Search

Your search keyword '"Pelin, Helena"' showing total 6 results
Start Over Author "Pelin, Helena"
6 results on '"Pelin, Helena"'

1. Characterisation of age and polarity at onset in bipolar disorder.

Author

Kalman, Janos L, Olde Loohuis, Loes M, Vreeker, Annabel, McQuillin, Andrew, Stahl, Eli A, Ruderfer, Douglas, Grigoroiu-Serbanescu, Maria, Panagiotaropoulou, Georgia, Ripke, Stephan, Bigdeli, Tim B, Stein, Frederike, Meller, Tina, Meinert, Susanne, Pelin, Helena, Streit, Fabian, Papiol, Sergi, Adams, Mark J, Adolfsson, Rolf, Adorjan, Kristina, Agartz, Ingrid, Aminoff, Sofie R, Anderson-Schmidt, Heike, Andreassen, Ole A, Ardau, Raffaella, Aubry, Jean-Michel, Balaban, Ceylan, Bass, Nicholas, Baune, Bernhard T, Bellivier, Frank, Benabarre, Antoni, Bengesser, Susanne, Berrettini, Wade H, Boks, Marco P, Bromet, Evelyn J, Brosch, Katharina, Budde, Monika, Byerley, William, Cervantes, Pablo, Chillotti, Catina, Cichon, Sven, Clark, Scott R, Comes, Ashley L, Corvin, Aiden, Coryell, William, Craddock, Nick, Craig, David W, Croarkin, Paul E, Cruceanu, Cristiana, Czerski, Piotr M, Dalkner, Nina, Dannlowski, Udo, Degenhardt, Franziska, Del Zompo, Maria, DePaulo, J Raymond, Djurovic, Srdjan, Edenberg, Howard J, Eissa, Mariam Al, Elvsåshagen, Torbjørn, Etain, Bruno, Fanous, Ayman H, Fellendorf, Frederike, Fiorentino, Alessia, Forstner, Andreas J, Frye, Mark A, Fullerton, Janice M, Gade, Katrin, Garnham, Julie, Gershon, Elliot, Gill, Michael, Goes, Fernando S, Gordon-Smith, Katherine, Grof, Paul, Guzman-Parra, Jose, Hahn, Tim, Hasler, Roland, Heilbronner, Maria, Heilbronner, Urs, Jamain, Stephane, Jimenez, Esther, Jones, Ian, Jones, Lisa, Jonsson, Lina, Kahn, Rene S, Kelsoe, John R, Kennedy, James L, Kircher, Tilo, Kirov, George, Kittel-Schneider, Sarah, Klöhn-Saghatolislam, Farah, Knowles, James A, Kranz, Thorsten M, Lagerberg, Trine Vik, Landen, Mikael, Lawson, William B, Leboyer, Marion, Li, Qingqin S, Maj, Mario, Malaspina, Dolores, Manchia, Mirko, and Mayoral, Fermin

Subjects
Psychiatric Genomics Consortium (PGC) Bipolar Disorder Working Group, International Consortium on Lithium Genetics, Colombia-US Cross Disorder Collaboration in Psychiatric Genetics, Humans, Bipolar Disorder, Depressive Disorder, Major, Age of Onset, Multifactorial Inheritance, Genome-Wide Association Study, Autism Spectrum Disorder, Bipolar disorder, GWAS, age at onset, polarity at onset, polygenic score, Genetics, Mental Health, Prevention, Brain Disorders, Serious Mental Illness, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundStudying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.AimsTo examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.MethodGenome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.ResultsEarlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = -0.34 years, s.e. = 0.08), major depression (β = -0.34 years, s.e. = 0.08), schizophrenia (β = -0.39 years, s.e. = 0.08), and educational attainment (β = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.ConclusionsAAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Published
2021

2. Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Author

Pelin, Helena, Ising, Marcus, Stein, Frederike, Meinert, Susanne, Meller, Tina, Brosch, Katharina, Winter, Nils R., Krug, Axel, Leenings, Ramona, Lemke, Hannah, Nenadić, Igor, Heilmann-Heimbach, Stefanie, Forstner, Andreas J., Nöthen, Markus M., Opel, Nils, Repple, Jonathan, Pfarr, Julia, Ringwald, Kai, Schmitt, Simon, Thiel, Katharina, Waltemate, Lena, Winter, Alexandra, Streit, Fabian, Witt, Stephanie, Rietschel, Marcella, Dannlowski, Udo, Kircher, Tilo, Hahn, Tim, Müller-Myhsok, Bertram, and Andlauer, Till F. M.

Published
2021
Full Text
View/download PDF

3. Subtyping psychiatric disorders using unsupervised learning methods

Author

Müller-Myhsok, Bertram (Prof. Dr.), Müller-Myhsok, Bertram (Prof. Dr.);Gagneur, Julien (Prof. Dr.), Pelin, Helena, Müller-Myhsok, Bertram (Prof. Dr.), Müller-Myhsok, Bertram (Prof. Dr.);Gagneur, Julien (Prof. Dr.), and Pelin, Helena

Abstract

Psychiatric disorders are highly heterogeneous regarding their symptoms and disease course. The aim of this thesis was to apply computational methods to generate new knowledge in the field of classification of psychiatric diseases and new subtypes discovery. Machine learning methods were used to identify and characterize the clusters in a transdiagnostic sample consisting of healthy controls and psychiatric disorders., Psychiatrische Erkrankungen sind in Bezug auf Symptome und Krankheitsverlauf sehr heterogen. Ziel dieser Arbeit war es, computergestützte Methoden anzuwenden, um neue Erkenntnisse auf dem Gebiet der Klassifikation psychiatrischer Erkrankungen zu generieren. Maschinelles Lernen wurde verwendet, um die Cluster in einer transdiagnostischen Stichprobe bestehend aus gesunden Kontrollpersonen und psychiatrischen Störungen zu identifizieren und zu charakterisieren

Published
2022

4. Characterisation of age and polarity at onset in bipolar disorder

Author

Kalman, Janos, Olde Loohuis, Loes, Vreeker, Annabel, Mcquillin, Andrew, Stahl, Eli, Ruderfer, Douglas, Grigoroiu-Serbanescu, Maria, Panagiotaropoulou, Georgia, Ripke, Stephan, Bigdeli, Tim, Stein, Frederike, Meller, Tina, Meinert, Susanne, Pelin, Helena, Streit, Fabian, Papiol, Sergi, Adams, Mark, Adolfsson, Rolf, Adorjan, Kristina, Agartz, Ingrid, Aminoff, Sofie, Anderson-Schmidt, Heike, Andreassen, Ole, Ardau, Raffaella, Aubry, Jean-Michel, Balaban, Ceylan, Bass, Nicholas, Baune, Bernhard, Bellivier, Frank, Benabarre, Antoni, Bengesser, Susanne, Berrettini, Wade, Boks, Marco, Bromet, Evelyn, Brosch, Katharina, Budde, Monika, Byerley, William, Cervantes, Pablo, Chillotti, Catina, Cichon, Sven, Clark, Scott, Comes, Ashley, Corvin, Aiden, Coryell, William, Craddock, Nick, Craig, David, Croarkin, Paul, Cruceanu, Cristiana, Czerski, Piotr, Dalkner, Nina, Dannlowski, Udo, Degenhardt, Franziska, del Zompo, Maria, Depaulo, J Raymond, Djurovic, Srdjan, Edenberg, Howard, Eissa, Mariam Al, Elvsåshagen, Torbjørn, Etain, Bruno, Fanous, Ayman, Fellendorf, Frederike, Fiorentino, Alessia, Forstner, Andreas, Frye, Mark, Fullerton, Janice, Gade, Katrin, Garnham, Julie, Gershon, Elliot, Gill, Michael, Goes, Fernando, Gordon-Smith, Katherine, Grof, Paul, Guzman-Parra, Jose, Hahn, Tim, Hasler, Roland, Heilbronner, Maria, Heilbronner, Urs, Jamain, Stephane, Jimenez, Esther, Jones, Ian, Jones, Lisa, Jonsson, Lina, Kahn, Rene, Kelsoe, John, Kennedy, James, Kircher, Tilo, Kirov, George, Kittel-Schneider, Sarah, Klöhn-Saghatolislam, Farah, Knowles, James, Kranz, Thorsten, Lagerberg, Trine Vik, Landen, Mikael, Lawson, William, Leboyer, Marion, Li, Qingqin, Maj, Mario, Malaspina, Dolores, Manchia, Mirko, Mayoral, Fermin, Mcelroy, Susan, Mcinnis, Melvin, McIntosh, Andrew, Medeiros, Helena, Melle, Ingrid, Milanova, Vihra, Mitchell, Philip, Monteleone, Palmiero, Monteleone, Alessio Maria, Nöthen, Markus, Novak, Tomas, Nurnberger, John, O'Brien, Niamh, O'Connell, Kevin, O'Donovan, Claire, O'Donovan, Michael, Opel, Nils, Ortiz, Abigail, Owen, Michael, Pålsson, Erik, Pato, Carlos, Pato, Michele, Pawlak, Joanna, Pfarr, Julia-Katharina, Pisanu, Claudia, Potash, James, Rapaport, Mark, Reich-Erkelenz, Daniela, Reif, Andreas, Reininghaus, Eva, Repple, Jonathan, Richard-Lepouriel, Hélène, Rietschel, Marcella, Ringwald, Kai, Roberts, Gloria, Rouleau, Guy, Schaupp, Sabrina, Scheftner, William, Schmitt, Simon, Schofield, Peter, Schubert, K Oliver, Schulte, Eva, Schweizer, Barbara, Senner, Fanny, Severino, Giovanni, Sharp, Sally, Slaney, Claire, Smeland, Olav, Sobell, Janet, Squassina, Alessio, Stopkova, Pavla, Strauss, John, Tortorella, Alfonso, Turecki, Gustavo, Twarowska-Hauser, Joanna, Veldic, Marin, Vieta, Eduard, Vincent, John, Xu, Wei, Zai, Clement, Zandi, Peter, Di Florio, Arianna, Smoller, Jordan, Biernacka, Joanna, Mcmahon, Francis, Alda, Martin, Müller-Myhsok, Bertram, Koutsouleris, Nikolaos, Falkai, Peter, Freimer, Nelson, Andlauer, Till, Schulze, Thomas, Ophoff, Roel, Depaulo, J. Raymond, Schubert, K. Oliver, Andlauer, Till F.M., Optimisation thérapeutique en Neuropsychopharmacologie (OPTeN (UMR_S_1144 / U1144)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), IMRB - 'Neuropsychiatrie translationnelle' [Créteil] (U955 Inserm - UPEC), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Etain, Bruno, Child and Adolescent Psychiatry / Psychology, Psychiatry, Kalman, J. L., Loohuis, L. M. O., Vreeker, A., Mcquillin, A., Stahl, E. A., Ruderfer, D., Grigoroiu-Serbanescu, M., Panagiotaropoulou, G., Ripke, S., Bigdeli, T. B., Stein, F., Meller, T., Meinert, S., Pelin, H., Streit, F., Papiol, S., Adams, M. J., Adolfsson, R., Adorjan, K., Agartz, I., Aminoff, S. R., Anderson-Schmidt, H., Andreassen, O. A., Ardau, R., Aubry, J. -M., Balaban, C., Bass, N., Baune, B. T., Bellivier, F., Benabarre, A., Bengesser, S., Berrettini, W. H., Boks, M. P., Bromet, E. J., Brosch, K., Budde, M., Byerley, W., Cervantes, P., Chillotti, C., Cichon, S., Clark, S. R., Comes, A. L., Corvin, A., Coryell, W., Craddock, N., Craig, D. W., Croarkin, P. E., Cruceanu, C., Czerski, P. M., Dalkner, N., Dannlowski, U., Degenhardt, F., Del Zompo, M., Depaulo, J. R., Djurovic, S., Edenberg, H. J., Al Eissa, M., Elvsashagen, T., Etain, B., Fanous, A. H., Fellendorf, F., Fiorentino, A., Forstner, A. J., Frye, M. A., Fullerton, J. M., Gade, K., Garnham, J., Gershon, E., Gill, M., Goes, F. S., Gordon-Smith, K., Grof, P., Guzman-Parra, J., Hahn, T., Hasler, R., Heilbronner, M., Heilbronner, U., Jamain, S., Jimenez, E., Jones, I., Jones, L., Jonsson, L., Kahn, R. S., Kelsoe, J. R., Kennedy, J. L., Kircher, T., Kirov, G., Kittel-Schneider, S., Klohn-Saghatolislam, F., Knowles, J. A., Kranz, T. M., Lagerberg, T. V., Landen, M., Lawson, W. B., Leboyer, M., Li, Q. S., Maj, M., Malaspina, D., Manchia, M., Mayoral, F., Mcelroy, S. L., Mcinnis, M. G., Mcintosh, A. M., Medeiros, H., Melle, I., Milanova, V., Mitchell, P. B., Monteleone, P., Monteleone, A. M., Nothen, M. M., Novak, T., Nurnberger, J. I., O'Brien, N., O'Connell, K. S., O'Donovan, C., O'Donovan, M. C., Opel, N., Ortiz, A., Owen, M. J., Palsson, E., Pato, C., Pato, M. T., Pawlak, J., Pfarr, J. -K., Pisanu, C., Potash, J. B., Rapaport, M. H., Reich-Erkelenz, D., Reif, A., Reininghaus, E., Repple, J., Richard-Lepouriel, H., Rietschel, M., Ringwald, K., Roberts, G., Rouleau, G., Schaupp, S., Scheftner, W. A., Schmitt, S., Schofield, P. R., Schubert, K. O., Schulte, E. C., Schweizer, B., Senner, F., Severino, G., Sharp, S., Slaney, C., Smeland, O. B., Sobell, J. L., Squassina, A., Stopkova, P., Strauss, J., Tortorella, A., Turecki, G., Twarowska-Hauser, J., Veldic, M., Vieta, E., Vincent, J. B., Xu, W., Zai, C. C., Zandi, P. P., Di Florio, A., Smoller, J. W., Biernacka, J. M., Mcmahon, F. J., Alda, M., Muller-Myhsok, B., Koutsouleris, N., Falkai, P., Freimer, N. B., Andlauer, T. F. M., Schulze, T. G., and Ophoff, R. A.

Subjects
Paper, Multifactorial Inheritance, medicine.medical_specialty, Autism Spectrum Disorder, Bipolar disorder, MESH: Age of Onset, [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Medizin, GWAS, age at onset, polarity at onset, polygenic score, MESH: Depressive Disorder, Major, BF, Genome-wide association study, Disease, Psykiatri, SDG 3 - Good Health and Well-being, ddc:150, Polarity at onset, Internal medicine, MESH: Bipolar Disorder, Polygenic score, medicine, Humans, Academic Psychiatry, Age of Onset, Genetic association, Psychiatry, MESH: Autism Spectrum Disorder, Depressive Disorder, Major, MESH: Humans, business.industry, Age at onset, Heritability, medicine.disease, Genetic architecture, ddc, Psychiatry and Mental health, Schizophrenia, Autism spectrum disorder, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, MESH: Genome-Wide Association Study, RC0321, MESH: Multifactorial Inheritance, business, Genome-Wide Association Study
Abstract

BackgroundStudying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.AimsTo examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.MethodGenome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.ResultsEarlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.ConclusionsAAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Published
2021
Full Text
View/download PDF

5. Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Author

Pelin, Helena, primary, Ising, Marcus, additional, Stein, Frederike, additional, Meinert, Susanne, additional, Meller, Tina, additional, Brosch, Katharina, additional, Winter, Nils R., additional, Krug, Axel, additional, Leenings, Ramona, additional, Lemke, Hannah, additional, Nenadic, Igor, additional, Heilmann-Heimbach, Stefanie, additional, Forstner, Andreas J., additional, Nöthen, Markus M., additional, Opel, Nils, additional, Repple, Jonathan, additional, Pfarr, Julia, additional, Ringwald, Kai, additional, Schmitt, Simon, additional, Thiel, Katharina, additional, Waltemate, Lena, additional, Winter, Alexandra, additional, Streit, Fabian, additional, Witt, Stephanie, additional, Rietschel, Marcella, additional, Dannlowski, Udo, additional, Kircher, Tilo, additional, Hahn, Tim, additional, Müller-Myhsok, Bertram, additional, and Andlauer, Till F. M., additional

Published
2021
Full Text
View/download PDF

6. Spektralno klasteriranje

Author

Pelin, Helena and Drmač, Zlatko

Subjects
Normalized cut, neusmjereni graf, partition ot the graph, normalizirani rez, spektralno klasteriranje, NP-teški problem, particioniranje grafa, undirected graph, spectral clustering method, PRIRODNE ZNANOSTI. Matematika, Laplacian graph, NP-hard problem, NATURAL SCIENCES. Mathematics, Laplacijani graf
Abstract

Ukratko, metoda spektralnog klasteriranja zahtijeva da naše podatke koje je potrebno grupirati prikažemo u formi neusmjerenog grafa u kojem težine bridova definiramo kao mjere sličnosti između podataka. Kako bismo particionirali graf, potrebne su nam funkcije particioniranja poput reza, razmjernog reza ili normaliziranog reza. Kao najbolja funkcija pokazuje se normalizirani rez jer jedini posjeduje svojstvo dualnosti, odnosno istovremene minimizacije sličnosti između klastera te maksimizacije sličnosti unutar klastera što je upravo i cilj metode. Kako su optimizacije normaliziranog reza NP-teški problemi, metoda se iz diskretnog prostora prebacuje na neprekidnu domenu gdje nam na raspolaganju stoje sva dobra svojstva Laplacijana grafa, njegovih svojstvenih vrijednosti i vektora te teoremi iz varijacijske karakterizacije spektra simetrične matrice susjedstva. Pokazuje se da je za particioniranje grafa na K dijelova potrebno pronaći svojstvene vektore K najvećih svojstvenih vrijednosti normaliziranog Laplacijana grafa čime dobivamo neprekidno rješenje particioniranja. Da bismo dobili diskretno rješenje koje najbolje aproksimira pravo, spektralnom rotacijom naizmjenično nalazimo ortogonalnu matricu i diskretna rješenja čija će ortonormalna transformacija konvergirati ka pravom rješenju. In this work we focused on the spectral clustering method which has become one of the most popular modern clustering algorithms. For the purpose of clustering the given objects (people, pixels, points...), we represented the data in a form of a weighted undirected graph where the nodes are the points in a feature space and the edges represent similarities between the points. In order to partition the graph, one needs partitioning functions like Cut, Ratio Cut or Normalized cut. The last one appears to perform the best because of the duality property. This means that the function simultaneously minimizes the disassociation between the groups and maximizes the association within the groups which is exactly what the clustering method is aiming to do. The optimization of the Normalized cut function is shown to be a NP-hard problem so in order to find a solution, we have to move our problem from the discrete domain to the continuous one. Based on the variational characterization of the spectrum of the associated Laplacian, an approximate solution is constructed using the eigenspace of its dominant eigenvalues. It is shown that to partition the graph into K groups, one has to find the eigenvectors of the K largest eigenvalues of the normalized Laplacian. By doing so, we obtain the relaxed solution to the problem. In order to get the discrete one, we iteratively find a sequence of orthogonal matrices and a discrete approximate solutions that tend to approximate well the true discrete solution.

Published
2016

Catalog

Books, media, physical & digital resources
See catalog results