Browsing by Author "Chung, Y."

Now showing 1 - 2 of 2

Unknown
Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder
(Wiley, 2020) de Zwarte, S. M. C.; Brouwer, R.; Agartz, I.; Alda, M.; Alonso-Lana, S.; Bearden, C.; Bertolino, A.; Bonvino, A.; Bramon, E.; Buimer, E.; Cahn, W.; Canales-Rodríguez, E.; Cannon, D. M.; Cannon, T. D.; Caseras, X.; Castro-Fornieles, J.; Chen, Q.; Chung, Y.; De la Serna, E.; del Mar Bonnin, C.; Demro, C.; Di Giorgio, A.; Doucet, G.; Eker, M.; Erk, S.; Fatjó-Vilas, M.; Fears, S.; Foley, S.; Frangou, S.; Fullerton, J.; Glahn, D.; Goghari, V.; Goikolea, J.; Goldman, A.; Gonul, A.; Gruber, O.; Hajek, T.; Hawkins, E.; Heinz, A.; Ongun, C.; Hillegers, M.; Houenou, J.; Pol, H.; Hultman, C.; Ingvar, M.; Johansson, V.; Jönsson, E.; Kane, F.; Kempton, M.; Koenis, M.; Kopecek, M.; Krämer, B.; Lawrie, S.; Lenroot, R.; Marcelis, M.; Mattay, V.; McDonald, C.; Meyer-Lindenberg, A.; Michielse, S.; Mitchell, P.; Moreno, D.; Murray, R.; Mwangi, B.; Nabulsi, L.; Newport, J.; Olman, C.; van Os, J.; Overs, B.; Ozerdem, A.; Pergola, G.; Picchioni, M.; Piguet, C.; Pomarol-Clotet, E.; Radua, J.; Ramsay, I.; Richter, A.; Roberts, G.; Salvador, R.; Saricicek-Aydogan, A.; Sarró, S.; Schofield, P.; Simsek, E.; Simsek, F.; Soares, J.; Sponheim, S.; Sugranyes, G.; Toulopoulou, Timothea; Tronchin, G.; Vieta, E.; Walter, H.; Weinberger, D.; Whalley, H.; Wu, M. -J.; Yalin, N.; Andreassen, O.; Ching, C.; Thomopoulos, S.; van Erp, T.; Jahanshad, N.; Thompson, P.; Kahn, R.; van Haren, N.
First‐degree relatives of patients diagnosed with schizophrenia (SZ‐FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First‐degree relatives of patients diagnosed with bipolar disorder (BD‐FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD‐FDRs are inconsistent. Here, we performed a meta‐analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ‐FDRs, 867 BD‐FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ‐FDRs showed a pattern of widespread thinner cortex, while BD‐FDRs had widespread larger cortical surface area. IQ was lower in SZ‐FDRs (d = −0.42, p = 3 × 10−5), with weak evidence of IQ reductions among BD‐FDRs (d = −0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group‐effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ‐FDRs and more pronounced effects in BD‐FDRs. To conclude, SZ‐FDRs and BD‐FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ‐FDRs and BD‐FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.
Open Access
Towards interactive data exploration
(Springer, 2019) Binnig, C.; Basık, Fuat; Buratti, B.; Çetintemel, U.; Chung, Y.; Crotty, A.; Cousins, C.; Ebert, D.; Eichmann, P.; Galakatos, A.; Hattasch, B.; Ilkhechi, A.; Kraska, T.; Shang, Z.; Tromba, I.; Usta, Arif; Utama, P.; Upfal, E.; Wang, L.; Weir, N.; Zeleznik, R.; Zgraggen, E.; Castellanos, M.; Chrysanthis, P.; Pelechrinis, K.
Enabling interactive visualization over new datasets at “human speed” is key to democratizing data science and maximizing human productivity. In this work, we first argue why existing analytics infrastructures do not support interactive data exploration and outline the challenges and opportunities of building a system specifically designed for interactive data exploration. Furthermore, we present the results of building IDEA, a new type of system for interactive data exploration that is specifically designed to integrate seamlessly with existing data management landscapes and allow users to explore their data instantly without expensive data preparation costs. Finally, we discuss other important considerations for interactive data exploration systems including benchmarking, natural language interfaces, as well as interactive machine learning.