MARCELO CAMARGO BATISTUZZO
Projetos de Pesquisa
Unidades Organizacionais
Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
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- Toward a neurocircuit-based taxonomy to guide treatment of obsessive-compulsive disorder(2021) SHEPHARD, Elizabeth; STERN, Emily R.; HEUVEL, Odile A. van den; COSTA, Daniel L. C.; BATISTUZZO, Marcelo C.; GODOY, Priscilla B. G.; LOPES, Antonio C.; BRUNONI, Andre R.; HOEXTER, Marcelo Q.; SHAVITT, Roseli G.; REDDY, Y. C. Janardhan; LOCHNER, Christine; STEIN, Dan J.; SIMPSON, H. Blair; MIGUEL, Euripedes C.An important challenge in mental health research is to translate findings from cognitive neuroscience and neuroimaging research into effective treatments that target the neurobiological alterations involved in psychiatric symptoms. To address this challenge, in this review we propose a heuristic neurocircuit-based taxonomy to guide the treatment of obsessive-compulsive disorder (OCD). We do this by integrating information from several sources. First, we provide case vignettes in which patients with OCD describe their symptoms and discuss different clinical profiles in the phenotypic expression of the condition. Second, we link variations in these clinical profiles to underlying neurocircuit dysfunctions, drawing on findings from neuropsychological and neuroimaging studies in OCD. Third, we consider behavioral, pharmacological, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions. Finally, we suggest methods of testing this neurocircuit-based taxonomy as well as important limitations to this approach that should be considered in future research.
- Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium(2020) YUN, Je-Yeon; BOEDHOE, Premika S. W.; VRIEND, Chris; JAHANSHAD, Neda; ABE, Yoshinari; AMEIS, Stephanie H.; ANTICEVIC, Alan; ARNOLD, Paul D.; BATISTUZZO, Marcelo C.; BENEDETTI, Francesco; BEUCKE, Jan C.; BOLLETTINI, Irene; BOSE, Anushree; BREM, Silvia; CALVO, Anna; CHENG, Yuqi; CHO, Kang Ik K.; CIULLO, Valentina; DALLASPEZIA, Sara; DENYS, Damiaan; FEUSNER, Jamie D.; FOUCHE, Jean-Paul; GIMENEZ, Monica; GRUNER, Patricia; HIBAR, Derrek P.; HOEXTER, Marcelo Q.; HU, Hao; HUYSER, Chaim; IKARI, Keisuke; KATHMANN, Norbert; KAUFMANN, Christian; KOCH, Kathrin; LAZARO, Luisa; LOCHNER, Christine; MARQUES, Paulo; MARSH, Rachel; MARTINEZ-ZALACAIN, Ignacio; MATAIX-COLS, David; MENCHON, Jose M.; MINUZZI, Luciano; MORGADO, Pedro; MOREIRA, Pedro; NAKAMAE, Takashi; NAKAO, Tomohiro; NARAYANASWAMY, Janardhanan C.; NURMI, Erika L.; O'NEILL, Joseph; PIACENTINI, John; PIRAS, Fabrizio; PIRAS, Federica; REDDY, Y. C. Janardhan; SATO, Joao R.; SIMPSON, H. Blair; SORENI, Noam; SORIANO-MAS, Carles; SPALLETTA, Gianfranco; STEVENS, Michael C.; SZESZKO, Philip R.; TOLIN, David F.; VENKATASUBRAMANIAN, Ganesan; WALITZA, Susanne; WANG, Zhen; WINGEN, Guido A. van; XU, Jian; XU, Xiufeng; ZHAO, Qing; THOMPSON, Paul M.; STEIN, Dan J.; HEUVEL, Odile A. van den; KWON, Jun SooBrain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect commontrajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsivedisorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scansacquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCDWorking Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to thesimilarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Globalnetworks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency),and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networkswas undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measureswere integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the networkdensity range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering(P<0.0001), lower modularity (P<0.0001), and lower small-worldness (P = 0.017). Detection of community membershipemphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed)centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicativeof altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated withOCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of thisstudy, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariancenetworks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometryin OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularlyin cingulate and orbitofrontal regions.
- Toward identifying reproducible brain signatures of obsessive-compulsive profiles: rationale and methods for a new global initiative(2020) SIMPSON, Helen Blair; HEUVEL, Odile A. van den; MIGUEL, Euripedes C.; REDDY, Y. C. Janardhan; STEIN, Dan J.; LEWIS-FERNANDEZ, Roberto; SHAVITT, Roseli Gedanke; LOCHNER, Christine; POUWELS, Petra J. W.; NARAYANAWAMY, Janardhanan C.; VENKATASUBRAMANIAN, Ganesan; HEZEL, Dianne M.; VRIEND, Chris; BATISTUZZO, Marcelo C.; HOEXTER, Marcelo Q.; JOODE, Niels T. de; COSTA, Daniel Lucas; MATHIS, Maria Alice de; SHESHACHALA, Karthik; NARAYAN, Madhuri; BALKOM, Anton J. L. M. van; BATELAAN, Neeltje M.; VENKATARAM, Shivakumar; CHERIAN, Anish; MARINCOWITZ, Clara; PANNEKOEK, Nienke; STOVEZKY, Yael R.; MARE, Karen; LIU, Feng; OTADUY, Maria Concepcion Garcia; PASTORELLO, Bruno; RAO, Rashmi; KATECHIS, Martha; METER, Page Van; WALL, MelanieBackground Obsessive-compulsive disorder (OCD) has a lifetime prevalence of 2-3% and is a leading cause of global disability. Brain circuit abnormalities in individuals with OCD have been identified, but important knowledge gaps remain. The goal of the new global initiative described in this paper is to identify robust and reproducible brain signatures of measurable behaviors and clinical symptoms that are common in individuals with OCD. A global approach was chosen to accelerate discovery, to increase rigor and transparency, and to ensure generalizability of results. Methods We will study 250 medication-free adults with OCD, 100 unaffected adult siblings of individuals with OCD, and 250 healthy control subjects at five expert research sites across five countries (Brazil, India, Netherlands, South Africa, and the U.S.). All participants will receive clinical evaluation, neurocognitive assessment, and magnetic resonance imaging (MRI). The imaging will examine multiple brain circuits hypothesized to underlie OCD behaviors, focusing on morphometry (T1-weighted MRI), structural connectivity (Diffusion Tensor Imaging), and functional connectivity (resting-state fMRI). In addition to analyzing each imaging modality separately, we will also use multi-modal fusion with machine learning statistical methods in an attempt to derive imaging signatures that distinguish individuals with OCD from unaffected siblings and healthy controls (Aim #1). Then we will examine how these imaging signatures link to behavioral performance on neurocognitive tasks that probe these same circuits as well as to clinical profiles (Aim #2). Finally, we will explore how specific environmental features (childhood trauma, socioeconomic status, and religiosity) moderate these brain-behavior associations. Discussion Using harmonized methods for data collection and analysis, we will conduct the largest neurocognitive and multimodal-imaging study in medication-free subjects with OCD to date. By recruiting a large, ethno-culturally diverse sample, we will test whether there are robust biosignatures of core OCD features that transcend countries and cultures. If so, future studies can use these brain signatures to reveal trans-diagnostic disease dimensions, chart when these signatures arise during development, and identify treatments that target these circuit abnormalities directly. The long-term goal of this research is to change not only how we conceptualize OCD but also how we diagnose and treat it.
- Cross-National Harmonization of Neurocognitive Assessment Across Five Sites in a Global Study(2023) BATISTUZZO, Marcelo C.; SHESHACHALA, Karthik; ALSCHULER, Daniel M.; HEZEL, Dianne M.; LEWIS-FERNANDEZ, Roberto; JOODE, Niels T. de; VRIEND, Chris; LEMPERT, Karolina M.; NARAYAN, Madhuri; MARINCOWITZ, Clara; LOCHNER, Christine; STEIN, Dan J.; NARAYANASWAMY, Janardhanan C.; HEUVEL, Odile A. van den; SIMPSON, Helen Blair; WALL, MelanieObjective: Cross-national work on neurocognitive testing has been characterized by inconsistent findings, suggesting the need for improved harmonization. Here, we describe a prospective harmonization approach in an ongoing global collaborative study. Method: Visuospatial N-Back, Tower of London (ToL), Stop Signal task (SST), Risk Aversion (RA), and Intertemporal Choice (ITC) tasks were administered to 221 individuals from Brazil, India, the Netherlands, South Africa, and the USA. Prospective harmonization methods were employed to ensure procedural similarity of task implementation and processing of derived task measures across sites. Generalized linear models tested for between-site differences controlling for sex, age, education, and socioeconomic status (SES). Associations with these covariates were also examined and tested for differences by site with site-by-covariate interactions. Results: The Netherlands site performed more accurately on N-Back and ToL than the other sites, except for the USA site on the N-Back. The Netherlands and the USA sites performed faster than the other three sites during the go events in the SST. Finally, the Netherlands site also exhibited a higher tolerance for delay discounting than other sites on the ITC, and the India site showed more risk aversion than other sites on the RA task. However, effect size differences across sites on the five tasks were generally small (i.e., partial eta-squared < 0.05) after dropping the Netherlands (on ToL, N-Back, ITC, and SST tasks) and India (on the RA task). Across tasks, regardless of site, the N-Back (sex, age, education, and SES), ToL (sex, age, and SES), SST (age), and ITC (SES) showed associations with covariates. Conclusions: Four out of the five sites showed only small between-site differences for each task. Nevertheless, despite our extensive prospective harmonization steps, task score performance deviated from the other sites in the Netherlands site (on four tasks) and the India site (on one task). Because the procedural methods were standardized across sites, and our analyses were adjusted for covariates, the differences found in cognitive performance may indicate selection sampling bias due to unmeasured confounders. Future studies should follow similar cross-site prospective harmonization procedures when assessing neurocognition and consider measuring other possible confounding variables for additional statistical control.
- The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium(2023) BRUIN, Willem B.; ABE, Yoshinari; ALONSO, Pino; ANTICEVIC, Alan; BACKHAUSEN, Lea L.; BALACHANDER, Srinivas; BARGALLO, Nuria; BATISTUZZO, Marcelo C.; BENEDETTI, Francesco; TRIQUELL, Sara Bertolin; BREM, Silvia; CALESELLA, Federico; COUTO, Beatriz; DENYS, Damiaan A. J. P.; ECHEVARRIA, Marco A. N.; ENG, Goi Khia; FERREIRA, Sonia; FEUSNER, Jamie D.; GRAZIOPLENE, Rachael G.; GRUNER, Patricia; GUO, Joyce Y.; HAGEN, Kristen; HANSEN, Bjarne; HIRANO, Yoshiyuki; HOEXTER, Marcelo Q.; JAHANSHAD, Neda; JASPERS-FAYER, Fern; KASPRZAK, Selina; KIM, Minah; KOCH, Kathrin; KWAK, Yoo Bin; KWON, Jun Soo; LAZARO, Luisa; LI, Chiang-Shan R.; LOCHNER, Christine; MARSH, Rachel; MARTINEZ-ZALACAIN, Ignacio; MENCHON, Jose M.; MOREIRA, Pedro S.; MORGADO, Pedro; NAKAGAWA, Akiko; NAKAO, Tomohiro; NARAYANASWAMY, Janardhanan C.; NURMI, Erika; ZORRILLA, Jose C. Pariente; PIACENTINI, John; PICO-PEREZ, Maria; PIRAS, Fabrizio; PIRAS, Federica; PITTENGER, Christopher; REDDY, Janardhan Y. C.; RODRIGUEZ-MANRIQUE, Daniela; SAKAI, Yuki; SHIMIZU, Eiji; SHIVAKUMAR, Venkataram; SIMPSON, Blair H.; SORIANO-MAS, Carles; SOUSA, Nuno M.; SPALLETTA, Gianfranco; STERN, Emily R.; STEWART, S. Evelyn; SZESZKO, Philip; TANG, Jinsong; I, Sophia Thomopoulos; THORSEN, Anders L.; TOKIKO, Yoshida; TOMIYAMA, Hirofumi; VAI, Benedetta; VEER, Ilya M.; VENKATASUBRAMANIAN, Ganesan; VETTER, Nora C.; VRIEND, Chris; WALITZA, Susanne; WALLER, Lea; WANG, Zhen; WATANABE, Anri; WOLFF, Nicole; YUN, Je-Yeon; ZHAO, Qing; LEEUWEN, Wieke A. van; MARLE, Hein J. F. van; MORTEL, Laurens A. van de; STRATEN, Anouk van der; WERF, Ysbrand D. van der; THOMPSON, Paul M.; STEIN, Dan J.; HEUVEL, Odile A. van den; WINGEN, Guido A. vanCurrent knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.
- Right Prefrontal Cortical Thickness Is Associated With Response to Cognitive-Behavioral Therapy in Children With Obsessive-Compulsive Disorder(2023) BERTOLIN, Sara; ALONSO, Pino; MARTINEZ-ZALACAIN, Ignacio; MENCHON, Jose M.; JIMENEZ-MURCIA, Susana; BAKER, Justin T.; BARGALLO, Nuria; BATISTUZZO, Marcelo Camargo; BOEDHOE, Premika S. W.; BRENNAN, Brian P.; FEUSNER, Jamie D.; FITZGERALD, Kate D.; FONTAINE, Martine; HANSEN, Bjarne; HIRANO, Yoshiyuki; HOEXTER, Marcelo Q.; HUYSER, Chaim; JAHANSHAD, Neda; JASPERS-FAYER, Fern; KUNO, Masaru; KVALE, Gerd; LAZARO, Luisa; MACHADO-SOUSA, Mafalda; MARSH, Rachel; MORGADO, Pedro; NAKAGAWA, Akiko; NORMAN, Luke; NURMI, Erika L.; O'NEILL, Joseph; ORTIZ, Ana E.; PERRIELLO, Chris; PIACENTINI, John; PICO-PEREZ, Maria; SHAVITT, Roseli G.; SHIMIZU, Eiji; SIMPSON, Helen Blair; STEWART, S. Evelyn; THOMOPOULOS, Sophia I.; THORSEN, Anders Lillevik; WALITZA, Susanne; WOLTERS, Lidewij H.; THOMPSON, Paul M.; HEUVEL, Odile A. van den; STEIN, Dan J.; SORIANO-MAS, CarlesObjective: Cognitive-behavioral therapy (CBT) is considered a first-line treatment for obsessive-compulsive disorder (OCD) in pediatric and adult populations. Nevertheless, some patients show partial or null response. The identification of predictors of CBT response may improve clinical management of patients with OCD. Here, we aimed to identify structural magnetic resonance imaging (MRI) predictors of CBT response in 2 large series of children and adults with OCD from the worldwide ENIGMA-OCD consortium. Method: Data from 16 datasets from 13 international sites were included in the study. We assessed which variations in baseline cortical thickness, cortical surface area, and subcortical volume predicted response to CBT (percentage of baseline to post-treatment symptom reduction) in 2 samples totaling 168 children and adolescents (age range 5-17.5 years) and 318 adult patients (age range 18-63 years) with OCD. Mixed linear models with random intercept were used to account for potential cross-site differences in imaging values. Results: Significant results were observed exclusively in the pediatric sample. Right prefrontal cortex thickness was positively associated with the percentage of CBT response. In a post hoc analysis, we observed that the specific changes accounting for this relationship were a higher thickness of the frontal pole and the rostral middle frontal gyrus. We observed no significant effects of age, sex, or medication on our findings. Conclusion: Higher cortical thickness in specific right prefrontal cortex regions may be important for CBT response in children with OCD. Our findings suggest that the right prefrontal cortex plays a relevant role in the mechanisms of action of CBT in children.
- White matter microstructure and its relation to clinical features of obsessive-compulsive disorder: findings from the ENIGMA OCD Working Group(2021) PIRAS, Fabrizio; PIRAS, Federica; ABE, Yoshinari; AGARWAL, Sri Mahavir; ANTICEVIC, Alan; AMEIS, Stephanie; ARNOLD, Paul; BANAJ, Nerisa; BARGALLO, Nuria; BATISTUZZO, Marcelo C.; BENEDETTI, Francesco; BEUCKE, Jan-Carl; BOEDHOE, Premika S. W.; BOLLETTINI, Irene; BREM, Silvia; CALVO, Anna; CHO, Kang Ik Kevin; CIULLO, Valentina; DALLASPEZIA, Sara; DICKIE, Erin; ELY, Benjamin Adam; FAN, Siyan; FOUCHE, Jean-Paul; GRUNER, Patricia; GUERSEL, Deniz A.; HAUSER, Tobias; HIRANO, Yoshiyuki; HOEXTER, Marcelo Q.; IORIO, Mariangela; JAMES, Anthony; REDDY, Y. C. Janardhan; KAUFMANN, Christian; KOCH, Kathrin; KOCHUNOV, Peter; KWON, Jun Soo; LAZARO, Luisa; LOCHNER, Christine; MARSH, Rachel; NAKAGAWA, Akiko; NAKAMAE, Takashi; NARAYANASWAMY, Janardhanan C.; SAKAI, Yuki; SHIMIZU, Eiji; SIMON, Daniela; SIMPSON, Helen Blair; SORENI, Noam; STAEMPFLI, Philipp; STERN, Emily R.; SZESZKO, Philip; TAKAHASHI, Jumpei; VENKATASUBRAMANIAN, Ganesan; WANG, Zhen; YUN, Je-Yeon; STEIN, Dan J.; JAHANSHAD, Neda; THOMPSON, Paul M.; HEUVEL, Odile A. van den; SPALLETTA, Gianfranco; ASSOGNA, Francesca; CALVO, Rosa; WIT, Stella J. de; HOUGH, Morgan; KUNO, Masaru; MIGUEL, Euripedes C.; MORER, Astrid; PITTENGER, Christopher; POLETTI, Sara; SMERALDI, Enrico; SATO, Joao R.; TSUCHIYAGAITO, Aki; WALITZA, Susanne; WERF, Ysbrand D. van der; VECCHIO, Daniela; ZAREI, MojtabaMicrostructural alterations in cortico-subcortical connections are thought to be present in obsessive-compulsive disorder (OCD). However, prior studies have yielded inconsistent findings, perhaps because small sample sizes provided insufficient power to detect subtle abnormalities. Here we investigated microstructural white matter alterations and their relation to clinical features in the largest dataset of adult and pediatric OCD to date. We analyzed diffusion tensor imaging metrics from 700 adult patients and 645 adult controls, as well as 174 pediatric patients and 144 pediatric controls across 19 sites participating in the ENIGMA OCD Working Group, in a cross-sectional case-control magnetic resonance study. We extracted measures of fractional anisotropy (FA) as main outcome, and mean diffusivity, radial diffusivity, and axial diffusivity as secondary outcomes for 25 white matter regions. We meta-analyzed patient-control group differences (Cohen's d) across sites, after adjusting for age and sex, and investigated associations with clinical characteristics. Adult OCD patients showed significant FA reduction in the sagittal stratum (d=-0.21, z=-3.21, p=0.001) and posterior thalamic radiation (d=-0.26, z=-4.57, p<0.0001). In the sagittal stratum, lower FA was associated with a younger age of onset (z=2.71, p=0.006), longer duration of illness (z=-2.086, p=0.036), and a higher percentage of medicated patients in the cohorts studied (z=-1.98, p=0.047). No significant association with symptom severity was found. Pediatric OCD patients did not show any detectable microstructural abnormalities compared to controls. Our findings of microstructural alterations in projection and association fibers to posterior brain regions in OCD are consistent with models emphasizing deficits in connectivity as an important feature of this disorder.
- An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group(2019) BOEDHOE, Premika S. W.; HEYMANS, Martijn W.; SCHMAAL, Lianne; ABE, Yoshinari; ALONSO, Pino; AMEIS, Stephanie H.; ANTICEVIC, Alan; ARNOLD, Paul D.; BATISTUZZO, Marcelo C.; BENEDETTI, Francesco; BEUCKE, Jan C.; BOLLETTINI, Irene; BOSE, Anushree; BREM, Silvia; CALVO, Anna; CALVO, Rosa; CHENG, Yuqi; CHO, Kang lk K.; CIULLO, Valentina; DALLASPEZIA, Sara; DENYS, Damiaan; FEUSNER, Jamie D.; FITZGERALD, Kate D.; FOUCHES, Jean-Paul; FRIDGEIRSSON, Egill A.; GRUNER, Patricia; HENNA, Gregory L.; HIBAR, Derrek P.; HOEXTER, Marcelo Q.; HU, Hao; HUYSER, Chaim; JAHANSHAD, Neda; JAMES, Anthony; KATHMANN, Norbert; KAUFMANN, Christian; KOCH, Kathrin; KWON, Jun Soo; LAZARO, Luisa; LOCHNER, Christine; MARSH, Rachel; MARTINEZ-ZALACAIN, Ignacio; MATAIX-COLS, David; MENCHON, Jose M.; MINUZZI, Luciano; MORER, Astrid; NAKAMAE, Takashi; NAKAO, Tomohiro; NARAYANASWAMY, Janardhanan C.; NISHIDA, Seiji; NURMI, Erika L.; O'NEILL, Joseph; PIACENTINI, John; PIRAS, Fabrizio; PIRAS, Federica; REDDY, Y. C. Janardhan; REESS, Tim J.; SAKAI, Yuki; SATO, Joao P.; SIMPSON, H. Blair; SORENI, Noam; SORIANO-MAS, Caries; SPALLETTA, Gianfranco; STEVENS, Michael C.; SZESZKOS, Philip P.; TOLIN, David F.; WINGEN, Guido A. van; VENKATASUBRAMANIAN, Ganesan; WALITZA, Susanne; WANG, Zhen; YUN, Je-Yeon; THOMPSON, Paul M.; STEIN, Dan J.; HEUVEL, Odile A. van den; TWISK, Jos W. R.Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.
conferenceObject Influences of Socioeconomic Status and Education on Intelligence Across 5 Multinational Sites(2022) DESERISY, Mariah; GOLDBERG, Terry; BATISTUZZO, Marcelo; KEYES, Katherine; JOODE, Niels de; LOCHNER, Christine; MARINCOWITZ, Clara; NARAYAN, Madhuri; ANAND, Nitin; RAPP, Amy; STEIN, Dan J.; WALL, Melanie; SIMPSON, H. Blair; MARGOLIS, Amy E.- Expanding the heuristic neurocircuit-based taxonomy to guide treatment for OCD: reply to the commentary ""Probing the genetic and molecular correlates of connectome alterations in obsessive-compulsive disorder""(2022) SHEPHARD, Elizabeth; STERN, Emily R.; HEUVEL, Odile A. van den; COSTA, Daniel L. C.; BATISTUZZO, Marcelo C.; GODOY, Priscilla B. G.; LOPES, Antonio C.; BRUNONI, Andre R.; HOEXTER, Marcelo Q.; SHAVITT, Roseli G.; REDDY, Y. C. Janardhan; LOCHNER, Christine; STEIN, Dan J.; SIMPSON, H. Blair; MIGUEL, Euripedes C.