kelly, C., Kohls, G., Konrad, K., Kuntsi, J., Lazaro, L., Lera-Miguel, S., Lesch, K.P., Louza, M.R., Lundervold, A.J., Malpas, C.B., Mattos, P., McCarthy, H., Namazova-Baranova, L., Nicolau, R., Nigg, J.T., Novotny, S.E., Weiss, E. Oberwelland, Tuura, R.L. O'Gorman, Oosterlaan, J., Oranje, B., Paloyelis, Y., Pauli, P., Picon, F.A., Plessen, K.J., Ramos-Quiroga, J.A., Reif, A., Reneman, L., Rosa, P.G., Rubia, K., Schrantee, A., Schweren, L.J., Seitz, J., Shaw, P., Silk, T.J., Skokauskas, N., Vila, J.C. Soliva, Stevens, M.C., Sudre, G., Tamm, L., Tovar-Moll, F., Erp, T.G. van, Vance, A., Vilarroya, O., Vives-Gilabert, Y., Polier, G.G. von, Walitza, S., Yoncheva, Y.N., Zanetti, M.V., Ziegler, G.C., Glahn, D.C., Fisher, S.E., Franke, B., and Francks, C.

Abstract

Item does not contain fulltext, OBJECTIVE: Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. METHODS: We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. RESULTS: There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing. CONCLUSION: Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Published
2021

18. Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders

Author

Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abé, C, Anticevic, A, Alda, M, Aleman, A, Alloza, C, Alonso-Lana, S, Ameis, SH, Anagnostou, E, McIntosh, AA, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Ayesa-Arriola, R, Bakker, G, Banaj, N, Banaschewski, T, Bandeira, CE, Baranov, A, Bargalló, N, Bau, CHD, Baumeister, S, Baune, BT, Bellgrove, MA, Benedetti, F, Bertolino, A, Boedhoe, PSW, Boks, M, Bollettini, I, Del Mar Bonnin, C, Borgers, T, Borgwardt, S, Brandeis, D, Brennan, BP, Bruggemann, JM, Bülow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodríguez, EJ, Cannon, DM, Carr, VJ, Cascella, N, Cercignani, M, Chaim-Avancini, TM, Christakou, A, Coghill, D, Conzelmann, A, Crespo-Facorro, B, Cubillo, AI, Cullen, KR, Cupertino, RB, Daly, E, Dannlowski, U, Davey, CG, Denys, D, Deruelle, C, Di Giorgio, A, Dickie, EW, Dima, D, Dohm, K, Ehrlich, S, Ely, BA, Erwin-Grabner, T, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Fatjó-Vilas, M, Fedor, JM, Fitzgerald, KD, Ford, JM, Frodl, T, Fu, CHY, Fullerton, JM, Gabel, MC, Glahn, DC, Roberts, G, Gogberashvili, T, Goikolea, JM, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Green, MJ, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Guerrero-Pedraza, A, Silk, Timothy, Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abé, C, Anticevic, A, Alda, M, Aleman, A, Alloza, C, Alonso-Lana, S, Ameis, SH, Anagnostou, E, McIntosh, AA, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Ayesa-Arriola, R, Bakker, G, Banaj, N, Banaschewski, T, Bandeira, CE, Baranov, A, Bargalló, N, Bau, CHD, Baumeister, S, Baune, BT, Bellgrove, MA, Benedetti, F, Bertolino, A, Boedhoe, PSW, Boks, M, Bollettini, I, Del Mar Bonnin, C, Borgers, T, Borgwardt, S, Brandeis, D, Brennan, BP, Bruggemann, JM, Bülow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodríguez, EJ, Cannon, DM, Carr, VJ, Cascella, N, Cercignani, M, Chaim-Avancini, TM, Christakou, A, Coghill, D, Conzelmann, A, Crespo-Facorro, B, Cubillo, AI, Cullen, KR, Cupertino, RB, Daly, E, Dannlowski, U, Davey, CG, Denys, D, Deruelle, C, Di Giorgio, A, Dickie, EW, Dima, D, Dohm, K, Ehrlich, S, Ely, BA, Erwin-Grabner, T, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Fatjó-Vilas, M, Fedor, JM, Fitzgerald, KD, Ford, JM, Frodl, T, Fu, CHY, Fullerton, JM, Gabel, MC, Glahn, DC, Roberts, G, Gogberashvili, T, Goikolea, JM, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Green, MJ, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Guerrero-Pedraza, A, and Silk, Timothy

Published
2021

19. Characterizing neuroanatomic heterogeneity in people with and without ADHD based on subcortical brain volumes

Author

Li, T, van Rooij, D, Roth Mota, N, Buitelaar, JK, Hoogman, M, Arias Vasquez, A, Franke, B, Ambrosino, S, Banaschewski, T, Bandeira, CE, Bau, CHD, Baumeister, S, Baur-Streubel, R, Bellgrove, MA, Biederman, J, Bralten, J, Bramati, IE, Brandeis, D, Berm, S, Busatto, GF, Calvo, A, Castellanos, FX, Cercignani, M, Chantiluke, KC, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, AI, Cupertino, RB, de Zeeuw, P, Durston, S, Earl, EA, Epstein, JN, Ethofer, T, Fallgatter, AJ, Fair, DA, Faraone, SV, Frodl, T, Gabel, MC, Gogberashvili, T, Grevet, EH, Haavik, J, Harrison, NA, Hartman, CA, Heslenfeld, DJ, Hoekstra, PJ, Høvik, MF, Jahanshad, N, Kardatzki, B, Karkashadze, G, Kelly, C, Kohls, G, Konrad, K, Kuntsi, J, Lazaro, L, Lera-Miguel, S, Lesch, KP, Louza, MR, Lundervold, AJ, Malpas, CB, Mattos, P, McCarthy, H, Nicolau, R, Nigg, JT, O'Gorman Tuura, RL, Oosterlaan, J, Oranje, B, Paloyelis, Y, Pauli, P, Picon, FA, Plessen, KJ, Ramos-Quiroga, JA, Reif, A, Reneman, L, Rosa, PGP, Rubia, K, Schrantee, A, Schweren, LJS, Seitz, J, Shaw, P, Silk, Tim, Skokauskas, N, Carlos Soliva Vila, J, Soloveva, A, Stevens, MC, Sudre, G, Tamm, L, Thompson, PM, Tovar-Moll, F, van Erp, TGM, Vance, A, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Yoncheva, YN, Zanetti, MV, Ziegler, GC, Anikin, A, Asherson, P, Li, T, van Rooij, D, Roth Mota, N, Buitelaar, JK, Hoogman, M, Arias Vasquez, A, Franke, B, Ambrosino, S, Banaschewski, T, Bandeira, CE, Bau, CHD, Baumeister, S, Baur-Streubel, R, Bellgrove, MA, Biederman, J, Bralten, J, Bramati, IE, Brandeis, D, Berm, S, Busatto, GF, Calvo, A, Castellanos, FX, Cercignani, M, Chantiluke, KC, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, AI, Cupertino, RB, de Zeeuw, P, Durston, S, Earl, EA, Epstein, JN, Ethofer, T, Fallgatter, AJ, Fair, DA, Faraone, SV, Frodl, T, Gabel, MC, Gogberashvili, T, Grevet, EH, Haavik, J, Harrison, NA, Hartman, CA, Heslenfeld, DJ, Hoekstra, PJ, Høvik, MF, Jahanshad, N, Kardatzki, B, Karkashadze, G, Kelly, C, Kohls, G, Konrad, K, Kuntsi, J, Lazaro, L, Lera-Miguel, S, Lesch, KP, Louza, MR, Lundervold, AJ, Malpas, CB, Mattos, P, McCarthy, H, Nicolau, R, Nigg, JT, O'Gorman Tuura, RL, Oosterlaan, J, Oranje, B, Paloyelis, Y, Pauli, P, Picon, FA, Plessen, KJ, Ramos-Quiroga, JA, Reif, A, Reneman, L, Rosa, PGP, Rubia, K, Schrantee, A, Schweren, LJS, Seitz, J, Shaw, P, Silk, Tim, Skokauskas, N, Carlos Soliva Vila, J, Soloveva, A, Stevens, MC, Sudre, G, Tamm, L, Thompson, PM, Tovar-Moll, F, van Erp, TGM, Vance, A, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Yoncheva, YN, Zanetti, MV, Ziegler, GC, Anikin, A, and Asherson, P

Published
2021

20. Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis

Author

Zhang-James, Y, Helminen, EC, Liu, J, Busatto, GF, Calvo, A, Cercignani, M, Chaim-Avancini, TM, Gabel, MC, Harrison, NA, Lazaro, L, Lera-Miguel, S, Louza, MR, Nicolau, R, Rosa, PGP, Schulte-Rutte, M, Zanetti, MV, Ambrosino, S, Asherson, P, Banaschewski, T, Baranov, A, Baumeister, S, Baur-Streubel, R, Bellgrove, MA, Biederman, J, Bralten, J, Bramati, IE, Brandeis, D, Brem, S, Buitelaar, JK, Castellanos, FX, Chantiluke, KC, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, AI, Dale, AM, de Zeeuw, P, Doyle, AE, Durston, S, Earl, EA, Epstein, JN, Ethofer, T, Fair, DA, Fallgatter, AJ, Frodl, T, Gogberashvili, T, Haavik, J, Hartman, CA, Heslenfeld, DJ, Hoekstra, PJ, Hohmann, S, Høvik, MF, Jahanshad, N, Jernigan, TL, Kardatzki, B, Karkashadze, G, Kelly, C, Kohls, G, Konrad, K, Kuntsi, J, Lesch, KP, Lundervold, AJ, Malpas, CB, Mattos, P, McCarthy, H, Mehta, MA, Namazova-Baranova, L, Nigg, JT, Novotny, SE, O’Gorman Tuura, RL, Weiss, EO, Oosterlaan, J, Oranje, B, Paloyelis, Y, Pauli, P, Plessen, KJ, Ramos-Quiroga, JA, Reif, A, Reneman, L, Rubia, K, Schrantee, A, Schwarz, L, Schweren, LJS, Seitz, J, Shaw, P, Silk, Timothy, Skokauskas, N, Vila, JCS, Stevens, MC, Sudre, G, Tamm, L, Thompson, PM, Tovar-Moll, F, van Erp, TGM, Vance, A, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Yoncheva, YN, Zhang-James, Y, Helminen, EC, Liu, J, Busatto, GF, Calvo, A, Cercignani, M, Chaim-Avancini, TM, Gabel, MC, Harrison, NA, Lazaro, L, Lera-Miguel, S, Louza, MR, Nicolau, R, Rosa, PGP, Schulte-Rutte, M, Zanetti, MV, Ambrosino, S, Asherson, P, Banaschewski, T, Baranov, A, Baumeister, S, Baur-Streubel, R, Bellgrove, MA, Biederman, J, Bralten, J, Bramati, IE, Brandeis, D, Brem, S, Buitelaar, JK, Castellanos, FX, Chantiluke, KC, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, AI, Dale, AM, de Zeeuw, P, Doyle, AE, Durston, S, Earl, EA, Epstein, JN, Ethofer, T, Fair, DA, Fallgatter, AJ, Frodl, T, Gogberashvili, T, Haavik, J, Hartman, CA, Heslenfeld, DJ, Hoekstra, PJ, Hohmann, S, Høvik, MF, Jahanshad, N, Jernigan, TL, Kardatzki, B, Karkashadze, G, Kelly, C, Kohls, G, Konrad, K, Kuntsi, J, Lesch, KP, Lundervold, AJ, Malpas, CB, Mattos, P, McCarthy, H, Mehta, MA, Namazova-Baranova, L, Nigg, JT, Novotny, SE, O’Gorman Tuura, RL, Weiss, EO, Oosterlaan, J, Oranje, B, Paloyelis, Y, Pauli, P, Plessen, KJ, Ramos-Quiroga, JA, Reif, A, Reneman, L, Rubia, K, Schrantee, A, Schwarz, L, Schweren, LJS, Seitz, J, Shaw, P, Silk, Timothy, Skokauskas, N, Vila, JCS, Stevens, MC, Sudre, G, Tamm, L, Thompson, PM, Tovar-Moll, F, van Erp, TGM, Vance, A, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, and Yoncheva, YN

Published
2021

21. Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets

Author

Postema, MC, Hoogman, M, Ambrosino, S, Asherson, P, Banaschewski, T, Bandeira, CE, Baranov, A, Bau, CHD, Baumeister, S, Baur-Streubel, R, Bellgrove, MA, Biederman, J, Bralten, J, Brandeis, D, Brem, S, Buitelaar, JK, Busatto, GF, Castellanos, FX, Cercignani, M, Chaim-Avancini, TM, Chantiluke, KC, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, AI, Cupertino, RB, de Zeeuw, P, Doyle, AE, Durston, S, Earl, EA, Epstein, JN, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Frodl, T, Gabel, MC, Gogberashvili, T, Grevet, EH, Haavik, J, Harrison, NA, Hartman, CA, Heslenfeld, DJ, Hoekstra, PJ, Hohmann, S, Høvik, MF, Jernigan, TL, Kardatzki, B, Karkashadze, G, Kelly, C, Kohls, G, Konrad, K, Kuntsi, J, Lazaro, L, Lera-Miguel, S, Lesch, KP, Louza, MR, Lundervold, AJ, Malpas, CB, Mattos, P, McCarthy, H, Namazova-Baranova, L, Nicolau, R, Nigg, JT, Novotny, SE, Oberwelland Weiss, E, O'Gorman Tuura, RL, Oosterlaan, J, Oranje, B, Paloyelis, Y, Pauli, P, Picon, FA, Plessen, KJ, Ramos-Quiroga, JA, Reif, A, Reneman, L, Rosa, PGP, Rubia, K, Schrantee, A, Schweren, LJS, Seitz, J, Shaw, P, Silk, Tim, Skokauskas, N, Soliva Vila, JC, Stevens, MC, Sudre, G, Tamm, L, Tovar-Moll, F, van Erp, TGM, Vance, A, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Yoncheva, YN, Zanetti, MV, Ziegler, GC, Glahn, DC, Jahanshad, N, Postema, MC, Hoogman, M, Ambrosino, S, Asherson, P, Banaschewski, T, Bandeira, CE, Baranov, A, Bau, CHD, Baumeister, S, Baur-Streubel, R, Bellgrove, MA, Biederman, J, Bralten, J, Brandeis, D, Brem, S, Buitelaar, JK, Busatto, GF, Castellanos, FX, Cercignani, M, Chaim-Avancini, TM, Chantiluke, KC, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, AI, Cupertino, RB, de Zeeuw, P, Doyle, AE, Durston, S, Earl, EA, Epstein, JN, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Frodl, T, Gabel, MC, Gogberashvili, T, Grevet, EH, Haavik, J, Harrison, NA, Hartman, CA, Heslenfeld, DJ, Hoekstra, PJ, Hohmann, S, Høvik, MF, Jernigan, TL, Kardatzki, B, Karkashadze, G, Kelly, C, Kohls, G, Konrad, K, Kuntsi, J, Lazaro, L, Lera-Miguel, S, Lesch, KP, Louza, MR, Lundervold, AJ, Malpas, CB, Mattos, P, McCarthy, H, Namazova-Baranova, L, Nicolau, R, Nigg, JT, Novotny, SE, Oberwelland Weiss, E, O'Gorman Tuura, RL, Oosterlaan, J, Oranje, B, Paloyelis, Y, Pauli, P, Picon, FA, Plessen, KJ, Ramos-Quiroga, JA, Reif, A, Reneman, L, Rosa, PGP, Rubia, K, Schrantee, A, Schweren, LJS, Seitz, J, Shaw, P, Silk, Tim, Skokauskas, N, Soliva Vila, JC, Stevens, MC, Sudre, G, Tamm, L, Tovar-Moll, F, van Erp, TGM, Vance, A, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Yoncheva, YN, Zanetti, MV, Ziegler, GC, Glahn, DC, and Jahanshad, N

Published
2021

22. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

Author

Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S.I., Pozzi, E., Abe, Y., Abé, C., Anticevic, A., Alda, M., Aleman, A., Alloza, C., Alonso-Lana, S., Ameis, S.H., Anagnostou, E., McIntosh, A.A., Arango, C., Arnold, P.D., Asherson, P., Assogna, F., Auzias, G., Ayesa-Arriola, R., Bakker, G., Banaj, N., Banaschewski, T., Bandeira, C.E., Baranov, A., Bargalló, N., Bau, C.H.D., Baumeister, S., Baune, B.T., Bellgrove, M.A., Benedetti, F., Bertolino, A., Boedhoe, P.S.W., Boks, M., Bollettini, I., Del Mar Bonnin, C., Borgers, T., Borgwardt, S., Brandeis, D., Brennan, B.P., Bruggemann, J.M., Bülow, R., Busatto, G.F., Calderoni, S., Calhoun, V.D., Calvo, R., Canales-Rodríguez, E.J., Cannon, D.M., Carr, V.J., Cascella, N., Cercignani, M., Chaim-Avancini, T.M., Christakou, A., Coghill, D., Conzelmann, A., Crespo-Facorro, B., Cubillo, A.I., Cullen, K.R., Cupertino, R.B., Daly, E., Dannlowski, U., Davey, C.G., Denys, D., Deruelle, C., Di Giorgio, A., Dickie, E.W., Dima, D., Dohm, K., Ehrlich, S., Ely, B.A., Erwin-Grabner, T., Ethofer, T., Fair, D.A., Fallgatter, A.J., Faraone, S.V., Fatjó-Vilas, M., Fedor, J.M., Fitzgerald, K.D., Ford, J.M., Frodl, T., Fu, C.H.Y., Fullerton, J.M., Gabel, M.C., Glahn, D.C., Roberts, G., Gogberashvili, T., Goikolea, J.M., Gotlib, I.H., Goya-Maldonado, R., Grabe, H.J., Green, M.J., Grevet, E.H., Groenewold, N.A., Grotegerd, D., Gruber, O., Gruner, P., Guerrero-Pedraza, A., Gur, R.E., Gur, R.C., Haar, S., Haarman, B.C.M., Haavik, J., Hahn, T., Hajek, T., Harrison, B.J., Harrison, N.A., Hartman, C.A., Whalley, H.C., Heslenfeld, D.J., Hibar, D.P., Hilland, E., Hirano, Y., Ho, T.C., Hoekstra, P.J., Hoekstra, L., Hohmann, S., Hong, L.E., Höschl, C., Høvik, M.F., Howells, F.M., Nenadic, I., Jalbrzikowski, M., James, A.C., Janssen, J., Jaspers-Fayer, F., Xu, J., Jonassen, R., Karkashadze, G., King, J.A., Kircher, T., Kirschner, M., Koch, K., Kochunov, P., Kohls, G., Konrad, K., Krämer, B., Krug, A., Kuntsi, J., Kwon, J.S., Landén, M., Landrø, N.I., Lazaro, L., Lebedeva, I.S., Leehr, E.J., Lera-Miguel, S., Lesch, K.-P., Lochner, C., Louza, M.R., Luna, B., Lundervold, A.J., Macmaster, F.P., Maglanoc, L.A., Malpas, C.B., Portella, M.J., Marsh, R., Martyn, F.M., Mataix-Cols, D., Mathalon, D.H., McCarthy, H., McDonald, C., McPhilemy, G., Meinert, S., Menchón, J.M., Minuzzi, L., Mitchell, P.B., Moreno, C., Morgado, P., Muratori, F., Murphy, C.M., Murphy, D., Mwangi, B., Nabulsi, L., Nakagawa, A., Nakamae, T., Namazova, L., Narayanaswamy, J., Jahanshad, N., Nguyen, D.D., Nicolau, R., O'Gorman Tuura, R.L., O'Hearn, K., Oosterlaan, J., Opel, N., Ophoff, R.A., Oranje, B., García De La Foz, V.O., Overs, B.J., Paloyelis, Y., Pantelis, C., Parellada, M., Pauli, P., Picó-Pérez, M., Picon, F.A., Piras, F., Plessen, K.J., Pomarol-Clotet, E., Preda, A., Puig, O., Quidé, Y., Radua, J., Ramos-Quiroga, J.A., Rasser, P.E., Rauer, L., Reddy, J., Redlich, R., Reif, A., Reneman, L., Repple, J., Retico, A., Richarte, V., Richter, A., Rosa, P.G.P., Rubia, K.K., Hashimoto, R., Sacchet, M.D., Salvador, R., Santonja, J., Sarink, K., Sarró, S., Satterthwaite, T.D., Sawa, A., Schall, U., Schofield, P.R., Schrantee, A., Seitz, J., Serpa, M.H., Setién-Suero, E., Shaw, P., Shook, D., Silk, T.J., Sim, K., Simon, S., Simpson, H.B., Singh, A., Skoch, A., Skokauskas, N., Soares, J.C., Soreni, N., Soriano-Mas, C., Spalletta, G., Spaniel, F., Lawrie, S.M., Stern, E.R., Stewart, S.E., Takayanagi, Y., Temmingh, H.S., Tolin, D.F., Tomecek, D., Tordesillas-Gutiérrez, D., Tosetti, M., Uhlmann, A., Van Amelsvoort, T., Van Der Wee, N.J.A., Van Der Werff, S.J.A., Van Haren, N.E.M., Van Wingen, G.A., Vance, A., Vázquez-Bourgon, J., Vecchio, D., Venkatasubramanian, G., Vieta, E., Vilarroya, O., Vives-Gilabert, Y., Voineskos, A.N., Völzke, H., Von Polier, G.G., Walton, E., Weickert, T.W., Weickert, C.S., Weideman, A.S., Wittfeld, K., Wolf, D.H., Wu, M.-J., Yang, T.T., Yang, K., Yoncheva, Y., Yun, J.-Y., Cheng, Y., Zanetti, M.V., Ziegler, G.C., Franke, B., Hoogman, M., Buitelaar, J.K., Van Rooij, D., Andreassen, O.A., Ching, C.R.K., Veltman, D.J., Schmaal, L., Stein, D.J., Van Den Heuvel, O.A., Turner, J.A., Van Erp, T.G.M., Pausova, Z., Thompson, P.M., Paus, T., Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S.I., Pozzi, E., Abe, Y., Abé, C., Anticevic, A., Alda, M., Aleman, A., Alloza, C., Alonso-Lana, S., Ameis, S.H., Anagnostou, E., McIntosh, A.A., Arango, C., Arnold, P.D., Asherson, P., Assogna, F., Auzias, G., Ayesa-Arriola, R., Bakker, G., Banaj, N., Banaschewski, T., Bandeira, C.E., Baranov, A., Bargalló, N., Bau, C.H.D., Baumeister, S., Baune, B.T., Bellgrove, M.A., Benedetti, F., Bertolino, A., Boedhoe, P.S.W., Boks, M., Bollettini, I., Del Mar Bonnin, C., Borgers, T., Borgwardt, S., Brandeis, D., Brennan, B.P., Bruggemann, J.M., Bülow, R., Busatto, G.F., Calderoni, S., Calhoun, V.D., Calvo, R., Canales-Rodríguez, E.J., Cannon, D.M., Carr, V.J., Cascella, N., Cercignani, M., Chaim-Avancini, T.M., Christakou, A., Coghill, D., Conzelmann, A., Crespo-Facorro, B., Cubillo, A.I., Cullen, K.R., Cupertino, R.B., Daly, E., Dannlowski, U., Davey, C.G., Denys, D., Deruelle, C., Di Giorgio, A., Dickie, E.W., Dima, D., Dohm, K., Ehrlich, S., Ely, B.A., Erwin-Grabner, T., Ethofer, T., Fair, D.A., Fallgatter, A.J., Faraone, S.V., Fatjó-Vilas, M., Fedor, J.M., Fitzgerald, K.D., Ford, J.M., Frodl, T., Fu, C.H.Y., Fullerton, J.M., Gabel, M.C., Glahn, D.C., Roberts, G., Gogberashvili, T., Goikolea, J.M., Gotlib, I.H., Goya-Maldonado, R., Grabe, H.J., Green, M.J., Grevet, E.H., Groenewold, N.A., Grotegerd, D., Gruber, O., Gruner, P., Guerrero-Pedraza, A., Gur, R.E., Gur, R.C., Haar, S., Haarman, B.C.M., Haavik, J., Hahn, T., Hajek, T., Harrison, B.J., Harrison, N.A., Hartman, C.A., Whalley, H.C., Heslenfeld, D.J., Hibar, D.P., Hilland, E., Hirano, Y., Ho, T.C., Hoekstra, P.J., Hoekstra, L., Hohmann, S., Hong, L.E., Höschl, C., Høvik, M.F., Howells, F.M., Nenadic, I., Jalbrzikowski, M., James, A.C., Janssen, J., Jaspers-Fayer, F., Xu, J., Jonassen, R., Karkashadze, G., King, J.A., Kircher, T., Kirschner, M., Koch, K., Kochunov, P., Kohls, G., Konrad, K., Krämer, B., Krug, A., Kuntsi, J., Kwon, J.S., Landén, M., Landrø, N.I., Lazaro, L., Lebedeva, I.S., Leehr, E.J., Lera-Miguel, S., Lesch, K.-P., Lochner, C., Louza, M.R., Luna, B., Lundervold, A.J., Macmaster, F.P., Maglanoc, L.A., Malpas, C.B., Portella, M.J., Marsh, R., Martyn, F.M., Mataix-Cols, D., Mathalon, D.H., McCarthy, H., McDonald, C., McPhilemy, G., Meinert, S., Menchón, J.M., Minuzzi, L., Mitchell, P.B., Moreno, C., Morgado, P., Muratori, F., Murphy, C.M., Murphy, D., Mwangi, B., Nabulsi, L., Nakagawa, A., Nakamae, T., Namazova, L., Narayanaswamy, J., Jahanshad, N., Nguyen, D.D., Nicolau, R., O'Gorman Tuura, R.L., O'Hearn, K., Oosterlaan, J., Opel, N., Ophoff, R.A., Oranje, B., García De La Foz, V.O., Overs, B.J., Paloyelis, Y., Pantelis, C., Parellada, M., Pauli, P., Picó-Pérez, M., Picon, F.A., Piras, F., Plessen, K.J., Pomarol-Clotet, E., Preda, A., Puig, O., Quidé, Y., Radua, J., Ramos-Quiroga, J.A., Rasser, P.E., Rauer, L., Reddy, J., Redlich, R., Reif, A., Reneman, L., Repple, J., Retico, A., Richarte, V., Richter, A., Rosa, P.G.P., Rubia, K.K., Hashimoto, R., Sacchet, M.D., Salvador, R., Santonja, J., Sarink, K., Sarró, S., Satterthwaite, T.D., Sawa, A., Schall, U., Schofield, P.R., Schrantee, A., Seitz, J., Serpa, M.H., Setién-Suero, E., Shaw, P., Shook, D., Silk, T.J., Sim, K., Simon, S., Simpson, H.B., Singh, A., Skoch, A., Skokauskas, N., Soares, J.C., Soreni, N., Soriano-Mas, C., Spalletta, G., Spaniel, F., Lawrie, S.M., Stern, E.R., Stewart, S.E., Takayanagi, Y., Temmingh, H.S., Tolin, D.F., Tomecek, D., Tordesillas-Gutiérrez, D., Tosetti, M., Uhlmann, A., Van Amelsvoort, T., Van Der Wee, N.J.A., Van Der Werff, S.J.A., Van Haren, N.E.M., Van Wingen, G.A., Vance, A., Vázquez-Bourgon, J., Vecchio, D., Venkatasubramanian, G., Vieta, E., Vilarroya, O., Vives-Gilabert, Y., Voineskos, A.N., Völzke, H., Von Polier, G.G., Walton, E., Weickert, T.W., Weickert, C.S., Weideman, A.S., Wittfeld, K., Wolf, D.H., Wu, M.-J., Yang, T.T., Yang, K., Yoncheva, Y., Yun, J.-Y., Cheng, Y., Zanetti, M.V., Ziegler, G.C., Franke, B., Hoogman, M., Buitelaar, J.K., Van Rooij, D., Andreassen, O.A., Ching, C.R.K., Veltman, D.J., Schmaal, L., Stein, D.J., Van Den Heuvel, O.A., Turner, J.A., Van Erp, T.G.M., Pausova, Z., Thompson, P.M., and Paus, T.

Abstract

Importance Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. Objective To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. Design, Setting, and Participants Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. Main Outcomes and Measures Interregional profiles of group difference in cortical thickness between cases and controls. Results A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (exce

Published
2021
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23. Ventral tegmental area disconnection contributes two years early to correctly classify patients converted to alzheimer's disease: Implications for treatment

Author

Serra, L., D'Amelio, M., Esposito, S., Di Domenico, C., Koch, G., Marra, Camillo, Mercuri, N. B., Caltagirone, C., Artusi, C. A., Lopiano, L., Cercignani, M., Bozzali, M., Marra C. (ORCID:0000-0003-3994-4044), Serra, L., D'Amelio, M., Esposito, S., Di Domenico, C., Koch, G., Marra, Camillo, Mercuri, N. B., Caltagirone, C., Artusi, C. A., Lopiano, L., Cercignani, M., Bozzali, M., and Marra C. (ORCID:0000-0003-3994-4044)

Abstract

Background: Recent cross-sectional studies highlighted the loss of dopaminergic neurons in the ventral tegmental area (VTA) as an early pathophysiological event in Alzheimer's disease (AD). Objective: In this study, we longitudinally investigated by resting-state fMRI (rs-fMRI) a cohort of patients with mild cognitive impairment (MCI) due to AD to evaluate the impact of VTA disconnection in predicting the conversion to AD. Methods: A cohort of 35 patients with MCI due to AD were recruited and followed-up for 24 months. They underwent cognitive evaluation and rs-fMRI to assess VTA connectivity at baseline and at follow-up. Results: At 24-month follow-up, 16 out of 35 patients converted to AD. Although converters and non-converters to AD did not differ in demographic and behavioral characteristics at baseline, the first group showed a significant reduction of VTA-driven connectivity in the posterior cingulate and precentral cortex. This pattern of additional disconnection in MCI-Converters compared to non-converters remained substantially unchanged at 24-month follow-up. Conclusion: This study reinforces the hypothesis of an early contribution of dopaminergic dysfunction to AD evolution by targeting the default-mode network. These results have potential implications for AD staging and prognosis and support new opportunities for therapeutic interventions to slow down disease progression.

Published
2021

24. Lesion distribution and substrate of white matter damage in myotonic dystrophy type 1: Comparison with multiple sclerosis

Author

Leddy, S., Serra, L., Esposito, D., Vizzotto, C., Giulietti, G., Silvestri, Gabriella, Petrucci, A., Meola, G., Lopiano, L., Cercignani, M., Bozzali, M., Silvestri G. (ORCID:0000-0002-1950-1468), Leddy, S., Serra, L., Esposito, D., Vizzotto, C., Giulietti, G., Silvestri, Gabriella, Petrucci, A., Meola, G., Lopiano, L., Cercignani, M., Bozzali, M., and Silvestri G. (ORCID:0000-0002-1950-1468)

Abstract

Myotonic Dystrophy type 1 (DM1) is an autosomal dominant condition caused by expansion of the CTG triplet repeats within the myotonic dystrophy protein of the kinase (DMPK) gene. The central nervous system is involved in the disease, with multiple symptoms including cognitive impairment. A typical feature of DM1 is the presence of widespread white matter (WM) lesions, whose total volume is associated with CTG triplet expansion. The aim of this study was to characterize the distribution and pathological substrate of these lesions as well as the normal appearing WM (NAWM) using quantitative magnetization transfer (qMT) MRI, and comparing data from DM1 patients with those from patients with multiple sclerosis (MS). Twenty-eight patients with DM1, 29 patients with relapsing-remitting MS, and 15 healthy controls had an MRI scan, including conventional and qMT imaging. The average pool size ratio (F), a proxy of myelination, was computed within lesions and NAWM for every participant. The lesion masks were warped into MNI space and lesion probability maps were obtained for each patient group. The lesion distribution, total lesion load and the tissue-specific mean F were compared between groups. The supratentorial distribution of lesions was similar in the 2 patient groups, although mean lesion volume was higher in MS than DM1. DM1 presented higher prevalence of anterior temporal lobe lesions, but none in the cerebellum and brainstem. Significantly reduced F values were found within DM1 lesions, suggesting a loss of myelin density. While F was reduced in the NAWM of MS patients, it did not differ between DM1 and controls. Our results provide further evidence for a need to compare histology and imaging using new MRI techniques in DM1 patients, in order to further our understanding of the underlying disease process contributing to WM disease.

Published
2021

26. Measurement of intra- and extra-neurite perfusion by combining ASL with the NODDI DWI model

Author

Asllani, I., Plaindoux, A., Petr, J., Woods, J., Osch, M., and Cercignani, M.

Subjects
otorhinolaryngologic diseases
Abstract

Intra- and extra-neurite perfusion in gray and white matter were estimated by applying a spatial linear regression algorithm on ASL images using the micro-structural anatomical information derived from the NODDI analysis of the DWI data. Baseline ASL images were acquired with 4 post-labeling delay (PLD) values in order to test the hypothesis of redistribution of ASL signal across the micro-compartments with increasing PLD. Motor activation was used to investigate the sensitivity of the method for detecting changes in perfusion at the micro-structural level.

Published
2020

28. Subcortical brain volume, regional cortical thickness, and cortical surface area across disorders: Findings from the ENIGMA ADHD, ASD, and OCD Working Groups

Author

Boedhoe, P.S., Rooij, D. van, Hoogman, M., Twisk, J.W.R., Schmaal, L., Abe, Y., Alonso, P., Ameis, S.H., Anikin, A., Anticevic, A., Arango, C., Arnold, P.D., Asherson, P., Assogna, F., Auzias, G., Banaschewski, T., Baranov, A., Batistuzzo, M.C., Baumeister, S., Baur-Streubel, R., Behrmann, M., Bellgrove, M.A., Benedetti, F. De, Beucke, J.C., Biederman, J., Bollettini, I., Bose, A., Bralten, J., Bramati, I.E., Brandeis, D., Brem, S., Brennan, B.P., Busatto, G.F., Calderoni, S., Calvo, A., Calvo, R., Castellanos, F.X., Cercignani, M., Chaim-Avancini, T.M., Chantiluke, K.C., Cheng, Y., Cho, K.I.K., Christakou, A., Coghill, D., Conzelmann, A., Cubillo, A.I., Dale, A.M., Dallaspezia, S., Daly, E., Denys, D., Deruelle, C., Martino, A, Dinstein, I., Doyle, A.E., Durston, S., Earl, E.A., Ecker, C., Ehrlich, S., Ely, B.A., Epstein, J.N., Ethofer, T., Fair, D.A., Fallgatter, A.J., Faraone, S.V, Fedor, J., Feng, X., Feusner, J.D., Fitzgerald, J., Fitzgerald, K.D., Fouche, J.P., Freitag, C.M., Fridgeirsson, E.A., Frodl, T., Gabel, M.C., Gallagher, L., Gogberashvili, T., Gori, I., Gruner, P., Gürsel, D.A., Haar, S., Haavik, J., Hall, G.B., Harrison, N.A., Hartman, Catharina A., Heslenfeld, D.J., Hirano, Y., Hoekstra, P.J., Hoexter, M.Q., Hohmann, S., Høvik, M.F., Hu, H., Huyser, C., Jahanshad, N., Jalbrzikowski, M., James, A., Janssen, J, Jaspers-Fayer, F., Jernigan, T.L., Kapilushniy, D., Kardatzki, B., Buitelaar, J.K., Franke, B., Heuvel, O.A. van den, Boedhoe, P.S., Rooij, D. van, Hoogman, M., Twisk, J.W.R., Schmaal, L., Abe, Y., Alonso, P., Ameis, S.H., Anikin, A., Anticevic, A., Arango, C., Arnold, P.D., Asherson, P., Assogna, F., Auzias, G., Banaschewski, T., Baranov, A., Batistuzzo, M.C., Baumeister, S., Baur-Streubel, R., Behrmann, M., Bellgrove, M.A., Benedetti, F. De, Beucke, J.C., Biederman, J., Bollettini, I., Bose, A., Bralten, J., Bramati, I.E., Brandeis, D., Brem, S., Brennan, B.P., Busatto, G.F., Calderoni, S., Calvo, A., Calvo, R., Castellanos, F.X., Cercignani, M., Chaim-Avancini, T.M., Chantiluke, K.C., Cheng, Y., Cho, K.I.K., Christakou, A., Coghill, D., Conzelmann, A., Cubillo, A.I., Dale, A.M., Dallaspezia, S., Daly, E., Denys, D., Deruelle, C., Martino, A, Dinstein, I., Doyle, A.E., Durston, S., Earl, E.A., Ecker, C., Ehrlich, S., Ely, B.A., Epstein, J.N., Ethofer, T., Fair, D.A., Fallgatter, A.J., Faraone, S.V, Fedor, J., Feng, X., Feusner, J.D., Fitzgerald, J., Fitzgerald, K.D., Fouche, J.P., Freitag, C.M., Fridgeirsson, E.A., Frodl, T., Gabel, M.C., Gallagher, L., Gogberashvili, T., Gori, I., Gruner, P., Gürsel, D.A., Haar, S., Haavik, J., Hall, G.B., Harrison, N.A., Hartman, Catharina A., Heslenfeld, D.J., Hirano, Y., Hoekstra, P.J., Hoexter, M.Q., Hohmann, S., Høvik, M.F., Hu, H., Huyser, C., Jahanshad, N., Jalbrzikowski, M., James, A., Janssen, J, Jaspers-Fayer, F., Jernigan, T.L., Kapilushniy, D., Kardatzki, B., Buitelaar, J.K., Franke, B., and Heuvel, O.A. van den

Abstract

Contains fulltext : 225388.pdf (Publisher’s version ) (Closed access), OBJECTIVE: Attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. The authors sought to directly compare these disorders using structural brain imaging data from ENIGMA consortium data. METHODS: Structural T(1)-weighted whole-brain MRI data from healthy control subjects (N=5,827) and from patients with ADHD (N=2,271), ASD (N=1,777), and OCD (N=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. The authors examined subcortical volume, cortical thickness, and cortical surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults, using linear mixed-effects models adjusting for age, sex, and site (and intracranial volume for subcortical and surface area measures). RESULTS: No shared differences were found among all three disorders, and shared differences between any two disorders did not survive correction for multiple comparisons. Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller intracranial volume than control subjects and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with adult control subjects and other clinical groups. No OCD-specific differences were observed across different age groups and surface area differences among all disorders in childhood and adulthood. CONCLUSIONS: The study findings suggest robust but subtle differences across different age groups among ADHD, ASD, and OCD. ADHD-specific intracranial volume and hippocampal differences in children and adolescents, and ASD-specific cortical thickness differences in the frontal cortex in adults, support previous work emphasizing structural brain differences in these disorders.

Published
2020

29. Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups

Author

Boedhoe, PSW, van Rooij, D, Hoogman, M, Twisk, JWR, Schmaal, L, Abe, Y, Alonso, P, Ameis, SH, Anikin, A, Anticevic, A, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Banaschewski, T, Baranov, A, Batistuzzo, MC, Baumeister, S, Baur-Streubel, R, Behrmann, M, Bellgrove, MA, Benedetti, F, Beucke, JC, Biederman, J, Bollettini, I, Bose, A, Bralten, J, Bramati, IE, Brandeis, D, Brem, S, Brennan, BP, Busatto, GF, Calderoni, S, Calvo, A, Calvo, R, Castellanos, FX, Cercignani, M, Chaim-Avancini, TM, Chantiluke, KC, Cheng, Y, Cho, KIK, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, A, Dale, AM, Dallaspezia, S, Daly, E, Denys, D, Deruelle, C, Di Martino, A, Dinstein, I, Doyle, AE, Durston, S, Earl, EA, Ecker, C, Ehrlich, S, Ely, BA, Epstein, JN, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, S, Fedor, J, Feng, X, Feusner, JD, Fitzgerald, J, Fitzgerald, KD, Fouche, J-P, Freitag, CM, Fridgeirsson, EA, Frodl, T, Gabel, MC, Gallagher, L, Gogberashvili, T, Gori, I, Gruner, P, Gursel, DA, Haar, S, Haavik, J, Hall, GB, Harrison, NA, Hartman, CA, Heslenfeld, DJ, Hirano, Y, Hoekstra, PJ, Hoexter, MQ, Hohmann, S, Hovik, MF, Hu, H, Huyser, C, Jahanshad, N, Jalbrzikowski, M, James, A, Janssen, J, Jaspers-Fayer, F, Jernigan, TL, Kapilushniy, D, Kardatzki, B, Karkashadze, G, Kathmann, N, Kaufmann, C, Kelly, C, Khadka, S, King, JA, Koch, K, Kohls, G, Konrad, K, Kuno, M, Kuntsi, J, Kvale, G, Kwon, JS, Lazaro, L, Lera-Miguel, S, Lesch, K-P, Hoekstra, L, Liu, Y, Lochner, C, Louza, MR, Luna, B, Lundervold, AJ, Malpas, CB, Marques, P, Marsh, R, Martinez-Zalacain, I, Mataix-Cols, D, Mattos, P, McCarthy, H, McGrath, J, Mehta, MA, Menchon, JM, Mennes, M, Martinho, MM, Moreira, PS, Morer, A, Morgado, P, Muratori, F, Murphy, CM, Murphy, DGM, Nakagawa, A, Nakamae, T, Nakao, T, Namazova-Baranova, L, Narayanaswamy, JC, Nicolau, R, Nigg, JT, Novotny, SE, Nurmi, EL, Weiss, EO, Tuura, RLO, O'Hearn, K, O'Neill, J, Oosterlaan, J, Oranje, B, Paloyelis, Y, Parellada, M, Pauli, P, Perriello, C, Piacentini, J, Piras, F, Plessen, KJ, Puig, O, Ramos-Quiroga, JA, Reddy, YCJ, Reif, A, Reneman, L, Retico, A, Rosa, PGP, Rubia, K, Rus, OG, Sakai, Y, Schrantee, A, Schwarz, L, Schweren, LJS, Seitz, J, Shaw, P, Shook, D, Silk, TJ, Simpson, HB, Skokauskas, N, Vila, JCS, Solovieva, A, Soreni, N, Soriano-Mas, C, Spalletta, G, Stern, ER, Stevens, MC, Stewart, SE, Sudre, G, Szeszko, PR, Tamm, L, Taylor, MJ, Tolin, DF, Tosetti, M, Tovar-Moll, F, Tsuchiyagaito, A, van Erp, TGM, van Wingen, GA, Vance, A, Venkatasubramanian, G, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Wallace, GL, Wang, Z, Wolfers, T, Yoncheva, YN, Yun, J-Y, Zanetti, M, Zhou, F, Ziegler, GC, Zierhut, KC, Zwiers, MP, Thompson, PM, Stein, DJ, Buitelaar, J, Franke, B, van den Heuvel, OA, Boedhoe, PSW, van Rooij, D, Hoogman, M, Twisk, JWR, Schmaal, L, Abe, Y, Alonso, P, Ameis, SH, Anikin, A, Anticevic, A, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Banaschewski, T, Baranov, A, Batistuzzo, MC, Baumeister, S, Baur-Streubel, R, Behrmann, M, Bellgrove, MA, Benedetti, F, Beucke, JC, Biederman, J, Bollettini, I, Bose, A, Bralten, J, Bramati, IE, Brandeis, D, Brem, S, Brennan, BP, Busatto, GF, Calderoni, S, Calvo, A, Calvo, R, Castellanos, FX, Cercignani, M, Chaim-Avancini, TM, Chantiluke, KC, Cheng, Y, Cho, KIK, Christakou, A, Coghill, D, Conzelmann, A, Cubillo, A, Dale, AM, Dallaspezia, S, Daly, E, Denys, D, Deruelle, C, Di Martino, A, Dinstein, I, Doyle, AE, Durston, S, Earl, EA, Ecker, C, Ehrlich, S, Ely, BA, Epstein, JN, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, S, Fedor, J, Feng, X, Feusner, JD, Fitzgerald, J, Fitzgerald, KD, Fouche, J-P, Freitag, CM, Fridgeirsson, EA, Frodl, T, Gabel, MC, Gallagher, L, Gogberashvili, T, Gori, I, Gruner, P, Gursel, DA, Haar, S, Haavik, J, Hall, GB, Harrison, NA, Hartman, CA, Heslenfeld, DJ, Hirano, Y, Hoekstra, PJ, Hoexter, MQ, Hohmann, S, Hovik, MF, Hu, H, Huyser, C, Jahanshad, N, Jalbrzikowski, M, James, A, Janssen, J, Jaspers-Fayer, F, Jernigan, TL, Kapilushniy, D, Kardatzki, B, Karkashadze, G, Kathmann, N, Kaufmann, C, Kelly, C, Khadka, S, King, JA, Koch, K, Kohls, G, Konrad, K, Kuno, M, Kuntsi, J, Kvale, G, Kwon, JS, Lazaro, L, Lera-Miguel, S, Lesch, K-P, Hoekstra, L, Liu, Y, Lochner, C, Louza, MR, Luna, B, Lundervold, AJ, Malpas, CB, Marques, P, Marsh, R, Martinez-Zalacain, I, Mataix-Cols, D, Mattos, P, McCarthy, H, McGrath, J, Mehta, MA, Menchon, JM, Mennes, M, Martinho, MM, Moreira, PS, Morer, A, Morgado, P, Muratori, F, Murphy, CM, Murphy, DGM, Nakagawa, A, Nakamae, T, Nakao, T, Namazova-Baranova, L, Narayanaswamy, JC, Nicolau, R, Nigg, JT, Novotny, SE, Nurmi, EL, Weiss, EO, Tuura, RLO, O'Hearn, K, O'Neill, J, Oosterlaan, J, Oranje, B, Paloyelis, Y, Parellada, M, Pauli, P, Perriello, C, Piacentini, J, Piras, F, Plessen, KJ, Puig, O, Ramos-Quiroga, JA, Reddy, YCJ, Reif, A, Reneman, L, Retico, A, Rosa, PGP, Rubia, K, Rus, OG, Sakai, Y, Schrantee, A, Schwarz, L, Schweren, LJS, Seitz, J, Shaw, P, Shook, D, Silk, TJ, Simpson, HB, Skokauskas, N, Vila, JCS, Solovieva, A, Soreni, N, Soriano-Mas, C, Spalletta, G, Stern, ER, Stevens, MC, Stewart, SE, Sudre, G, Szeszko, PR, Tamm, L, Taylor, MJ, Tolin, DF, Tosetti, M, Tovar-Moll, F, Tsuchiyagaito, A, van Erp, TGM, van Wingen, GA, Vance, A, Venkatasubramanian, G, Vilarroya, O, Vives-Gilabert, Y, von Polier, GG, Walitza, S, Wallace, GL, Wang, Z, Wolfers, T, Yoncheva, YN, Yun, J-Y, Zanetti, M, Zhou, F, Ziegler, GC, Zierhut, KC, Zwiers, MP, Thompson, PM, Stein, DJ, Buitelaar, J, Franke, B, and van den Heuvel, OA

Abstract

Objective: Attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. The authors sought to directly compare these disorders using structural brain imaging data from ENIGMA consortium data. Methods: Structural T1-weighted whole-brain MRI data from healthy control subjects (N=5,827) and from patients with ADHD (N=2,271), ASD (N=1,777), and OCD (N=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. The authors examined subcortical volume, cortical thickness, and cortical surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults, using linear mixed-effects models adjusting for age, sex, and site (and intracranial volume for subcortical and surface area measures). Results: No shared differences were found among all three disorders, and shared differences between any two disorders did not survive correction for multiple comparisons. Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller intracranial volume than control subjects and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with adult control subjects and other clinical groups. No OCD-specific differences were observed across different age groups and surface area differences among all disorders in childhood and adulthood. Conclusions: The study findings suggest robust but subtle differences across different age groups among ADHD, ASD, and OCD. ADHD-specific intracranial volume and hippocampal differences in children and adolescents, and ASD-specific cortical thickness differences in the frontal cortex in adults, support previous work emphasizing structural brain differences in these disorders.

Published
2020

30. Behavioral psychological symptoms of dementia and functional connectivity changes: a network-based study

Author

Serra, L., Bruschini, M., Di Domenico, C., Mancini, M., Bechi Gabrielli, G., Bonarota, S., Caltagirone, C., Cercignani, M., Marra, Camillo, Bozzali, M., Marra C. (ORCID:0000-0003-3994-4044), Serra, L., Bruschini, M., Di Domenico, C., Mancini, M., Bechi Gabrielli, G., Bonarota, S., Caltagirone, C., Cercignani, M., Marra, Camillo, Bozzali, M., and Marra C. (ORCID:0000-0003-3994-4044)

Abstract

Behavioral and psychological symptoms of dementia (BPSD) are commonly observed since the early stage of Alzheimer's disease (AD) associated with structural brain changes. It is conceivable that they may also relate to functional brain changes. This resting-state functional MRI (RS-fMRI) study investigated the alterations within functional brain networks of a cohort of AD patients at different clinical stages who presented with BPSD. One hundred one AD patients and 56 patients with amnestic mild cognitive impairment underwent a neuropsychological evaluation including the Neuropsychiatry Inventory-12 (NPI-12). All patients and 35 healthy controls (HS) underwent 3T-MRI. Factor analysis was used to extract the principal factors from NPI-12, while RS-fMRI data were processed using graph theory to investigate functional connectivity. Five factors were extracted from NPI-12. Sixty-two percent of patients showed BPSD and functional brain connectivity changes in various networks compared to those without BPSD and HS. These changes contributed to account for patients' BPSD. This work opens new perspectives in terms of nonpharmacological interventions that might be designed to modulate brain connectivity and improve patients' BPSD.

Published
2020

31. Ventral tegmental area dysfunction affects decision-making in patients with myotonic dystrophy type-1

Author

Serra, L., Scocchia, M., Meola, G., D'Amelio, M., Bruschini, M., Silvestri, Gabriella, Petrucci, A., Di Domenico, C., Caltagirone, C., Koch, G., Cercignani, M., Petrosini, L., Bozzali, M., Silvestri G. (ORCID:0000-0002-1950-1468), Serra, L., Scocchia, M., Meola, G., D'Amelio, M., Bruschini, M., Silvestri, Gabriella, Petrucci, A., Di Domenico, C., Caltagirone, C., Koch, G., Cercignani, M., Petrosini, L., Bozzali, M., and Silvestri G. (ORCID:0000-0002-1950-1468)

Abstract

The clinical manifestations of Myotonic Dystrophy type-1 (DM1) are associated with a complex mixture of multisystem features including cognitive dysfunctions that strongly impact on patients' social and occupational functioning. Decision making, a function controlled by dopaminergic circuitry, is critical for succeeding in one's social and professional life. We tested here the hypothesis that altered connectivity of the ventral tegmental area (VTA), one of the major sources of diffuse dopaminergic projections in the brain, might account for some higher-level dysfunctions observed in patients with DM1. In this case–control study, we recruited 31 patients with DM1 and 26 healthy controls who underwent the IOWA Gambling task and resting-state functional MRI (RS-fMRI) at 3T. Functional connectivity of the VTA was assessed using RS-fMRI. VTA connectivity was compared between 25 DM1 patients and all the controls, and the presence of associations between VTA connectivity and IOWA Gambling task performance was also investigated. DM1 patients performed significantly worse than controls at the IOWA Gambling task. A significant increase of functional connectivity was observed between VTA and the left supramarginal and superior temporal gyri in DM1 patients. Patients' IOWA Gambling task net-scores were strictly associated with VTA-driven functional connectivity in the bilateral supplementary motor area and right precentral gyrus. This study demonstrates a prominent deficit of decision-making in patients with DM1. It might be related to increased connectivity between VTA and brain areas critically involved in the reward/punishment system and social cognition. These findings indicate that dopaminergic function is a potential target for pharmacological and non-pharmacological interventions in DM1.

Published
2020

32. Cerebellar dentate nucleus functional connectivity with cerebral cortex in Alzheimer's disease and memory: a seed-based approach

Author

Olivito, G., Serra, L., Marra, C., Di Domenico, C., Caltagirone, C., Toniolo, S., Cercignani, M., Leggio, M., Bozzali, M., Marra C. (ORCID:0000-0003-3994-4044), Olivito, G., Serra, L., Marra, C., Di Domenico, C., Caltagirone, C., Toniolo, S., Cercignani, M., Leggio, M., Bozzali, M., and Marra C. (ORCID:0000-0003-3994-4044)

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disorder characterized by specific patterns of gray and white matter damage and cognitive/behavioral manifestations. The cerebellum has also been implicated in the pathophysiology of AD. Because the cerebellum is known to have strong functional connectivity (FC) with associative cerebral cortex regions, it is possible to hypothesize that it is incorporated into intrinsic FC networks relevant to cognitive manifestation of AD. In the present study, the cerebellar dentate nucleus, the largest cerebellar nucleus and the major output channel to the cerebral cortex, was chosen as the region of interest to test potential cerebellocerebral FC alterations and correlations with patients' memory impairment in a group of patients with AD. Compared to controls, patients with AD showed an increase in FC between the dentate nucleus and regions of the lateral temporal lobe. This study demonstrates that lower memory performances in AD may be related to altered FC within specific cerebellocortical functional modules, thus suggesting the cerebellar contribution to AD pathophysiology and typical memory dysfunctions.

Published
2020

33. Cerebellar White Matter Disruption in Alzheimer's Disease Patients: A Diffusion Tensor Imaging Study

Author

Toniolo, S., Serra, L., Olivito, G., Caltagirone, C., Mercuri, N. B., Marra, Camillo, Cercignani, M., Bozzali, M., Marra C. (ORCID:0000-0003-3994-4044), Toniolo, S., Serra, L., Olivito, G., Caltagirone, C., Mercuri, N. B., Marra, Camillo, Cercignani, M., Bozzali, M., and Marra C. (ORCID:0000-0003-3994-4044)

Abstract

The cognitive role of the cerebellum has recently gained much attention, and its pivotal role in Alzheimer's disease (AD) has now been widely recognized. Diffusion tensor imaging (DTI) has been used to evaluate the disruption of the microstructural milieu in AD, and though several white matter (WM) tracts such as corpus callosum, inferior and superior longitudinal fasciculus, cingulum, fornix, and uncinate fasciculus have been evaluated in AD, data on cerebellar WM tracts are currently lacking. We performed a tractography-based DTI reconstruction of the middle cerebellar peduncle (MCP), and the left and right superior cerebellar peduncles separately (SCPL and SCPR) and addressed the differences in fractional anisotropy (FA), axial diffusivity (Dax), radial diffusivity (RD), and mean diffusivity (MD) in the three tracts between 50 patients with AD and 25 healthy subjects. We found that AD patients showed a lower FA and a higher RD compared to healthy subjects in MCP, SCPL, and SCPR. Moreover, higher MD was found in SCPR and SCPL and higher Dax in SCPL. This result is important as it challenges the traditional view that WM bundles in the cerebellum are unaffected in AD and might identify new targets for therapeutic interventions.

Published
2020

37. White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. (Paper)

Author

Bozzali, M., Falini, A., Franceschi, M., Cercignani, M., Zuffi, M., Scotti, G., Comi, G., and Filippi, M.

Subjects
Brain -- Physiological aspects, Brain damage -- Physiological aspects, Magnetic resonance imaging -- Physiological aspects, Alzheimer's disease -- Physiological aspects, Health, Psychology and mental health, Physiological aspects
Abstract

Objective: To investigate the extent and the nature of white matter tissue damage of patients with Alzheimer's disease using diffusion tensor magnetic resonance imaging (DT-MRI). Background: Although Alzheimer's disease pathology [...]

Published
2002

44. Interferon-alpha induced changes in NODDI predispose to the development of fatigue

Author

Dowell, N. G., Bouyagoub, S., Tibble, J., Voon, V., Cercignani, M., and Harrison, N. A.

Abstract

Interferon-alpha (IFN-α) is an important mediator of antiviral immune responses. It is also used clinically in the treatment of hepatitis-C infection. Though effective, IFN-α-based therapies can often impair mood, motivation and cognition, which when severe can appear indistinguishable from major depression. In susceptible patients, fatigue and motivational impairment emerge early and have been linked to changes in basal ganglia (striatal) metabolism, neurochemistry and microstructural integrity. Here we use neurite orientation dispersion and density imaging (NODDI) modeling of multi-shell diffusion MRI to investigate whether changes in orientation dispersion index (ODI) or neurite density index (NDI) can predict the later emergence of IFN-α-induced fatigue. Eighteen patients initiating IFN-α based treatment for hepatitis-C underwent diffusion MRI and blood sampling at baseline and 4 hours after their first IFN-α injection. They were then followed up with regular psychological assessments for 12 weeks of treatment. IFN-α injection stimulated an acute inflammatory cytokine response and evoked acute fatigue that peaked between 4 and 12 weeks of treatment. Within the brain, IFN-α induced an acute increase in NDI in patients that experienced a simultaneous increase in IFN-α-induced fatigue but not patients that did not. Acute changes in striatal microstructure additionally predicted the continued development of fatigue but not mood symptoms 4 and 8 weeks later into treatment. Our findings highlight the value of NODDI as a potential in vivo biomarker of the central effects of peripheral inflammation. We highlight the exquisite sensitivity of the striatum to IFN-α and further implicate striatal perturbation in IFN-α-induced fatigue.

Published
2019

45. Extra-neurite Perfusion Measurement with Combined Arterial Spin Labeling and Diffusion Weighted MRI

Author

Asllani, I., Petr, J., Mutsaerts, H.-J., Bozzali, M., and Cercignani, M.

Subjects
fMRI CONTRAST MECHANISMS, Data analysis, Cerebral Blood Flow, MRI
Abstract

Introduction: Arterial Spin Labeling (ASL) is an MRI method that uses magnetically labeled endogenous water as a tracer for measuring cerebral perfusion in vivo1. The arterial water that is usually 'labeled' at a plane positioned at the base of the brain, perpendicular to the carotids. A post-labeling delay (PLD) is introduced prior to acquisition to allow labeled water to cross the vasculature and perfuse into the tissue1. Because of signal decay due to T1 relaxation, fast acquisition schemes are employed to ensure optimal SNR. Consequently, the spatial resolution of ASL is relatively low (~ 3 x 3 x 6 mm3). As such, the measured blood flow from a given voxel reflects a mixture of signals from gray matter (GM), white matter (WM), and CSF, a phenomenon known as partial voluming (PV)2. To correct for the confounding effects of PV in ASL imaging, an algorithm (PVC) has been developed and already used by several studies2,3. The algorithm is based on GM and WM volume data obtained from the segmentation of the T1w image2, and makes no further distinction between different compartments within the same tissue type. Here, we investigated the potential of PVC ASL to map blood perfusion in the extra-neurite compartment (e.g., soma, glial cells4) and the intra-neurite (comprised of axons and axon terminals4) within the same tissue, independently. We applied the PVC algorithm using compartmental data from a diffusion weighted imaging (DWI) model, referred to as NODDI4. The underlying hypothesis was that the blood flow in the extra- and intra-neurite compartments would vary with the PLD; a short PLD acquisition would increase the flow in the extra-neurite compartment compared to the long PLD for which there should be an increased flow in the intra-neurite compartment instead. Methods: Theory At any given voxel, the blood flow (fT) is given as: fT=VFIn•fIn+VFEn•fEn+VFIso•fIs where, VFIn, VFEn, VFIso represent respectively: the intra-neurite, extra-neurite, and non-tissue compartments obtained from NODDI4. By assuming that for each compartment blood flow is constant over a 'kernel', the equation can be re-written in vectorial form to reflect the flow at the voxel in the center of the kernel2, from which then each compartmental flow can be computed using linear regression as detailed in Asllani et al.2.. MRI protocol & image analysis T1w (MPRAGE), NODDI, and ASL MRI images were obtained on 4 healthy participants (mean age = 44.5 ± 7.4 y, 2 men) a Siemens 3T system. To test the hypothesis that a shorter PLD would increase the signal in the extra-neurite GM compartment, ASL was acquired with a short (200ms) and long PLD (1800ms). Only results from voxels with GM content > 80% are presented. Results: Fig.1 shows the raw images that were used by the PVC algorithm to extract the flow from each compartment within the GM. For the long-PLD acquisition, average CBF in the extra- and intra-neurite compartments was 76 ± 10 mL/100g*min and 59 ± 8 mL/100g*min, respectively. As hypothesized, for the short-PLD, the CBF signal was contained primarily in the extra-neurite department (118 ± 17 mL/100g*min) with the intra-neurite compartment flow being essentially zero (-0.9 ± 0.6 mL/100g*min). Results from one participant are shown in Fig.2. Supporting Image: Fig1.jpg ·Fig.1: ‘Raw’ NODDI and ASL images used by the PVC algorithm from one subject. Top row: MPRAGE and VFIn images; middle row: VFEn and VFISO; bottom row: CBF for short PLD (left) and long PLD (right). Supporting Image: Fig2.jpg ·Fig.2: Top: Extra-neurite GM CBF from short (left) & long (right) PLD acquisitions. Bottom: axial and sagittal views of Intra-neurite CBF for long PLD with areas in blue indicating ~zero signal. Conclusions: We combined NODDI with PVC ASL MRI to distinguish between blood flow in the extra- and intra-neurite compartments within GM. While these initial results look promising, more work is needed to test the sensitivity of this method and its feasibility for clinical applications. For example, a larger PLD range is needed to test whether the method can be used to detect inter-neurite subcortical flow. If successful, this method could prove invaluable in mapping blood flow with high spatial specificity.

Published
2019

46. Inflammatory biomarkers in Alzheimer's disease plasma

Author

Leckey, C, Nevado-Holgado, AJ, Barkhof, F, Bertram, L, Blin, O, Bos, I, Dobricic, V, Engelborghs, S, Frisoni, G, Frolich, L, Gabel, S, Johannsen, P, Kettunen, P, Koszewska, I, Legido-Quigley, C, Lleo, A, Martinez-Lage, P, Mecocci, P, Meersmans, K, Molinuevo, JL, Peyratout, G, Popp, J, Richardson, J, Sala, I, Scheltens, P, Streffer, J, Soininen, H, Tainta-Cuezva, M, Teunissen, C, Tsolaki, M, Vandenberghe, R, Visser, PJ, Vos, S, Wahlund, LO, Wallin, A, Westwood, S, Zetterberg, H, Bullmore, ET, Bhatti, J, Chamberlain, SJ, Correia, MM, Crofts, AL, Dickinson, A, Foster, AC, Kitzbichler, MG, Knight, C, Lynall, ME, Maurice, C, O'Donnell, C, Pointon, LJ, Hyslop, PS, Turner, L, Vertes, P, Widmer, B, Williams, GB, Morgan, BP, Morgan, AR, O'Hagan, C, Touchard, S, Cavanagh, J, Deith, C, Farmer, S, McClean, J, McColl, A, McPherson, A, Scouller, P, Sutherland, M, Boddeke, HWGM, Richardson, JC, Khan, S, Murphy, P, Parker, CA, Patel, J, Jones, D, Boer, P, Kemp, J, Drevets, WC, Nye, JS, Wittenberg, G, Isaac, J, Bhattacharya, A, Carruthers, N, Kolb, H, Pariante, CM, Turkheimer, F, Barker, GJ, Byrom, H, Cash, D, Cattaneo, A, Gee, A, Hastings, C, Mariani, N, McLaughlin, A, Mondelli, V, Nettis, M, Nikkheslat, N, Randall, K, Sheridan, H, Simmons, C, Singh, N, Van Loo, V, Vicente-Rodriguez, M, Wood, TC, Worrell, C, Zajkowska, Z, Plath, N, Egebjerg, J, Eriksson, H, Gastambide, F, Adams, KH, Jeggo, R, Thomsen, C, Pederson, JT, Campbell, B, Moller, T, Nelson, B, Zorn, S, O'Connor, J, Attenburrow, MJ, Baird, A, Benjamin, J, Clare, S, Cowen, P, Huang, IS, Hurley, S, Jones, H, Lovestone, S, Mada, F, Nevado-Holgado, A, Oladejo, A, Ribe, E, Smith, K, Vyas, A, Hughes, Z, Balice-Gordon, R, Duerr, J, Piro, JR, Sporn, J, Perry, VH, Cleal, M, Fryatt, G, Gomez-Nicola, D, Mancuso, R, Reynolds, R, Harrison, NA, Cercignani, M, Clarke, CL, Hoskins, E, Kohn, C, Murray, R, Wilcock, L, Wlazly, D, NIMA Consortium, and NIMA-Wellcome Trust Consortium

Subjects
Inflammation, Plasma, Complement, Biomarker, Alzheimer's disease
Abstract

Introduction: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers. Methods: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. Results: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOe4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). Discussion: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Published
2019

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