93 results on '"Lencer, R."'
Search Results
2. Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness
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Wenzel, J, Badde, L, Haas, SS, Bonivento, C, van Rheenen, TE, Antonucci, LA, Ruef, A, Penzel, N, Rosen, M, Lichtenstein, T, Lalousis, PA, Paolini, M, Stainton, A, Dannlowski, U, Romer, G, Brambilla, P, Wood, SJ, Upthegrove, R, Borgwardt, S, Meisenzahl, E, Salokangas, RKR, Pantelis, C, Lencer, R, Bertolino, A, Kambeitz, J, Koutsouleris, N, Dwyer, DB, Kambeitz-Ilankovic, L, Wenzel, J, Badde, L, Haas, SS, Bonivento, C, van Rheenen, TE, Antonucci, LA, Ruef, A, Penzel, N, Rosen, M, Lichtenstein, T, Lalousis, PA, Paolini, M, Stainton, A, Dannlowski, U, Romer, G, Brambilla, P, Wood, SJ, Upthegrove, R, Borgwardt, S, Meisenzahl, E, Salokangas, RKR, Pantelis, C, Lencer, R, Bertolino, A, Kambeitz, J, Koutsouleris, N, Dwyer, DB, and Kambeitz-Ilankovic, L
- Abstract
Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (NROP = 79, NROD = 30, NCHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (NROP = 61, NROD = 100, NCHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC.
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- 2024
3. A multivariate cognitive approach to predict social functioning in recent onset psychosis in response to computerized cognitive training
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Walter, N, Wenzel, J, Haas, SS, Squarcina, L, Bonivento, C, Ruef, A, Dwyer, D, Lichtenstein, T, Bastruek, O, Stainton, A, Antonucci, LA, Brambilla, P, Wood, SJ, Upthegrove, R, Borgwardt, S, Lencer, R, Meisenzahl, E, Salokangas, RKR, Pantelis, C, Bertolino, A, Koutsouleris, N, Kambeitz, J, Kambeitz-Ilankovic, L, Walter, N, Wenzel, J, Haas, SS, Squarcina, L, Bonivento, C, Ruef, A, Dwyer, D, Lichtenstein, T, Bastruek, O, Stainton, A, Antonucci, LA, Brambilla, P, Wood, SJ, Upthegrove, R, Borgwardt, S, Lencer, R, Meisenzahl, E, Salokangas, RKR, Pantelis, C, Bertolino, A, Koutsouleris, N, Kambeitz, J, and Kambeitz-Ilankovic, L
- Abstract
Clinical and neuroimaging data has been increasingly used in recent years to disentangle heterogeneity of treatment response to cognitive training (CT) and predict which individuals may achieve the highest benefits. CT has small to medium effects on improving cognitive and social functioning in recent onset psychosis (ROP) patients, who show the most profound cognitive and social functioning deficits among psychiatric patients. We employed multivariate pattern analysis (MVPA) to investigate the potential of cognitive data to predict social functioning improvement in response to 10 h of CT in patients with ROP. A support vector machine (SVM) classifier was trained on the naturalistic data of the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study sample to predict functioning in an independent sample of 70 ROP patients using baseline cognitive data. PRONIA is a part of a FP7 EU grant program that involved 7 sites across 5 European countries, designed and conducted with the main aim of identifying (bio)markers associated with an enhanced risk of developing psychosis in order to improve early detection and prognosis. Social functioning was predicted with a balanced accuracy (BAC) of 66.4% (Sensitivity 78.8%; Specificity 54.1%; PPV 60.5%; NPV 74.1%; AUC 0.64; P = 0.01). The most frequently selected cognitive features (mean feature weights > ± 0.2) included the (1) correct number of symbol matchings within the Digit Symbol Substitution Test, (2) the number of distracting stimuli leading to an error within 300 and 200 trials in the Continuous Performance Test and (3) the dynamics of verbal fluency between 15 and 30 s within the Verbal Fluency Test, phonetic part. Next, the SVM classifier generated on the PRONIA sample was applied to the intervention sample, that obtained 54 ROP patients who were randomly assigned to a social cognitive training (SCT) or treatment as usual (TAU) group and dichotomized into good (GF-S ≥ 7) and poor (GF-S < 7) function
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- 2024
4. Anhedonia as a Potential Transdiagnostic Phenotype With Immune-Related Changes in Recent-Onset Mental Health Disorders.
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Lalousis, PA, Malaviya, A, Khatibi, A, Saberi, M, Kambeitz-Ilankovic, L, Haas, SS, Wood, SJ, Barnes, NM, Rogers, J, Chisholm, K, Bertolino, A, Borgwardt, S, Brambilla, P, Kambeitz, J, Lencer, R, Pantelis, C, Ruhrmann, S, Salokangas, RKR, Schultze-Lutter, F, Schmidt, A, Meisenzahl, E, Dwyer, D, Koutsouleris, N, Upthegrove, R, Griffiths, SL, PRONIA Consortium, Lalousis, PA, Malaviya, A, Khatibi, A, Saberi, M, Kambeitz-Ilankovic, L, Haas, SS, Wood, SJ, Barnes, NM, Rogers, J, Chisholm, K, Bertolino, A, Borgwardt, S, Brambilla, P, Kambeitz, J, Lencer, R, Pantelis, C, Ruhrmann, S, Salokangas, RKR, Schultze-Lutter, F, Schmidt, A, Meisenzahl, E, Dwyer, D, Koutsouleris, N, Upthegrove, R, Griffiths, SL, and PRONIA Consortium
- Abstract
BACKGROUND: Chronic low-grade inflammation is observed across mental disorders and is associated with difficult-to-treat-symptoms of anhedonia and functional brain changes, reflecting a potential transdiagnostic dimension. Previous investigations have focused on distinct illness categories in people with enduring illness, but few have explored inflammatory changes. We sought to identify an inflammatory signal and the associated brain function underlying anhedonia among young people with recent-onset psychosis and recent-onset depression. METHODS: Resting-state functional magnetic resonance imaging, inflammatory markers, and anhedonia symptoms were collected from 108 (mean [SD] age = 26.2 [6.2] years; female = 50) participants with recent-onset psychosis (n = 53) and recent-onset depression (n = 55) from the European Union/Seventh Framework Programme-funded PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Time series were extracted using the Schaefer atlas, defining 100 cortical regions of interest. Using advanced multimodal machine learning, an inflammatory marker model and a functional connectivity model were developed to classify participants into an anhedonic group or a normal hedonic group. RESULTS: A repeated nested cross-validation model using inflammatory markers classified normal hedonic and anhedonic recent-onset psychosis/recent-onset depression groups with a balanced accuracy of 63.9% and an area under the curve of 0.61. The functional connectivity model produced a balanced accuracy of 55.2% and an area under the curve of 0.57. Anhedonic group assignment was driven by higher levels of interleukin 6, S100B, and interleukin 1 receptor antagonist and lower levels of interferon gamma, in addition to connectivity within the precuneus and posterior cingulate. CONCLUSIONS: We identified a potential transdiagnostic anhedonic subtype that was accounted for by an inflammatory profile and functional connectivity. Results have implications
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- 2024
5. Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders.
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Hoheisel, L, Kambeitz-Ilankovic, L, Wenzel, J, Haas, SS, Antonucci, LA, Ruef, A, Penzel, N, Schultze-Lutter, F, Lichtenstein, T, Rosen, M, Dwyer, DB, Salokangas, RKR, Lencer, R, Brambilla, P, Borgwardt, S, Wood, SJ, Upthegrove, R, Bertolino, A, Ruhrmann, S, Meisenzahl, E, Koutsouleris, N, Fink, GR, Daun, S, Kambeitz, J, PRONIA Consortium, Hoheisel, L, Kambeitz-Ilankovic, L, Wenzel, J, Haas, SS, Antonucci, LA, Ruef, A, Penzel, N, Schultze-Lutter, F, Lichtenstein, T, Rosen, M, Dwyer, DB, Salokangas, RKR, Lencer, R, Brambilla, P, Borgwardt, S, Wood, SJ, Upthegrove, R, Bertolino, A, Ruhrmann, S, Meisenzahl, E, Koutsouleris, N, Fink, GR, Daun, S, Kambeitz, J, and PRONIA Consortium
- Abstract
BACKGROUND: Patients with psychosis and patients with depression exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC) allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders. METHODS: We report the analysis of dFC in a large sample including 127 patients at clinical high risk for psychosis, 142 patients with recent-onset psychosis, 134 patients with recent-onset depression, and 256 healthy control participants. A sliding window-based technique was used to calculate the time-dependent FC in resting-state magnetic resonance imaging data, followed by clustering to reveal recurrent FC states in each diagnostic group. RESULTS: We identified 5 unique FC states, which could be identified in all groups with high consistency (mean r = 0.889 [SD = 0.116]). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly connected FC state in patients with recent-onset depression (p < .0005) compared with the other groups and a common increase in the lifetime of an FC state characterized by high sensorimotor and cingulo-opercular connectivities in all patient groups compared with the healthy control group (p < .0002). Canonical correlation analysis revealed a mode that exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < .0029), which was associated with positive psychosis symptom severity and several dFC parameters. CONCLUSIONS: Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression and psychosis and clinical risk states.
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- 2024
6. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.
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Jiang, Y, Luo, C, Wang, J, Palaniyappan, L, Chang, X, Xiang, S, Zhang, J, Duan, M, Huang, H, Gaser, C, Nemoto, K, Miura, K, Hashimoto, R, Westlye, LT, Richard, G, Fernandez-Cabello, S, Parker, N, Andreassen, OA, Kircher, T, Nenadić, I, Stein, F, Thomas-Odenthal, F, Teutenberg, L, Usemann, P, Dannlowski, U, Hahn, T, Grotegerd, D, Meinert, S, Lencer, R, Tang, Y, Zhang, T, Li, C, Yue, W, Zhang, Y, Yu, X, Zhou, E, Lin, C-P, Tsai, S-J, Rodrigue, AL, Glahn, D, Pearlson, G, Blangero, J, Karuk, A, Pomarol-Clotet, E, Salvador, R, Fuentes-Claramonte, P, Garcia-León, MÁ, Spalletta, G, Piras, F, Vecchio, D, Banaj, N, Cheng, J, Liu, Z, Yang, J, Gonul, AS, Uslu, O, Burhanoglu, BB, Uyar Demir, A, Rootes-Murdy, K, Calhoun, VD, Sim, K, Green, M, Quidé, Y, Chung, YC, Kim, W-S, Sponheim, SR, Demro, C, Ramsay, IS, Iasevoli, F, de Bartolomeis, A, Barone, A, Ciccarelli, M, Brunetti, A, Cocozza, S, Pontillo, G, Tranfa, M, Park, MTM, Kirschner, M, Georgiadis, F, Kaiser, S, Van Rheenen, TE, Rossell, SL, Hughes, M, Woods, W, Carruthers, SP, Sumner, P, Ringin, E, Spaniel, F, Skoch, A, Tomecek, D, Homan, P, Homan, S, Omlor, W, Cecere, G, Nguyen, DD, Preda, A, Thomopoulos, SI, Jahanshad, N, Cui, L-B, Yao, D, Thompson, PM, Turner, JA, van Erp, TGM, Cheng, W, ENIGMA Schizophrenia Consortium, Feng, J, ZIB Consortium, Jiang, Y, Luo, C, Wang, J, Palaniyappan, L, Chang, X, Xiang, S, Zhang, J, Duan, M, Huang, H, Gaser, C, Nemoto, K, Miura, K, Hashimoto, R, Westlye, LT, Richard, G, Fernandez-Cabello, S, Parker, N, Andreassen, OA, Kircher, T, Nenadić, I, Stein, F, Thomas-Odenthal, F, Teutenberg, L, Usemann, P, Dannlowski, U, Hahn, T, Grotegerd, D, Meinert, S, Lencer, R, Tang, Y, Zhang, T, Li, C, Yue, W, Zhang, Y, Yu, X, Zhou, E, Lin, C-P, Tsai, S-J, Rodrigue, AL, Glahn, D, Pearlson, G, Blangero, J, Karuk, A, Pomarol-Clotet, E, Salvador, R, Fuentes-Claramonte, P, Garcia-León, MÁ, Spalletta, G, Piras, F, Vecchio, D, Banaj, N, Cheng, J, Liu, Z, Yang, J, Gonul, AS, Uslu, O, Burhanoglu, BB, Uyar Demir, A, Rootes-Murdy, K, Calhoun, VD, Sim, K, Green, M, Quidé, Y, Chung, YC, Kim, W-S, Sponheim, SR, Demro, C, Ramsay, IS, Iasevoli, F, de Bartolomeis, A, Barone, A, Ciccarelli, M, Brunetti, A, Cocozza, S, Pontillo, G, Tranfa, M, Park, MTM, Kirschner, M, Georgiadis, F, Kaiser, S, Van Rheenen, TE, Rossell, SL, Hughes, M, Woods, W, Carruthers, SP, Sumner, P, Ringin, E, Spaniel, F, Skoch, A, Tomecek, D, Homan, P, Homan, S, Omlor, W, Cecere, G, Nguyen, DD, Preda, A, Thomopoulos, SI, Jahanshad, N, Cui, L-B, Yao, D, Thompson, PM, Turner, JA, van Erp, TGM, Cheng, W, ENIGMA Schizophrenia Consortium, Feng, J, and ZIB Consortium
- Abstract
Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
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- 2024
7. Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study
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Buciuman, M-O, Oeztuerk, OF, Popovic, D, Enrico, P, Ruef, A, Bieler, N, Sarisik, E, Weiske, J, Dong, MS, Dwyer, DB, Kambeitz-Ilankovic, L, Haas, SS, Stainton, A, Ruhrmann, S, Chisholm, K, Kambeitz, J, Riecher-Rossler, A, Upthegrove, R, Schultze-Lutter, F, Salokangas, RKR, Hietala, J, Pantelis, C, Lencer, R, Meisenzahl, E, Wood, SJ, Brambilla, P, Borgwardt, S, Falkai, P, Antonucci, LA, Bertolino, A, Liddle, P, Koutsouleris, N, Buciuman, M-O, Oeztuerk, OF, Popovic, D, Enrico, P, Ruef, A, Bieler, N, Sarisik, E, Weiske, J, Dong, MS, Dwyer, DB, Kambeitz-Ilankovic, L, Haas, SS, Stainton, A, Ruhrmann, S, Chisholm, K, Kambeitz, J, Riecher-Rossler, A, Upthegrove, R, Schultze-Lutter, F, Salokangas, RKR, Hietala, J, Pantelis, C, Lencer, R, Meisenzahl, E, Wood, SJ, Brambilla, P, Borgwardt, S, Falkai, P, Antonucci, LA, Bertolino, A, Liddle, P, and Koutsouleris, N
- Abstract
BACKGROUND: Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level. METHODS: Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up. RESULTS: Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2%, BACslow-4 = 72.9%, BACslow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%. CONCLUSIONS: We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and
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- 2023
8. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.
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Schijven, Dick, Postema, M.C., Fukunaga, M., Matsumoto, J., Miura, K., Zwarte, S.M.C. de, Haren, N.E.M. van, Cahn, W., Hulshoff Pol, H.E., Kahn, R.S., Ayesa-Arriola, R., Ortoz-García de la Foz, V., Tordesillas-Gutierrez, D., Vázquez-Bourgon, J., Crespo-Facorro, B., Alnæs, D., Dahl, A., Westlye, L.T., Agartz, I., Andreassen, O.A., Jönsson, E.G., Kochunov, P., Bruggemann, J.M., Catts, S.V., Michie, P.T., Mowry, B.J., Quidé, Y., Rasser, P.E., Schall, U., Scott, R.J., Carr, V.J., Green, M.J., Henskens, F.A., Loughland, C.M., Pantelis, C., Weickert, C.S., Weickert, T.W., Haan, L. de, Brosch, K., Pfarr, J.K., Ringwald, K.G., Stein, F., Jansen, Andreas, Kircher, T.T.J., Nenadić, I., Krämer, Bernd, Gruber, O., Satterthwaite, T.D., Bustillo, J., Mathalon, D.H., Preda, A., Calhoun, V.D., Ford, J.M., Potkin, S.G., Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, F., Ehrlich, S., Fuentes-Claramonte, P., Garcia-Leon, M.A., Guerrero-Pedraza, A., Salvador, R., Sarró, S., Pomarol-Clotet, E., Ciullo, V., Piras, F., Vecchio, D., Banaj, N., Spalletta, G., Michielse, S., Amelsvoort, T. van, Dickie, E.W., Voineskos, A.N., Sim, K., Ciufolini, S., Dazzan, P., Murray, R.M., Kim, W.S., Chung, Y.C., Andreou, C., Schmidt, A, Borgwardt, S., McIntosh, A.M., Whalley, H.C., Lawrie, S.M., Plessis, S. du, Luckhoff, H.K., Scheffler, F., Emsley, R., Grotegerd, D., Lencer, R., Dannlowski, U., Edmond, J.T., Rootes-Murdy, K., Stephen, J.M., Mayer, A.R., Antonucci, L.A., Fazio, L., Pergola, G., Bertolino, A., Díaz-Caneja, C.M., Janssen, J, Lois, N.G., Arango, C., Tomyshev, A.S., Lebedeva, I., Cervenka, S., Sellgren, C.M., Georgiadis, F., Kirschner, M., Kaiser, S., Hajek, T., Skoch, A., Spaniel, F., Kim, M., Kwak, Y.B., Oh, S., Kwon, J.S., James, A., Bakker, G., Knöchel, C., Stäblein, M., Oertel, V., Uhlmann, A., Howells, F.M., Stein, D.J., Temmingh, H.S., Diaz-Zuluaga, A.M., Pineda-Zapata, J.A., López-Jaramillo, C., Homan, S., Ji, E., Surbeck, W., Homan, P., Fisher, S.E., Franke, B., Glahn, D.C., Gur, R.C., Hashimoto, R., Jahanshad, N., Luders, E., Medland, S.E., Thompson, P.M., Turner, J.A., Erp, T.G. van, Francks, C., Schijven, Dick, Postema, M.C., Fukunaga, M., Matsumoto, J., Miura, K., Zwarte, S.M.C. de, Haren, N.E.M. van, Cahn, W., Hulshoff Pol, H.E., Kahn, R.S., Ayesa-Arriola, R., Ortoz-García de la Foz, V., Tordesillas-Gutierrez, D., Vázquez-Bourgon, J., Crespo-Facorro, B., Alnæs, D., Dahl, A., Westlye, L.T., Agartz, I., Andreassen, O.A., Jönsson, E.G., Kochunov, P., Bruggemann, J.M., Catts, S.V., Michie, P.T., Mowry, B.J., Quidé, Y., Rasser, P.E., Schall, U., Scott, R.J., Carr, V.J., Green, M.J., Henskens, F.A., Loughland, C.M., Pantelis, C., Weickert, C.S., Weickert, T.W., Haan, L. de, Brosch, K., Pfarr, J.K., Ringwald, K.G., Stein, F., Jansen, Andreas, Kircher, T.T.J., Nenadić, I., Krämer, Bernd, Gruber, O., Satterthwaite, T.D., Bustillo, J., Mathalon, D.H., Preda, A., Calhoun, V.D., Ford, J.M., Potkin, S.G., Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, F., Ehrlich, S., Fuentes-Claramonte, P., Garcia-Leon, M.A., Guerrero-Pedraza, A., Salvador, R., Sarró, S., Pomarol-Clotet, E., Ciullo, V., Piras, F., Vecchio, D., Banaj, N., Spalletta, G., Michielse, S., Amelsvoort, T. van, Dickie, E.W., Voineskos, A.N., Sim, K., Ciufolini, S., Dazzan, P., Murray, R.M., Kim, W.S., Chung, Y.C., Andreou, C., Schmidt, A, Borgwardt, S., McIntosh, A.M., Whalley, H.C., Lawrie, S.M., Plessis, S. du, Luckhoff, H.K., Scheffler, F., Emsley, R., Grotegerd, D., Lencer, R., Dannlowski, U., Edmond, J.T., Rootes-Murdy, K., Stephen, J.M., Mayer, A.R., Antonucci, L.A., Fazio, L., Pergola, G., Bertolino, A., Díaz-Caneja, C.M., Janssen, J, Lois, N.G., Arango, C., Tomyshev, A.S., Lebedeva, I., Cervenka, S., Sellgren, C.M., Georgiadis, F., Kirschner, M., Kaiser, S., Hajek, T., Skoch, A., Spaniel, F., Kim, M., Kwak, Y.B., Oh, S., Kwon, J.S., James, A., Bakker, G., Knöchel, C., Stäblein, M., Oertel, V., Uhlmann, A., Howells, F.M., Stein, D.J., Temmingh, H.S., Diaz-Zuluaga, A.M., Pineda-Zapata, J.A., López-Jaramillo, C., Homan, S., Ji, E., Surbeck, W., Homan, P., Fisher, S.E., Franke, B., Glahn, D.C., Gur, R.C., Hashimoto, R., Jahanshad, N., Luders, E., Medland, S.E., Thompson, P.M., Turner, J.A., Erp, T.G. van, and Francks, C.
- Abstract
Item does not contain fulltext, Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.
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- 2023
9. Large- scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium
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Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, Francks, C, Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, and Francks, C
- Abstract
Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.
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- 2023
10. The Role of Social Defeat in Neurological differences in Psychotic Patients
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Malaviya, A., primary, Lalousis, P. A., additional, Wood, S. J., additional, Bertolino, A., additional, Borgwardt, S. B., additional, Brambilla, P., additional, Kambeitz, J., additional, Lencer, R., additional, Pantelis, C., additional, Ruhrmann, S., additional, Salokangas, R. K., additional, Schultze-Lutter, F., additional, Meisenzahl, E., additional, Koutsouleris, N., additional, and Upthegrove, R., additional
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- 2023
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11. The co-localisation with specific neurotransmitter systems discriminates schizophrenia patients from healthy controls
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Hahn, L., primary, Raabe, F.J., additional, Keeser, D., additional, Rossner, M.J., additional, Vetter, C., additional, Hasan, A., additional, Papzova, I., additional, Kambeitz, J., additional, Salokangas, R.K.R., additional, Hietala, J., additional, Bertolino, A., additional, Brambilla, P., additional, Upthegrove, R., additional, Wood, S.J., additional, Lencer, R., additional, Borgwardt, S., additional, Meyer-Lindenberg, A., additional, Meisenzahl, E., additional, Fabbro, F., additional, Schwarz, E., additional, Pantelis, C., additional, Nöthen, M., additional, Mann, M., additional, Paul, R., additional, Ruef, A., additional, and Koutsouleris, N., additional
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- 2023
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12. Modality specificity of audio-visual perceptual decisions alongside supramodal metacognition is revealed by computational modelling in patients with psychotic disorders
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Franzen, L., primary, Eickhoff, S., additional, Schewe, H., additional, Schmitt, L.M., additional, Erb, J., additional, Lencer, R., additional, Lange, C., additional, Andreou, C., additional, Borgwardt, S., additional, and Obleser, J., additional
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- 2023
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13. Distinct gray matter volume signatures of symptom-based patient subgroups in recent-onset psychosis
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Buciuman, M.O., primary, Vetter, C.S., additional, Tovar, S., additional, Weyer, C., additional, Zhutovsky, P., additional, Khuntia, A., additional, Paul, R., additional, Herrera, A., additional, Ruef, A., additional, Ruhrmann, S., additional, Chisholm, K., additional, Kambeitz, J., additional, Riecher-Rössler, A., additional, Upthegrove, R., additional, Schultze-Lutter, F., additional, Salokangas, R.K.R., additional, Hietala, J., additional, Pantelis, C., additional, Lencer, R., additional, Meisenzahl, E., additional, Wood, S.J., additional, Brambilla, P., additional, Borgwardt, S., additional, Falkai, P., additional, Bertolino, A., additional, and Koutsouleris, N., additional
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- 2023
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14. Alterations of functional connectivity dynamics in affective and psychotic disorders
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Hoheisel, L., primary, Kambeitz-Ilankovic, L., additional, Wenzel, J., additional, Haas, S.S., additional, Antonucci, L.A., additional, Ruef, A., additional, Penzel, N., additional, Schultze-Lutter, F., additional, Lichtenstein, T., additional, Rosen, M., additional, Dwyer, D.B., additional, Salokangas, R.K.R., additional, Lencer, R., additional, Brambilla, P., additional, Borgwardt, S., additional, Wood, S.J., additional, Upthegrove, R., additional, Bertolino, A., additional, Ruhrmann, S., additional, Meisenzahl, E., additional, Koutsouleris, N., additional, Fink, G.R., additional, Daun, S., additional, and Kambeitz, J., additional
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- 2023
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15. Neurobiologically Based Stratification of Recent- Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes
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Lalousis, PA, Schmaal, L, Wood, SJ, Reniers, RLEP, Barnes, NM, Chisholm, K, Griffiths, SL, Stainton, A, Wen, J, Hwang, G, Davatzikos, C, Wenzel, J, Kambeitz-Ilankovic, L, Andreou, C, Bonivento, C, Dannlowski, U, Ferro, A, Lichtenstein, T, Riecher-Rossler, A, Romer, G, Upthegrove, R, Lencer, R, Pantelis, C, Ruhrmann, S, Salokangas, RKR, Schultze-Lutter, F, Schmidt, A, Meisenzahl, E, Koutsouleris, N, Dwyer, D, Rosen, M, Bertolino, A, Borgwardt, S, Brambilla, P, Kambeitz, J, Lalousis, PA, Schmaal, L, Wood, SJ, Reniers, RLEP, Barnes, NM, Chisholm, K, Griffiths, SL, Stainton, A, Wen, J, Hwang, G, Davatzikos, C, Wenzel, J, Kambeitz-Ilankovic, L, Andreou, C, Bonivento, C, Dannlowski, U, Ferro, A, Lichtenstein, T, Riecher-Rossler, A, Romer, G, Upthegrove, R, Lencer, R, Pantelis, C, Ruhrmann, S, Salokangas, RKR, Schultze-Lutter, F, Schmidt, A, Meisenzahl, E, Koutsouleris, N, Dwyer, D, Rosen, M, Bertolino, A, Borgwardt, S, Brambilla, P, and Kambeitz, J
- Abstract
BACKGROUND: Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS: HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS: The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS: We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnost
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- 2022
16. Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis
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Penzel, N, Sanfelici, R, Antonucci, LA, Betz, LT, Dwyer, D, Ruef, A, Cho, KIK, Cumming, P, Pogarell, O, Howes, O, Falkai, P, Upthegrove, R, Borgwardt, S, Brambilla, P, Lencer, R, Meisenzahl, E, Schultze-Lutter, F, Rosen, M, Lichtenstein, T, Kambeitz-Ilankovic, L, Ruhrmann, S, Salokangas, RKR, Pantelis, C, Wood, SJ, Quednow, BB, Pergola, G, Bertolino, A, Koutsouleris, N, Kambeitz, J, Penzel, N, Sanfelici, R, Antonucci, LA, Betz, LT, Dwyer, D, Ruef, A, Cho, KIK, Cumming, P, Pogarell, O, Howes, O, Falkai, P, Upthegrove, R, Borgwardt, S, Brambilla, P, Lencer, R, Meisenzahl, E, Schultze-Lutter, F, Rosen, M, Lichtenstein, T, Kambeitz-Ilankovic, L, Ruhrmann, S, Salokangas, RKR, Pantelis, C, Wood, SJ, Quednow, BB, Pergola, G, Bertolino, A, Koutsouleris, N, and Kambeitz, J
- Abstract
Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p < 0.001) of ROP and 59 % of CHR patients. The classifications of CCu based on sMRI and cognition were non-significant (ps > 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.
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- 2022
17. Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning Dementia Praecox Revisited
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Koutsouleris, N, Pantelis, C, Velakoulis, D, McGuire, P, Dwyer, DB, Urquijo-Castro, M-F, Paul, R, Sen, D, Popovic, D, Oeztuerk, O, Kambeitz, J, Salokangas, RKR, Hietala, J, Bertolino, A, Brambilla, P, Upthegrove, R, Wood, SJ, Lencer, R, Borgwardt, S, Maj, C, Nothen, M, Degenhardt, F, Polyakova, M, Mueller, K, Villringer, A, Danek, A, Fassbender, K, Fliessbach, K, Jahn, H, Kornhuber, J, Landwehrmeyer, B, Anderl-Straub, S, Prudlo, J, Synofzik, M, Wiltfang, J, Riedl, L, Diehl-Schmid, J, Otto, M, Meisenzahl, E, Falkai, P, Schroeter, ML, Koutsouleris, N, Pantelis, C, Velakoulis, D, McGuire, P, Dwyer, DB, Urquijo-Castro, M-F, Paul, R, Sen, D, Popovic, D, Oeztuerk, O, Kambeitz, J, Salokangas, RKR, Hietala, J, Bertolino, A, Brambilla, P, Upthegrove, R, Wood, SJ, Lencer, R, Borgwardt, S, Maj, C, Nothen, M, Degenhardt, F, Polyakova, M, Mueller, K, Villringer, A, Danek, A, Fassbender, K, Fliessbach, K, Jahn, H, Kornhuber, J, Landwehrmeyer, B, Anderl-Straub, S, Prudlo, J, Synofzik, M, Wiltfang, J, Riedl, L, Diehl-Schmid, J, Otto, M, Meisenzahl, E, Falkai, P, and Schroeter, ML
- Abstract
IMPORTANCE: The behavioral and cognitive symptoms of severe psychotic disorders overlap with those seen in dementia. However, shared brain alterations remain disputed, and their relevance for patients in at-risk disease stages has not been explored so far. OBJECTIVE: To use machine learning to compare the expression of structural magnetic resonance imaging (MRI) patterns of behavioral-variant frontotemporal dementia (bvFTD), Alzheimer disease (AD), and schizophrenia; estimate predictability in patients with bvFTD and schizophrenia based on sociodemographic, clinical, and biological data; and examine prognostic value, genetic underpinnings, and progression in patients with clinical high-risk (CHR) states for psychosis or recent-onset depression (ROD). DESIGN, SETTING, AND PARTICIPANTS: This study included 1870 individuals from 5 cohorts, including (1) patients with bvFTD (n = 108), established AD (n = 44), mild cognitive impairment or early-stage AD (n = 96), schizophrenia (n = 157), or major depression (n = 102) to derive and compare diagnostic patterns and (2) patients with CHR (n = 160) or ROD (n = 161) to test patterns' prognostic relevance and progression. Healthy individuals (n = 1042) were used for age-related and cohort-related data calibration. Data were collected from January 1996 to July 2019 and analyzed between April 2020 and April 2022. MAIN OUTCOMES AND MEASURES: Case assignments based on diagnostic patterns; sociodemographic, clinical, and biological data; 2-year functional outcomes and genetic separability of patients with CHR and ROD with high vs low pattern expression; and pattern progression from baseline to follow-up MRI scans in patients with nonrecovery vs preserved recovery. RESULTS: Of 1870 included patients, 902 (48.2%) were female, and the mean (SD) age was 38.0 (19.3) years. The bvFTD pattern comprising prefrontal, insular, and limbic volume reductions was more expressed in patients with schizophrenia (65 of 157 [41.2%]) and major depressi
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- 2022
18. The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis
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Schwarzer, JM, Meyhoefer, I, Antonucci, LA, Kambeitz-Ilankovic, L, Surmann, M, Bienek, O, Romer, G, Dannlowski, U, Hahn, T, Korda, A, Dwyer, DB, Ruef, A, Haas, SS, Rosen, M, Lichtenstein, T, Ruhrmann, S, Kambeitz, J, Salokangas, RKR, Pantelis, C, Schultze-Lutter, F, Meisenzahl, E, Brambilla, P, Bertolino, A, Borgwardt, S, Upthegrove, R, Koutsouleris, N, Lencer, R, Schwarzer, JM, Meyhoefer, I, Antonucci, LA, Kambeitz-Ilankovic, L, Surmann, M, Bienek, O, Romer, G, Dannlowski, U, Hahn, T, Korda, A, Dwyer, DB, Ruef, A, Haas, SS, Rosen, M, Lichtenstein, T, Ruhrmann, S, Kambeitz, J, Salokangas, RKR, Pantelis, C, Schultze-Lutter, F, Meisenzahl, E, Brambilla, P, Bertolino, A, Borgwardt, S, Upthegrove, R, Koutsouleris, N, and Lencer, R
- Abstract
Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrit
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- 2022
19. Relationships between global functioning and neuropsychological predictors in subjects at high risk of psychosis or with a recent onset of depression
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Squarcina, L, Kambeitz-Ilankovic, L, Bonivento, C, Prunas, C, Oldani, L, Wenzel, J, Ruef, A, Dwyer, D, Ferro, A, Borgwardt, S, Kambeitz, J, Lichtenstein, TK, Meisenzahl, E, Pantelis, C, Rosen, M, Upthegrove, R, Antonucci, LA, Bertolino, A, Lencer, R, Ruhrmann, S, Salokangas, RRK, Schultze-Lutter, F, Chisholm, K, Stainton, A, Wood, SJ, Koutsouleris, N, Brambilla, P, Squarcina, L, Kambeitz-Ilankovic, L, Bonivento, C, Prunas, C, Oldani, L, Wenzel, J, Ruef, A, Dwyer, D, Ferro, A, Borgwardt, S, Kambeitz, J, Lichtenstein, TK, Meisenzahl, E, Pantelis, C, Rosen, M, Upthegrove, R, Antonucci, LA, Bertolino, A, Lencer, R, Ruhrmann, S, Salokangas, RRK, Schultze-Lutter, F, Chisholm, K, Stainton, A, Wood, SJ, Koutsouleris, N, and Brambilla, P
- Abstract
OBJECTIVE: Psychotic disorders are frequently associated with decline in functioning and cognitive difficulties are observed in subjects at clinical high risk (CHR) for psychosis. In this work, we applied automatic approaches to neurocognitive and functioning measures, with the aim of investigating the link between global, social and occupational functioning, and cognition. METHODS: 102 CHR subjects and 110 patients with recent onset depression (ROD) were recruited. Global assessment of functioning (GAF) related to symptoms (GAF-S) and disability (GAF-D). and global functioning social (GF-S) and role (GF-R), at baseline and of the previous month and year, and a set of neurocognitive measures, were used for classification and regression. RESULTS: Neurocognitive measures related to GF-R at baseline (r = 0.20, p = 0.004), GF-S at present (r = 0.14, p = 0.042) and of the past year (r = 0.19, p = 0.005), for GAF-F of the past month (r = 0.24, p < 0.001) and GAF-D of the past year (r = 0.28, p = 0.002). Classification reached values of balanced accuracy of 61% for GF-R and GAF-D. CONCLUSION: We found that neurocognition was related to psychosocial functioning. More specifically, a deficit in executive functions was associated to poor social and occupational functioning.
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- 2022
20. Brain based BMIgap as a new tool to correlate obesity and neural alterations – a multicohort study
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Khuntia, A., Paul, R., Andreassen, O.A., Degenhard, F., Eils, R., Erdmann, J., Herrmann, C., Hofmann-Apitius, M., Kaufmann, T., Kodamullil, A.T., Mucha, S., Nöthen, M.M., Pedersen, M.L., Quintero, A., Schunkert, H., Sharma, A., Tost, H., Westlye, L.T., Zhang, Y., Kambeitz, J., Salokangas, R.K.R., Hietala, J., Bertolino, A., Brambilla, P., Upthegrove, R., Wood, S.J., Lencer, R., Borgwardt, S., Meyer-Lindenberg, A., Meisenzahl, E., Falkai, P., Schwarz, E., and Koutsouleris, N.
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- 2022
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21. Transdiagnostic individualised brain texture changes that are associated with symptom severity using contrast feature map
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Korda, A., Andreou, C., Ruef, A., Lencer, R., Schmidt, A., Dannlowski, U., Kambeitz-Ilankovic, L., Dwyer, D.B., Kambeitz, J., Wenzel, J., Ruhrmann, S., Salokangas, R.K.R., Pantelis, C., Schultze-Lutter, F., Meisenzahl, E., Brambilla, P., Lalousis, P.A., Upthegrove, R., Koutsouleris, N., and Borgwardt, S.
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- 2022
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22. Structural alterations in psychotic disorders co-localize with serotonergic and dopaminergic neurotransmitter systems
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Hahn, L., Raabe, F.J., Keeser, D., Rossner, M.J., Hasan, A., Papzova, I., Kambeitz, J., Salokangas, R.K.R., Hietala, J., Bertolino, A., Brambilla, P., Upthegrove, R., Wood, S.J., Lencer, R., Borgwardt, S., Meyer-Lindenberg, A., Meisenzahl, E., Fabbro, F., Schwarz, E., Pantelis, C., Nöthen, M.M., Mann, M., Ruef, A., Paul, R., Falkai, P., and Koutsouleris, N.
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- 2022
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23. Identification of subtle visual dysfunctions in recent onset psychosis and clinical high-risk state using entropy and energy feature maps
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Korda, A., Meyhöfer, I., Romer, G., Dannlowski, U., Andreou, C., Schmidt, A., Kambeitz-Ilankovic, L., Dwyer, D.B., Ruef, A., Kambeitz, J., Ruhrmann, S., Salokangas, R.K.R., Pantelis, C., Schultze-Lutter, F., Meisenzahl, E., Brambilla, P., Bertolino, A., Upthegrove, R., Koutsouleris, N., Borgwardt, S., and Lencer, R.
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- 2022
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24. Multiband fractional amplitude of low-frequency fluctuations predicts social functioning transdiagnostically in the clinical high-risk for psychosis state and recent-onset depression
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Buciuman, M.O., Haas, S.S., Antonucci, L.A., Kambeitz-Ilankovic, L., Ruef, A., Hasan, A., Borgwardt, S., Schwarz, E., Kambeitz, J., Meyer-Lindenberg, A., Pantelis, C., Degenhardt, F., Noethen, M., Lencer, R., Fabbro, F., Bertolino, A., Brambilla, P., Upthegrove, R., Wood, S.J., Falkai, P., Meisenzahl-Lechner, E., Hietala, J., Salokangas, R.K.R., Dwyer, D.B., and Koutsouleris, N.
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- 2022
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25. Exploring Associations between Grey Matter Volume and Clinical High-Risk for Psychosis: A Transdiagnostic Study Utilizing the NAPLS-2 Risk Calculator in the PRONIA Cohort.
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Neuner, L.-M., Hahn, L., Kambeitz, J., Salokangas, R. K., Hietala, J., Bertolino, A., Borgwardt, S., Brambilla, P., Upthegrove, R., Wood, S. J., Lencer, R., Meisenzahl, E., Falkai, P., Cannon, T. D., and Koutsouleris, N.
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DISEASE risk factors ,MAGNETIC resonance imaging ,NEURAL development ,PROGNOSTIC tests ,MULTIPLE comparisons (Statistics) - Abstract
Introduction: The clinical high-risk state for psychosis (CHR) is associated with alterations in grey matter volume (GMV) in various regions such as the hippocampus (Vissink et al. BP:GOS 2022; 2(2) 147-152). Within the scope of the North American Prodrome Longitudinal Study (NAPLS-2; Cannon et al. AM J Psychiatry 2016; 173(10), 980-988), a publicly available risk calculator based on clinical variables was developed to assess the likelihood of individuals to transition to psychosis within a 2-year period. Objectives: In the current study, we aim to examine the association between GMV and NAPLS-2 risk scores calculated for individuals with CHR and recent-onset depression (ROD), taking a transdiagnostic approach on the transition to psychosis. Methods: The sample consisted of 315 CHR (M = 23.85, SD = ± 5.64; female: 164) and 295 ROD (M = 25.11, SD = ± 6.21; female: 144) patients from the multi-site Personalised Prognostic Tools for Early Psychosis Management (PRONIA) Study (Koutsouleris et al. JAMA Psychiatry 2018; 57(11), 1156-1172). Risk scores were calculated using the six clinical and neurocognitive variables included in the NAPLS-2 risk calculator that were significant for predicting psychosis. Further, we derived smoothed GMV maps from T1-weighted structural magnetic resonance imaging using a full width at half maximum kernel size of 8 mm. We employed a multiple regression design in SPM12 to examine associations between risk scores and GMV. On the whole-brain level, we calculated permutation-based threshold-free cluster enhancement (TFCE) contrasts using the TFCE toolbox. Additionally, we calculated t-contrasts within a region-of-interest (ROI) analysis encompassing the hippocampus. All results were thresholded at p < 0.05 with family wise error correction to address multiple comparisons. Results: Our analysis revealed that linear GMV increases in the right middle and superior frontal gyrus (k
E = 2726 voxels) were significantly associated with higher risk for psychosis transition within two years (see figure 1, highlighted in blue). In the ROI analysis, we found a significant negative linear association between GMV decreases in the left hippocampus (kE = 353 voxels) and higher risk for psychosis transition (see figure 1, highlighted in red). Image: Conclusions: GMV reductions in the hippocampus have frequently been observed in CHR and psychosis patients (Vissink et al. BP:GOS 2022; 2(2) 147-152), therefore our results further highlight the crucial role of this region in the progression of the disease. There is limited evidence on GMV increases in CHR patients. However, the GMV increase we found in the frontal pole may reflect compensatory mechanisms of the brain in the development of psychosis. In addition, we were able to provide biological validation of the NAPLS-2 risk calculator and its assessment of risk for transition to psychosis. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]- Published
- 2024
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26. Multivariate associations between psychiatric drug intake and grey matter volume changes in individuals at early stages of psychosis and depression.
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Weyer, C., Popovic, D., Ruef, A., Hahn, L., Sarişik, E., Fanning, J., Kambeitz, J., Salokangas, R. K., Hietala, J., Bertolino, A., Borgwardt, S., Brambilla, P., Upthegrove, R., Wood, S. J., Lencer, R., Meisenzahl, E., Falkai, P., and Koutsouleris, N.
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PSYCHIATRIC drugs ,SUSTAINABILITY ,MAGNETIC resonance imaging ,TEMPORAL lobe ,BRAIN anatomy ,FIVE-factor model of personality - Abstract
Introduction: Psychiatric drugs, including antipsychotics and antidepressants, are widely prescribed, even in young and adolescent populations at early or subthreshold disease stages. However, their impact on brain structure remains elusive. Elucidating the relationship between psychotropic medication and structural brain changes could enhance the understanding of the potential benefits and risks associated with such treatment. Objectives: Investigation of the associations between psychiatric drug intake and longitudinal grey matter volume (GMV) changes in a transdiagnostic sample of young individuals at early stages of psychosis or depression using an unbiased data-driven approach. Methods: The study sample comprised 247 participants (mean [SD] age = 25.06 [6.13] years, 50.61% male), consisting of young, minimally medicated individuals at clinical high-risk states for psychosis, individuals with recent-onset depression or psychosis, and healthy control individuals. Structural magnetic resonance imaging was used to obtain whole-brain voxel-wise GMV for all participants at two timepoints (mean [SD] time between scans = 11.15 [4.93] months). The multivariate sparse partial least squares (SPLS) algorithm (Monteiro et al. JNMEDT 2016; 271:182-194) was embedded in a nested cross-validation framework to identify parsimonious associations between the cumulative intake of psychiatric drugs, including commonly prescribed antipsychotics and antidepressants, and change in GMV between both timepoints, while additionally factoring in age, sex, and diagnosis. Furthermore, we correlated the retrieved SPLS results to personality domains (NEO-FFI) and childhood trauma (CTQ). Results: SPLS analysis revealed significant associations between the antipsychotic classes of benzamides, butyrophenones and thioxanthenes and longitudinal GMV decreases in cortical regions including the insula, posterior superior temporal sulcus as well as cingulate, postcentral, precentral, orbital and frontal gyri (Figure 1A-C). These brain regions corresponded most closely to the dorsal and ventral attention, somatomotor, salience and default network (Figure 1D). Furthermore, the medication signature was negatively associated with the personality domains extraversion, agreeableness and conscientiousness and positively associated with the CTQ domains emotional and physical neglect. Image: Conclusions: Psychiatric drug intake over a period of one year was linked to distinct GMV reductions in key cortical hubs. These patterns were already visible in young individuals at early or subthreshold stages of mental illness and were further linked to childhood neglect and personality traits. Hence, a better and more in-depth understanding of the structural brain implications of medicating young and adolescent individuals might lead to more cautious, sustainable and targeted treatment strategies. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]
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- 2024
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27. Spektrum neurologischer und psychiatrischer Manifestationen bei der SCA17
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Kock, N., Hagenah, J., Hiller, A., Lencer, R., Lasek, K., Steinlechner, S., Zühlke, C., Nitschke, M.F., Binkofski, F., Klein, C., Wolters, A., and Rolfs, A.
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- 2024
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28. Pathoanatomic correlates of psychiatric symptoms in PINK1 mutation carriers
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Reetz, K, Lencer, R, Steinlechner, S, Gaser, C, Hagenah, J, Büchel, C, Djarmati, A, Siebner, HR, Klein, C, and Binkofski, F
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- 2024
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29. <italic>We like to move it</italic> – patients with schizophrenia spectrum disorders are impaired in estimating their physical fitness levels and benefit from individualized exercise.
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Rippe, Wido, Weisner, L., Ewen, J., Mench, P., Koppius, T., Borgwardt, S., Tari, B., Heath, M., Sprenger, A., Wilms, B., and Lencer, R.
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PHYSICAL fitness , *SPORTS psychology , *PEOPLE with schizophrenia , *AEROBIC exercises , *EXERCISE therapy - Abstract
Background: People with schizophrenia spectrum disorders (SSD) engage less in physical activity than healthy individuals. The impact of subjectively assessed physical fitness levels on motivation for sports engagement and its relation to objective fitness parameters in SSD is unclear. Methods: 25 patients with SSD (P-SSD) and 24 healthy controls (H-CON) participated in a randomized controlled study. Individual anaerobic thresholds (AT) were determined by an incremental exercise test and on separate days, aerobic exercise (cycling at 80% of workload at AT) and non-exercise control (sitting on an ergometer without cycling) sessions were performed. Demographic, clinical and objective physical fitness data (i.e., weekly physical activity, workload at AT, heart rate) were collected. Subjective physical fitness parameters were assessed before and after exercise and control sessions. Results: Weekly physical activity in P-SSD was lower than in H-CON (
p < 0.05) attributed to reduced engagement in sport activities (p < 0.001). Workload and percentage of predicted maximal heart rate at AT were also reduced in P-SSD compared to H-CON (bothp < 0.05). Although objective and subjective physical fitness parameters were related in H-CON (p < 0.01), this relationship was absent in P-SSD. However, during exercise sessions subjective physical fitness ratings increased to a stronger extent in P-SSD than H-CON (p < 0.05). Conclusion: The missing relationship between subjective and objective physical fitness parameters in people with SSD may represent a barrier for stronger engagement in physical activity. Accordingly, supervised exercise interventions with individually adjusted workload intensity may support realistic subjective fitness estimations and enhance motivation for sports activity in individuals with SSD. [ABSTRACT FROM AUTHOR]- Published
- 2024
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30. Neurocognitive dysfunction in adolescents with recent onset major depressive disorder: a cross-sectional comparative study.
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Bienek O, Allott K, Antonucci L, Bertolino A, Bonivento C, Borgwardt S, Brambilla P, Chisholm K, Dannlowski U, Lichtenstein TK, Kambeitz J, Kambeitz-Ilankovic L, Koutsouleris N, Lencer R, Griffiths SL, Maggioni E, Meisenzahl E, Pantelis C, Rosen M, Ruhrmann S, Salokangas RKR, Stainton A, Surmann M, Upthegrove R, Wenzel J, Wood SJ, Romer G, and Müller JM
- Abstract
The aim of this study was to examine the neurocognitive deficits associated with the first episode of major depressive disorder (recent onset depression, ROD) in adolescents as compared to adult patients. Cross-sectional neurocognitive data from the baseline assessments of the PRONIA study with N = 650 (55.31% females) were analyzed. Based on a principal component analysis of eleven neurocognitive tests, we constructed an overall neurocognitive performance (NP) score. We examined mean score differences in NP between the groups of healthy controls (HC) and ROD and between adolescents (15-21 years) and adults (22-40 years) within a GLM approach. This accounts for unbalanced data with focus on interaction effects while controlling for effects of medication and educational years. Our results show lower NP for the ROD as compared to the HC group (d = - 0.29, p = .046) and lower NP for the adolescent group as compared to the adult group (d = - 0.29; p < .039). There was no interaction between these two group effects (F = 1.11; p = .29). Our findings suggest that the detrimental effect of ROD on neurocognitive functioning is comparable in adolescent and adult patients, since lower scores in adolescent patients are explained by effects of age and education. Neurocognitive impairment is an under addressed issue in clinical treatment guidelines for adolescent MDD. We suggest efficient monitoring in clinical practice by using an aggregate of the Digit Symbol Substitution Test and the Trail Making Test B, which highly correlated with the overall score of NP (r = 0.82)., (© 2024. The Author(s).)
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- 2024
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31. [The Well-informed Patient: a Survey on Patients' Initiative in Seeking Disease-related Information].
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Telger A, Lencer R, Arolt V, and Notzon S
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- Humans, Male, Female, Adult, Middle Aged, Aged, Young Adult, Surveys and Questionnaires, Mental Disorders psychology, Mental Disorders therapy, Information Seeking Behavior, Internet
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Internet and print media are frequently used by laypersons to learn about health issues. The objective of this study was to find out whether people with mental disorders showed a special pattern of usage. Where and why do they seek for information about their disorder? How do they experience their search? In semi-standardized interviews, we surveyed 200 psychiatric inpatients. Only patients of the following diagnostic groups were included: 1. Schizophrenia, schizotypal and delusional disorders (F20-F29), 2. Affective disorders (F30-F39) and 3. Disorders of adult personality and behavior (F60-F69). We focused on the sources the patients had used and the experiences they had in the course of their internet search. The vast majority had already searched for information about psychiatry, psychology or medication via internet or in print media. Most participants described positive emotions while reading. More than two-thirds rated the information as useful. Only 10 participants discontinued or rejected therapeutic measures due to information they had gained. Patients with personality disorders were significantly more likely than other patients to attribute their symptoms to a wrong diagnosis after seeking for information. Overall, psychiatric patients mostly experience helpful effects of reading medical information. In rare cases there are negative effects, e. g. negative emotions, discontinuation of therapy or an incorrect assessment of one's own illness. Further research is required in order to find out how the use of internet by people with mental disorders, which is already successful in many cases, can be improved even further., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)
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- 2024
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32. Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study.
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Liu N, Lencer R, Andreou C, Avram M, Handels H, Zhang W, Hui S, Yang C, Borgwardt S, Sweeney JA, Lui S, and Korda AI
- Abstract
Background: Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding., Methods: T1-weighted brain images of 150 first-episode antipsychotic-naïve schizophrenia (FES) patients and 161 healthy comparison participants (HC) were examined. The Chaos analysis approach was applied to detect alterations in brain structural complexity using the largest Lyapunov exponent (Lambda) as the key measure. Then, the Lambda spatial series was mapped in the frequency domain using the correlation of the Morlet wavelet to reflect cortical folding complexity., Results: A widespread voxel-wise decrease in Lambda values in space and frequency domains was observed in FES, especially in frontal, parietal, temporal, limbic, basal ganglia, thalamic, and cerebellar regions. The widespread decrease indicates a general loss of brain topological complexity and cortical folding. An additional pattern of increased Lambda values in certain regions highlights the redistribution of complexity measures in schizophrenia at an early stage with potential progression as the illness advances. Strong correlations were found between the duration of untreated psychosis and Lambda values related to the cerebellum, temporal, and occipital gyri., Conclusions: Our findings support the notion that defining brain complexity by non-linear dynamic analyses offers a novel approach for identifying structural brain alterations related to the early stages of schizophrenia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
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- 2024
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33. Aberrant inhibitory control as a transdiagnostic dimension of mental disorders - A meta-analysis of the antisaccade task in different psychiatric populations.
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Breuer F, Meyhöfer I, Lencer R, Sprenger A, Roesmann K, Schag K, Dannlowski U, and Leehr EJ
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- Humans, Executive Function physiology, Psychomotor Performance physiology, Inhibition, Psychological, Mental Disorders physiopathology, Saccades physiology
- Abstract
This meta-analysis examined inhibitory control performance in the antisaccade task across mental disorders. Following PRISMA guidelines, we analyzed data from k = 146 studies (n = 13,807 participants) on antisaccade performance. Effect sizes were estimated using random-effects models and restricted maximum-likelihood estimation, with robustness tests for study heterogeneity and publication bias. Most disorders displayed elevated error rates, with schizophrenia showing the greatest impairments, followed by autism spectrum disorder, bipolar disorder and attention deficit hyperactivity disorder. Small to medium impairments were also found in eating disorders, major depressive disorder, obsessive-compulsive disorder and substance use disorder. Results were robust against corrections for publication bias and largely unaffected by confounding variables. Prolonged latencies were observed in schizophrenia, attention deficit hyperactivity disorder, bipolar disorder and obsessive compulsive disorder, with smaller and less robust effect sizes. Results indicate inhibitory control deficits in the antisaccade task across mental disorders, especially evident for error rates. While present in most disorders, results imply varying degrees of impairments, ranging from small to large in effect sizes, with largest impairments in schizophrenia., Competing Interests: Conflict of interest All authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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- 2024
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34. Anhedonia as a Potential Transdiagnostic Phenotype With Immune-Related Changes in Recent-Onset Mental Health Disorders.
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Lalousis PA, Malaviya A, Khatibi A, Saberi M, Kambeitz-Ilankovic L, Haas SS, Wood SJ, Barnes NM, Rogers J, Chisholm K, Bertolino A, Borgwardt S, Brambilla P, Kambeitz J, Lencer R, Pantelis C, Ruhrmann S, Salokangas RKR, Schultze-Lutter F, Schmidt A, Meisenzahl E, Dwyer D, Koutsouleris N, Upthegrove R, and Griffiths SL
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- Humans, Male, Female, Adult, Young Adult, Brain diagnostic imaging, Brain physiopathology, Inflammation, Phenotype, Depression immunology, Depression physiopathology, Machine Learning, Anhedonia physiology, Magnetic Resonance Imaging, Psychotic Disorders immunology, Psychotic Disorders physiopathology, Psychotic Disorders diagnostic imaging
- Abstract
Background: Chronic low-grade inflammation is observed across mental disorders and is associated with difficult-to-treat-symptoms of anhedonia and functional brain changes, reflecting a potential transdiagnostic dimension. Previous investigations have focused on distinct illness categories in people with enduring illness, but few have explored inflammatory changes. We sought to identify an inflammatory signal and the associated brain function underlying anhedonia among young people with recent-onset psychosis and recent-onset depression., Methods: Resting-state functional magnetic resonance imaging, inflammatory markers, and anhedonia symptoms were collected from 108 (mean [SD] age = 26.2 [6.2] years; female = 50) participants with recent-onset psychosis (n = 53) and recent-onset depression (n = 55) from the European Union/Seventh Framework Programme-funded PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Time series were extracted using the Schaefer atlas, defining 100 cortical regions of interest. Using advanced multimodal machine learning, an inflammatory marker model and a functional connectivity model were developed to classify participants into an anhedonic group or a normal hedonic group., Results: A repeated nested cross-validation model using inflammatory markers classified normal hedonic and anhedonic recent-onset psychosis/recent-onset depression groups with a balanced accuracy of 63.9% and an area under the curve of 0.61. The functional connectivity model produced a balanced accuracy of 55.2% and an area under the curve of 0.57. Anhedonic group assignment was driven by higher levels of interleukin 6, S100B, and interleukin 1 receptor antagonist and lower levels of interferon gamma, in addition to connectivity within the precuneus and posterior cingulate., Conclusions: We identified a potential transdiagnostic anhedonic subtype that was accounted for by an inflammatory profile and functional connectivity. Results have implications for anhedonia as an emerging transdiagnostic target across emerging mental disorders., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
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- 2024
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35. Gray matter matters: Cognitive stability and flexibility in schizophrenia spectrum disorder.
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Herkströter F, Zahedi A, Standke I, Dannlowski U, Lencer R, Schubotz RI, and Trempler I
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- Humans, Male, Adult, Female, Cognitive Dysfunction diagnostic imaging, Cognitive Dysfunction physiopathology, Cognitive Dysfunction pathology, Middle Aged, Executive Function physiology, Caudate Nucleus diagnostic imaging, Caudate Nucleus pathology, Young Adult, Schizophrenia diagnostic imaging, Schizophrenia pathology, Schizophrenia physiopathology, Magnetic Resonance Imaging, Gray Matter pathology, Gray Matter diagnostic imaging, Prefrontal Cortex diagnostic imaging, Prefrontal Cortex pathology
- Abstract
Cognitive dysfunction constitutes a core characteristic of schizophrenia spectrum disorders (SZ). Specifically, deficits in updating generative models (i.e., cognitive flexibility) and shielding against distractions (i.e., cognitive stability) are considered critical contributors to cognitive impairment in these patients. Here, we examined the structural integrity of frontostriatal networks and their associations with reduced cognitive stability and flexibility in SZ patients. In a sample of 21 patients diagnosed with SZ and 22 healthy controls, we measured gray matter volume (GMV) using structural MRI. Further, cognitive stability and flexibility were assessed using a switch-drift paradigm, quantifying the successful ignoring of distracters and detection of rule switches. Compared to controls, patients showed significantly smaller GMV in the whole brain and three predefined regions of interest: the medial prefrontal cortex (mPFC), inferior frontal gyrus (IFG), and caudate nucleus (CN). Notably, GMV in these areas positively correlated with correct rule-switch detection but not with ignoring rule-compatible drifts. Further, the volumetric differences between SZ patients and controls were statistically explainable by considering the behavioral performance in the switch-drift task. Our results indicate that morphological abnormalities in frontostriatal networks are associated with deficient flexibility in SZ patients and highlight the necessity of minimizing neurodevelopmental and progressive brain atrophy in this population., (© 2024 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)
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- 2024
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36. CutFEM-based MEG forward modeling improves source separability and sensitivity to quasi-radial sources: A somatosensory group study.
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Erdbrügger T, Höltershinken M, Radecke JO, Buschermöhle Y, Wallois F, Pursiainen S, Gross J, Lencer R, Engwer C, and Wolters C
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- Humans, Adult, Male, Female, Models, Neurological, Brain Mapping methods, Somatosensory Cortex physiology, Somatosensory Cortex diagnostic imaging, Young Adult, Magnetoencephalography methods, Evoked Potentials, Somatosensory physiology, Finite Element Analysis
- Abstract
Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so-called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM's meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6-compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post-stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6-compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3-compartment BEM. They also demonstrate higher quasi-radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
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- 2024
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37. Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders.
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Hoheisel L, Kambeitz-Ilankovic L, Wenzel J, Haas SS, Antonucci LA, Ruef A, Penzel N, Schultze-Lutter F, Lichtenstein T, Rosen M, Dwyer DB, Salokangas RKR, Lencer R, Brambilla P, Borgwardt S, Wood SJ, Upthegrove R, Bertolino A, Ruhrmann S, Meisenzahl E, Koutsouleris N, Fink GR, Daun S, and Kambeitz J
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- Humans, Male, Female, Adult, Young Adult, Brain physiopathology, Brain diagnostic imaging, Neural Pathways physiopathology, Connectome, Adolescent, Nerve Net physiopathology, Nerve Net diagnostic imaging, Psychotic Disorders physiopathology, Psychotic Disorders diagnostic imaging, Magnetic Resonance Imaging
- Abstract
Background: Patients with psychosis and patients with depression exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC) allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders., Methods: We report the analysis of dFC in a large sample including 127 patients at clinical high risk for psychosis, 142 patients with recent-onset psychosis, 134 patients with recent-onset depression, and 256 healthy control participants. A sliding window-based technique was used to calculate the time-dependent FC in resting-state magnetic resonance imaging data, followed by clustering to reveal recurrent FC states in each diagnostic group., Results: We identified 5 unique FC states, which could be identified in all groups with high consistency (mean r = 0.889 [SD = 0.116]). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly connected FC state in patients with recent-onset depression (p < .0005) compared with the other groups and a common increase in the lifetime of an FC state characterized by high sensorimotor and cingulo-opercular connectivities in all patient groups compared with the healthy control group (p < .0002). Canonical correlation analysis revealed a mode that exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < .0029), which was associated with positive psychosis symptom severity and several dFC parameters., Conclusions: Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression and psychosis and clinical risk states., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
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- 2024
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38. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.
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Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye LT, Richard G, Fernandez-Cabello S, Parker N, Andreassen OA, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin CP, Tsai SJ, Rodrigue AL, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León MÁ, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul AS, Uslu O, Burhanoglu BB, Uyar Demir A, Rootes-Murdy K, Calhoun VD, Sim K, Green M, Quidé Y, Chung YC, Kim WS, Sponheim SR, Demro C, Ramsay IS, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park MTM, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen TE, Rossell SL, Hughes M, Woods W, Carruthers SP, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen DD, Preda A, Thomopoulos SI, Jahanshad N, Cui LB, Yao D, Thompson PM, Turner JA, van Erp TGM, Cheng W, and Feng J
- Subjects
- Humans, Male, Female, Adult, Machine Learning, Middle Aged, Brain diagnostic imaging, Brain pathology, Cross-Sectional Studies, Europe, Neuroimaging, Reproducibility of Results, North America, Hippocampus diagnostic imaging, Hippocampus pathology, Schizophrenia diagnostic imaging, Schizophrenia pathology, Algorithms, Magnetic Resonance Imaging, Gray Matter diagnostic imaging, Gray Matter pathology
- Abstract
Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors., (© 2024. The Author(s).)
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- 2024
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39. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis.
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Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr EJ, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher TTJ, Keedy SS, Bishop JR, Ivleva EI, McDowell JE, Reilly JL, Hill SK, Pearlson GD, Tamminga CA, Keshavan MS, Gershon ES, Clementz BA, Sweeney JA, Hahn T, Dannlowski U, and Lencer R
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- Humans, Male, Female, Adult, Young Adult, Bipolar Disorder diagnosis, Bipolar Disorder physiopathology, Middle Aged, Case-Control Studies, Adolescent, Pursuit, Smooth physiology, Psychotic Disorders diagnosis, Psychotic Disorders physiopathology, Biomarkers
- Abstract
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis., (© 2024. The Author(s).)
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- 2024
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40. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study.
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Georgiadis F, Larivière S, Glahn D, Hong LE, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens FA, Green MJ, Cairns MJ, Michie PT, Rasser PE, Catts S, Tooney P, Scott RJ, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite TD, Karuk A, Pomarol-Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay IS, Sponheim SR, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein DJ, Howells F, Temmingh HS, Diaz Zuluaga AM, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk SL, Thompson P, van Erp TGM, Turner JA, Bernhardt B, and Kirschner M
- Subjects
- Humans, Adult, Female, Male, Cerebral Cortex pathology, Cerebral Cortex physiopathology, Nerve Net pathology, Nerve Net physiopathology, Nerve Net diagnostic imaging, Brain pathology, Brain physiopathology, Middle Aged, Neural Pathways physiopathology, Neural Pathways pathology, Young Adult, Schizophrenia pathology, Schizophrenia physiopathology, Connectome methods, Magnetic Resonance Imaging methods
- Abstract
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia., (© 2024. The Author(s).)
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- 2024
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41. Neurocognitive skills and vulnerability for psychosis in depression and across the psychotic spectrum: findings from the PRONIA Consortium - CORRIGENDUM.
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Bonivento C, Kambeitz-Ilankovic L, Maggioni E, Borgwardt S, Lencer R, Meisenzahl E, Kambeitz J, Ruhrmann S, Salokangas RKR, Bertolino A, Stainton A, Wenzel J, Pantelis C, Wood SJ, Upthegrove R, Koutsouleris N, and Brambilla P
- Subjects
- Humans, Depressive Disorder, Psychotic Disorders
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- 2024
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42. Distinct multimodal biological and functional profiles of symptom-based subgroups in recent-onset psychosis.
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Koutsouleris N, Buciuman MO, Vetter CS, Weyer CFC, Zhutovsky P, Perdomo ST, Khuntia A, Milaneschi Y, Popovic D, Ruef A, Dwyer D, Chisholm K, Kambeitz L, Antonucci L, Ruhrmann S, Kambeitz J, Riecher-Rössler A, Upthegrove R, Salokangas R, Hietala J, Pantelis C, Lencer R, Meisenzahl E, Wood S, Brambilla P, Borgwardt S, Bertolino A, and Falkai P
- Abstract
Symptom heterogeneity characterizes psychotic disorders and hinders the delineation of underlying biomarkers. Here, we identify symptom-based subtypes of recent-onset psychosis (ROP) patients from the multi-center PRONIA (Personalized Prognostic Tools for Early Psychosis Management) database and explore their multimodal biological and functional signatures. We clustered N = 328 ROP patients based on their maximum factor scores in an exploratory factor analysis on the Positive and Negative Syndrome Scale items. We assessed inter-subgroup differences and compared to N = 464 healthy control (HC) individuals regarding gray matter volume (GMV), neurocognition, polygenic risk scores, and longitudinal functioning trajectories. Finally, we evaluated factor stability at 9- and 18-month follow-ups. A 4-factor solution optimally explained symptom heterogeneity, showing moderate longitudinal stability. The ROP-MOTCOG ( Motor/Cognition ) subgroup was characterized by GMV reductions within salience, control and default mode networks, predominantly throughout cingulate regions, relative to HC individuals, had the most impaired neurocognition and the highest genetic liability for schizophrenia. ROP-SOCWD ( Social Withdrawal ) patients showed GMV reductions within medial fronto-temporal regions of the control, default mode, and salience networks, and had the lowest social functioning across time points. ROP-POS ( Positive ) evidenced GMV decreases in salience, limbic and frontal regions of the control and default mode networks. The ROP-AFF ( Affective ) subgroup showed GMV reductions in the salience, limbic, and posterior default-mode and control networks, thalamus and cerebellum. GMV reductions in fronto-temporal regions of the salience and control networks were shared across subgroups. Our results highlight the existence of behavioral subgroups with distinct neurobiological and functional profiles in early psychosis, emphasizing the need for refined symptom-based diagnosis and prognosis frameworks., Competing Interests: Conflict of Interest/Financial disclosure Dr Bertolino reports speaker fees from Otsuka, Lundbeck, Angelini and Rovi outside of the submitted work. Dr Hietala reports personal fees from Orion ltd, personal fees from Lundbeck, personal fees from Otsuka and other from Takeda during the conduct of the study. Dr Koutsouleris, Dr Ruhrmann, Dr Riecher-Rossler report grants from European Union over the duration of the study. Dr Meisenzahl and Dr Koutsouleris hold patent US20160192889A1 (‘Adaptive pattern recognition for psychosis risk modelling’). Dr Koutsouleris reports speaker fees from Otsuka, Roche and Angelini outside of the submitted work. Dr Pantelis reports grants from Australian NHMRC during the study, and personal fees from Lundbeck, Australia Pty Ltd outside the submitted work. Dr Upthegrove reports speaker fees from Sunovion, Otsuka and Vitaris outside the submitted work as well as unpaid officership with the British Association for Pharmacology - Honorary General Secretary 2021–2024. She serves as Deputy Editor for The British Journal of Psychiatry. Dr Falkai reports he has received research support/honoraria for lectures or advisory activities from Boehringer-Ingelheim, Janssen, Lundbeck, Otsuka, Recordati and Richter outside the submitted work. Dr Pantelis was supported by an Australian National Health and Medical Research Council (NHMRC) L3 Investigator Grant (1196508) outside the submitted work. Dr Lana Kambeitz-Ilankovic reports receiving a NARSAD Young Investigator Award of the Brain & Behavior Research Foundation No° 28474 (PI: LK-I) outside the submitted work. Dr Milaneschi reports consulting fees from Noema Pharma outside the submitted work. Dr Upthegrove reports support from the UK NIHR Oxford Health Biomedical Research Centre. The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care. All other co-authors did not report any other financial disclosures or other conflicts of interests within or outside of the scope of the submitted work.
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- 2024
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43. Comparing the performance of beamformer algorithms in estimating orientations of neural sources.
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Buschermöhle Y, Höltershinken MB, Erdbrügger T, Radecke JO, Sprenger A, Schneider TR, Lencer R, Gross J, and Wolters CH
- Abstract
The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking. For fixed brain locations, EEG and MEG data from sources with randomized orientations were simulated. The orientation was then estimated (1) with an EEG and (2) with a combined EEG-MEG approach. Three commonly used beamformer algorithms were evaluated with respect to their abilities to estimate the correct orientation: Unit-Gain (UG), Unit-Noise-Gain (UNG), and Array-Gain (AG) beamformer. Performance depends on the signal-to-noise ratios for the modalities and on the chosen beamformer. Overall, the UNG and AG beamformers appear as the most reliable. With increasing noise, the UG estimate converges to a vector determined by the leadfield, thus leading to insufficient orientation estimates., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)
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- 2024
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44. Structural brain abnormalities and aggressive behaviour in schizophrenia: Mega-analysis of data from 2095 patients and 2861 healthy controls via the ENIGMA consortium.
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Lamsma J, Raine A, Kia SM, Cahn W, Arold D, Banaj N, Barone A, Brosch K, Brouwer R, Brunetti A, Calhoun VD, Chew QH, Choi S, Chung YC, Ciccarelli M, Cobia D, Cocozza S, Dannlowski U, Dazzan P, de Bartolomeis A, Di Forti M, Dumais A, Edmond JT, Ehrlich S, Evermann U, Flinkenflügel K, Georgiadis F, Glahn DC, Goltermann J, Green MJ, Grotegerd D, Guerrero-Pedraza A, Ha M, Hong EL, Hulshoff Pol H, Iasevoli F, Kaiser S, Kaleda V, Karuk A, Kim M, Kircher T, Kirschner M, Kochunov P, Kwon JS, Lebedeva I, Lencer R, Marques TR, Meinert S, Murray R, Nenadić I, Nguyen D, Pearlson G, Piras F, Pomarol-Clotet E, Pontillo G, Potvin S, Preda A, Quidé Y, Rodrigue A, Rootes-Murdy K, Salvador R, Skoch A, Sim K, Spalletta G, Spaniel F, Stein F, Thomas-Odenthal F, Tikàsz A, Tomecek D, Tomyshev A, Tranfa M, Tsogt U, Turner JA, van Erp TGM, van Haren NEM, van Os J, Vecchio D, Wang L, Wroblewski A, and Nickl-Jockschat T
- Abstract
Background: Schizophrenia is associated with an increased risk of aggressive behaviour, which may partly be explained by illness-related changes in brain structure. However, previous studies have been limited by group-level analyses, small and selective samples of inpatients and long time lags between exposure and outcome., Methods: This cross-sectional study pooled data from 20 sites participating in the international ENIGMA-Schizophrenia Working Group. Sites acquired T
1 -weighted and diffusion-weighted magnetic resonance imaging scans in a total of 2095 patients with schizophrenia and 2861 healthy controls. Measures of grey matter volume and white matter microstructural integrity were extracted from the scans using harmonised protocols. For each measure, normative modelling was used to calculate how much patients deviated (in z -scores) from healthy controls at the individual level. Ordinal regression models were used to estimate the associations of these deviations with concurrent aggressive behaviour (as odds ratios [ORs] with 99% confidence intervals [CIs]). Mediation analyses were performed for positive symptoms (i.e., delusions, hallucinations and disorganised thinking), impulse control and illness insight. Aggression and potential mediators were assessed with the Positive and Negative Syndrome Scale, Scale for the Assessment of Positive Symptoms or Brief Psychiatric Rating Scale., Results: Aggressive behaviour was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = .003) and global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10-5 ) and additional reductions in dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p =.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10-7 ) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10-4 ). Except for inferior parietal lobule volume, these associations were largely mediated by increased severity of positive symptoms and reduced impulse control., Conclusions: This study provides evidence that the co-occurrence of positive symptoms, poor impulse control and aggressive behaviour in schizophrenia has a neurobiological basis, which may inform the development of therapeutic interventions.- Published
- 2024
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45. Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.
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Şahin D, Kambeitz-Ilankovic L, Wood S, Dwyer D, Upthegrove R, Salokangas R, Borgwardt S, Brambilla P, Meisenzahl E, Ruhrmann S, Schultze-Lutter F, Lencer R, Bertolino A, Pantelis C, Koutsouleris N, and Kambeitz J
- Subjects
- Humans, Psychotic Disorders therapy, Psychiatry
- Abstract
Background: Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce., Aims: To evaluate fairness in prediction models for development of psychosis and functional outcome., Method: Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis ( n = 11) and functional outcome ( n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements., Results: Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions., Conclusions: Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.
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- 2024
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46. Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness.
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Wenzel J, Badde L, Haas SS, Bonivento C, Van Rheenen TE, Antonucci LA, Ruef A, Penzel N, Rosen M, Lichtenstein T, Lalousis PA, Paolini M, Stainton A, Dannlowski U, Romer G, Brambilla P, Wood SJ, Upthegrove R, Borgwardt S, Meisenzahl E, Salokangas RKR, Pantelis C, Lencer R, Bertolino A, Kambeitz J, Koutsouleris N, Dwyer DB, and Kambeitz-Ilankovic L
- Subjects
- Female, Humans, Brain diagnostic imaging, Executive Function, Gray Matter diagnostic imaging, Male, Multicenter Studies as Topic, Cognitive Dysfunction diagnosis, Psychotic Disorders complications, Psychotic Disorders diagnosis
- Abstract
Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (N
ROP = 79, NROD = 30, NCHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (NROP = 61, NROD = 100, NCHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. CLINICAL TRIAL REGISTRY NAME: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042., (© 2023. The Author(s).)- Published
- 2024
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47. A multivariate cognitive approach to predict social functioning in recent onset psychosis in response to computerized cognitive training.
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Walter N, Wenzel J, Haas SS, Squarcina L, Bonivento C, Ruef A, Dwyer D, Lichtenstein T, Bastrük Ö, Stainton A, Antonucci LA, Brambilla P, Wood SJ, Upthegrove R, Borgwardt S, Lencer R, Meisenzahl E, Salokangas RKR, Pantelis C, Bertolino A, Koutsouleris N, Kambeitz J, and Kambeitz-Ilankovic L
- Abstract
Clinical and neuroimaging data has been increasingly used in recent years to disentangle heterogeneity of treatment response to cognitive training (CT) and predict which individuals may achieve the highest benefits. CT has small to medium effects on improving cognitive and social functioning in recent onset psychosis (ROP) patients, who show the most profound cognitive and social functioning deficits among psychiatric patients. We employed multivariate pattern analysis (MVPA) to investigate the potential of cognitive data to predict social functioning improvement in response to 10 h of CT in patients with ROP. A support vector machine (SVM) classifier was trained on the naturalistic data of the Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study sample to predict functioning in an independent sample of 70 ROP patients using baseline cognitive data. PRONIA is a part of a FP7 EU grant program that involved 7 sites across 5 European countries, designed and conducted with the main aim of identifying (bio)markers associated with an enhanced risk of developing psychosis in order to improve early detection and prognosis. Social functioning was predicted with a balanced accuracy (BAC) of 66.4% (Sensitivity 78.8%; Specificity 54.1%; PPV 60.5%; NPV 74.1%; AUC 0.64; P = 0.01). The most frequently selected cognitive features (mean feature weights > ± 0.2) included the (1) correct number of symbol matchings within the Digit Symbol Substitution Test, (2) the number of distracting stimuli leading to an error within 300 and 200 trials in the Continuous Performance Test and (3) the dynamics of verbal fluency between 15 and 30 s within the Verbal Fluency Test, phonetic part. Next, the SVM classifier generated on the PRONIA sample was applied to the intervention sample, that obtained 54 ROP patients who were randomly assigned to a social cognitive training (SCT) or treatment as usual (TAU) group and dichotomized into good (GF-S ≥ 7) and poor (GF-S < 7) functioning patients based on their level of Global Functioning-Social (GF-S) score at follow-up (FU). By applying the initial PRONIA classifier, using out-of-sample cross-validation (OOCV) to the sample of ROP patients who have undergone the CT intervention, a BAC of 59.3% (Sensitivity 70.4%; Specificity 48.1%; PPV 57.6%; NPV 61.9%; AUC 0.63) was achieved at T0 and a BAC of 64.8% (Sensitivity 66.7%; Specificity 63.0%; PPV 64.3%; NPV 65.4%; AUC 0.66) at FU. After SCT intervention, a significant improvement in predicted social functioning values was observed in the SCT compared to TAU group (P ≤0.05; ES[Cohens' d] = 0.18). Due to a small sample size and modest variance of social functioning of the intervention sample it was not feasible to predict individual response to SCT in the current study. Our findings suggest that the use of baseline cognitive data could provide a robust individual estimate of future social functioning, while prediction of individual response to SCT using cognitive data that can be generated in the routine patient care remains to be addressed in large-scale cognitive training trials., Competing Interests: Declaration of Competing Interest This study was supported by EU-FP7 project PRONIA (Personalized Prognostic Tools for Early Psychosis Management) under the Grant Agreement No° 602152 (PI: NK), NARSAD Young Investigator Award of the Brain & Behavior Research Foundation No° 28474 (PI: LK-I) and LMU excellent (LKI). NK, JK and RKRA are currently honorary speakers for Otsuka/Lundbeck. RU received grants from Medical Research Council, grants from the National Institute for Health Research, and personal fees from Sunovion. C Pantelis was supported by a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (1105825), an aNHMRC L3 Investigator Grant (1196508), and NHMRC-EU grant (1075379). SSH is supported by NIH National Institute of Mental Health, grant T32MH122394. The remaining authors including members of the PRONIA consortium have nothing to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)
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- 2024
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48. Correction: Obesity and brain structure in schizophrenia - ENIGMA study in 3021 individuals.
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McWhinney SR, Brosch K, Calhoun VD, Crespo-Facorro B, Crossley NA, Dannlowski U, Dickie E, Dietze LMF, Donohoe G, Du Plessis S, Ehrlich S, Emsley R, Furstova P, Glahn DC, Gonzalez-Valderrama A, Grotegerd D, Holleran L, Kircher TTJ, Knytl P, Kolenic M, Lencer R, Nenadić I, Opel N, Pfarr JK, Rodrigue AL, Rootes-Murdy K, Ross AJ, Sim K, Škoch A, Spaniel F, Stein F, Švancer P, Tordesillas-Gutiérrez D, Undurraga J, Vázquez-Bourgon J, Voineskos A, Walton E, Weickert TW, Weickert CS, Thompson PM, van Erp TGM, Turner JA, and Hajek T
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- 2024
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49. [Recovery-oriented treatment and peer support in psychiatry].
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Lange C, Plock S, Rudloff B, and Lencer R
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- Humans, Counseling, Psychotherapy, Intention, Mental Disorders therapy, Psychiatry
- Abstract
The concept of recovery in the care of mentally ill individuals is now firmly established both nationally and internationally. While clinical recovery focuses on a measurable ultimate goal based on the expression of symptoms with the intention of returning individuals to a premorbid state, personal recovery implies a process of personal development. The three key pillars are salutogenesis, resilience and empowerment. Collaborating with peer support workers is essential for the authentic expansion of therapy offers in line with the principles of recovery. These individuals have their own experiences with psychiatric care, which they utilize to support individuals in their unique recovery journey. The implementation process of recovery-oriented services presents a range of challenges and requires openness and a reorientation on the part of professional treatment teams., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)
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- 2024
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50. Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect.
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Radecke JO, Sprenger A, Stöckler H, Espeter L, Reichhardt MJ, Thomann LS, Erdbrügger T, Buschermöhle Y, Borgwardt S, Schneider TR, Gross J, Wolters CH, and Lencer R
- Subjects
- Humans, Pursuit, Smooth, Frontal Lobe, Magnetic Resonance Imaging methods, Transcranial Direct Current Stimulation methods
- Abstract
The neural networks subserving smooth pursuit eye movements (SPEM) provide an ideal model for investigating the interaction of sensory processing and motor control during ongoing movements. To better understand core plasticity aspects of sensorimotor processing for SPEM, normative sham, anodal or cathodal transcranial direct current stimulation (tDCS) was applied over visual area V5 and frontal eye fields (FEF) in sixty healthy participants. The identical within-subject paradigm was used to assess SPEM modulations by practice. While no specific tDCS effects were revealed, within- and between-session practice effects indicate plasticity of top-down extraretinal mechanisms that mainly affect SPEM in the absence of visual input and during SPEM initiation. To explore the potential of tDCS effects, individual electric field simulations were computed based on calibrated finite element head models and individual functional localization of V5 and FEF location (using functional MRI) and orientation (using combined EEG/MEG) was conducted. Simulations revealed only limited electric field target intensities induced by the applied normative tDCS montages but indicate the potential efficacy of personalized tDCS for the modulation of SPEM. In sum, results indicate the potential susceptibility of extraretinal SPEM control to targeted external neuromodulation (e.g., personalized tDCS) and intrinsic learning protocols., (© 2023. The Author(s).)
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- 2023
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