18 results on '"Kuplicki, Rayus T."'
Search Results
2. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals
- Author
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McWhinney, Sean R, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J, Cannon, Dara M, Dannlowski, Udo, Díaz-Zuluaga, Ana M, Elvsåshagen, Torbjørn, Eyler, Lisa T, Fullerton, Janice M, Goikolea, Jose M, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus CM, Hahn, Tim, Howells, Fleur M, Ingvar, Martin, Kircher, Tilo TJ, Krug, Axel, Kuplicki, Rayus T, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F, Martyn, Fiona M, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A, Overs, Bronwyn J, Pfarr, Julia-Katharina, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G, Roberts, Gloria, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R, Sim, Kang, Stein, Dan J, Stein, Frederike, Temmingh, Henk S, Thiel, Katharina, van Haren, Neeltje EM, Gestel, Holly Van, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N, Ching, Christopher RK, Andreassen, Ole, Thompson, Paul M, and Hajek, Tomas
- Subjects
Biological Psychology ,Biomedical and Clinical Sciences ,Psychology ,Mental Health ,Biomedical Imaging ,Nutrition ,Brain Disorders ,Obesity ,Neurosciences ,Clinical Research ,Prevention ,Mental health ,Amygdala ,Bipolar Disorder ,Body Mass Index ,Brain ,Humans ,Magnetic Resonance Imaging ,ENIGMA Bipolar Disorders Working Group ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry ,Clinical sciences ,Biological psychology ,Clinical and health psychology - Abstract
Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.
- Published
- 2021
3. Impact of serotonergic medication on interoception in major depressive disorder
- Author
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Bodurka, Jerzy, Guinjoan, Salvador, Savitz, Jonathan, Victor, Teresa A., Burrows, Kaiping, DeVille, Danielle C., Cosgrove, Kelly T., Kuplicki, Rayus T., Paulus, Martin P., Aupperle, Robin, Khalsa, Sahib S., and Stewart, Jennifer L.
- Published
- 2022
- Full Text
- View/download PDF
4. A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE
- Author
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Le, Trang T, Kuplicki, Rayus T, McKinney, Brett A, Yeh, Hung-Wen, Thompson, Wesley K, Paulus, Martin P, Investigators, Tulsa 1000, Aupperle, Robin L, Bodurka, Jerzy, Cha, Yoon-Hee, Feinstein, Justin S, Khalsa, Sahib S, Savitz, Jonathan, Simmons, W Kyle, and Victor, Teresa A
- Subjects
Biological Psychology ,Psychology ,Aging ,Biomedical Imaging ,Clinical Research ,Behavioral and Social Science ,Neurological ,Mental health ,BrainAGE ,simulation ,false positives ,SVR ,MRI ,aging ,Tulsa 1000 Investigators ,Biochemistry and Cell Biology ,Neurosciences ,Cognitive Sciences ,Biological psychology - Abstract
Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.
- Published
- 2018
5. Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity
- Author
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Sangchooli, Arshiya, Zare-Bidoky, Mehran, Jouzdani, Ali Fathi, Schacht, Joseph, Bjork, James M., Claus, Eric D., Prisciandaro, James J., Wilson, Stephen J., Wuestenberg, Torsten, Potvin, Stephane, Ahmadi, Pooria, Bach, Patrick, Baldacchino, Alex, Beck, Anne, Brady, Kathleen T., Brewer, Judson A., Childress, Anna Rose, Courtney, Kelly E., Ebrahimi, Mohsen, Filbey, Francesca M., Garavan, Hugh, Ghahremani, Dara G., Goldstein, Rita Z., Goudriaan, Anneke E., Grodin, Erica N., Hanlon, Colleen A., Haugg, Amelie, Heilig, Markus, Heinz, Andreas, Holczer, Adrienn, Van Holst, Ruth J., Joseph, Jane E., Juliano, Anthony C., Kaufman, Marc J., Kiefer, Falk, Zonoozi, Arash Khojasteh, Kuplicki, Rayus T., Leyton, Marco, London, Edythe D., Mackey, Scott, McClernon, F. Joseph, Mellick, William H., Morley, Kirsten, Noori, Hamid R., Oghabian, Mohammad Ali, Oliver, Jason A., Owens, Max, Paulus, Martin P., Perini, Irene, Rafei, Parnian, Ray, Lara A., Sinha, Rajita, Smolka, Michael N., Soleimani, Ghazaleh, Spanagel, Rainer, Steele, Vaughn R., Tapert, Susan F., Vollstaedt-Klein, Sabine, Wetherill, Reagan R., Witkiewitz, Katie, Yuan, Kai, Zhang, Xiaochu, Verdejo-Garcia, Antonio, Potenza, Marc N., Janes, Amy C., Kober, Hedy, Zilverstand, Anna, Ekhtiari, Hamed, Sangchooli, Arshiya, Zare-Bidoky, Mehran, Jouzdani, Ali Fathi, Schacht, Joseph, Bjork, James M., Claus, Eric D., Prisciandaro, James J., Wilson, Stephen J., Wuestenberg, Torsten, Potvin, Stephane, Ahmadi, Pooria, Bach, Patrick, Baldacchino, Alex, Beck, Anne, Brady, Kathleen T., Brewer, Judson A., Childress, Anna Rose, Courtney, Kelly E., Ebrahimi, Mohsen, Filbey, Francesca M., Garavan, Hugh, Ghahremani, Dara G., Goldstein, Rita Z., Goudriaan, Anneke E., Grodin, Erica N., Hanlon, Colleen A., Haugg, Amelie, Heilig, Markus, Heinz, Andreas, Holczer, Adrienn, Van Holst, Ruth J., Joseph, Jane E., Juliano, Anthony C., Kaufman, Marc J., Kiefer, Falk, Zonoozi, Arash Khojasteh, Kuplicki, Rayus T., Leyton, Marco, London, Edythe D., Mackey, Scott, McClernon, F. Joseph, Mellick, William H., Morley, Kirsten, Noori, Hamid R., Oghabian, Mohammad Ali, Oliver, Jason A., Owens, Max, Paulus, Martin P., Perini, Irene, Rafei, Parnian, Ray, Lara A., Sinha, Rajita, Smolka, Michael N., Soleimani, Ghazaleh, Spanagel, Rainer, Steele, Vaughn R., Tapert, Susan F., Vollstaedt-Klein, Sabine, Wetherill, Reagan R., Witkiewitz, Katie, Yuan, Kai, Zhang, Xiaochu, Verdejo-Garcia, Antonio, Potenza, Marc N., Janes, Amy C., Kober, Hedy, Zilverstand, Anna, and Ekhtiari, Hamed
- Abstract
Importance In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence Review The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25, Funding Agencies|Deutsche Forschungsgemeinschaft
- Published
- 2024
- Full Text
- View/download PDF
6. Mega-analysis of association between obesity and cortical morphology in bipolar disorders:ENIGMA study in 2832 participants
- Author
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Mcwhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Boen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Diaz-Zuluaga, Ana M., Dietze, Lorielle M.F., Elvsåshagen, Torbjorn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Jahanshad, Neda, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G., Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R., Sim, Kang, Stein, Dan J., Stein, Frederike, Temmingh, Henk S., Thiel, Katharina, Thomopoulos, Sophia I., Van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N., Ching, Christopher R.K., Andreassen, Ole A., Thompson, Paul M., Hajek, Tomas, Mcwhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Boen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Diaz-Zuluaga, Ana M., Dietze, Lorielle M.F., Elvsåshagen, Torbjorn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Jahanshad, Neda, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G., Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R., Sim, Kang, Stein, Dan J., Stein, Frederike, Temmingh, Henk S., Thiel, Katharina, Thomopoulos, Sophia I., Van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N., Ching, Christopher R.K., Andreassen, Ole A., Thompson, Paul M., and Hajek, Tomas
- Abstract
Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.
- Published
- 2023
7. Rapid, reliable mobile assessment of affect-related motor processing
- Author
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Howlett, Jonathon R., primary, Larkin, Florence, additional, Touthang, James, additional, Kuplicki, Rayus T., additional, Lim, Kelvin O., additional, and Paulus, Martin P., additional
- Published
- 2022
- Full Text
- View/download PDF
8. Characterizing and Coding Psychiatric Diagnoses Using Electronic Health Record Data
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Kuplicki, Rayus T., primary and Thompson, Wesley K., additional
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- 2022
- Full Text
- View/download PDF
9. Corrigendum to “Impact of serotonergic medication on interoception in major depressive disorder” [Biological Psychology 169 (2022),108286]
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Burrows, Kaiping, primary, DeVille, Danielle C., additional, Cosgrove, Kelly T., additional, Kuplicki, Rayus T., additional, Paulus, Martin P., additional, Aupperle, Robin, additional, Khalsa, Sahib S., additional, and Stewart, Jennifer L., additional
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- 2022
- Full Text
- View/download PDF
10. Association of Generalized Anxiety Disorder With Autonomic Hypersensitivity and Blunted Ventromedial Prefrontal Cortex Activity During Peripheral Adrenergic Stimulation
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Teed, Adam R., primary, Feinstein, Justin S., additional, Puhl, Maria, additional, Lapidus, Rachel C., additional, Upshaw, Valerie, additional, Kuplicki, Rayus T., additional, Bodurka, Jerzy, additional, Ajijola, Olujimi A., additional, Kaye, Walter H., additional, Thompson, Wesley K., additional, Paulus, Martin P., additional, and Khalsa, Sahib S., additional
- Published
- 2022
- Full Text
- View/download PDF
11. Impact of serotonergic medication on interoception in major depressive disorder
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Burrows, Kaiping, primary, DeVille, Danielle C., additional, Cosgrove, Kelly T., additional, Kuplicki, Rayus T., additional, Paulus, Martin P., additional, Aupperle, Robin, additional, Khalsa, Sahib S., additional, Stewart, Jennifer L., additional, Bodurka, Jerzy, additional, Guinjoan, Salvador, additional, Savitz, Jonathan, additional, and Victor, Teresa A., additional
- Published
- 2022
- Full Text
- View/download PDF
12. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals
- Author
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McWhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Elvsåshagen, Torbjørn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, van Haren, Neeltje E.M., Vreeker, Annabel, Thompson, Paul M, Hajek, Tomas, McWhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Elvsåshagen, Torbjørn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, van Haren, Neeltje E.M., Vreeker, Annabel, Thompson, Paul M, and Hajek, Tomas
- Abstract
Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.
- Published
- 2021
13. Predicting Age From Brain EEG Signals—A Machine Learning Approach
- Author
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Al Zoubi, Obada, primary, Ki Wong, Chung, additional, Kuplicki, Rayus T., additional, Yeh, Hung-wen, additional, Mayeli, Ahmad, additional, Refai, Hazem, additional, Paulus, Martin, additional, and Bodurka, Jerzy, additional
- Published
- 2018
- Full Text
- View/download PDF
14. A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE.
- Author
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Le, Trang T., Kuplicki, Rayus T., McKinney, Brett A., Yeh, Hung-Wen, Thompson, Wesley K., Paulus, Martin P., Aupperle, Robin L, Bodurka, Jerzy, Cha, Yoon-Hee, Feinstein, Justin S., Khalsa, Sahib S., Savitz, Jonathan, Simmons, W Kyle, and Victor, Teresa A
- Abstract
Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18–60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18–56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
15. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals
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Sean R, McWhinney, Christoph, Abé, Martin, Alda, Francesco, Benedetti, Erlend, Bøen, Caterina, Del Mar Bonnin, Tiana, Borgers, Katharina, Brosch, Erick J, Canales-Rodríguez, Dara M, Cannon, Udo, Dannlowski, Ana M, Diaz-Zuluaga, Lorielle, Dietze, Torbjørn, Elvsåshagen, Lisa T, Eyler, Janice M, Fullerton, Jose M, Goikolea, Janik, Goltermann, Dominik, Grotegerd, Bartholomeus C M, Haarman, Tim, Hahn, Fleur M, Howells, Martin, Ingvar, Tilo T J, Kircher, Axel, Krug, Rayus T, Kuplicki, Mikael, Landén, Hannah, Lemke, Benny, Liberg, Carlos, Lopez-Jaramillo, Ulrik F, Malt, Fiona M, Martyn, Elena, Mazza, Colm, McDonald, Genevieve, McPhilemy, Sandra, Meier, Susanne, Meinert, Tina, Meller, Elisa M T, Melloni, Philip B, Mitchell, Leila, Nabulsi, Igor, Nenadic, Nils, Opel, Roel A, Ophoff, Bronwyn J, Overs, Julia-Katharina, Pfarr, Julian A, Pineda-Zapata, Edith, Pomarol-Clotet, Joaquim, Raduà, Jonathan, Repple, Maike, Richter, Kai G, Ringwald, Gloria, Roberts, Alex, Ross, Raymond, Salvador, Jonathan, Savitz, Simon, Schmitt, Peter R, Schofield, Kang, Sim, Dan J, Stein, Frederike, Stein, Henk S, Temmingh, Katharina, Thiel, Sophia I, Thomopoulos, Neeltje E M, van Haren, Holly, Van Gestel, Cristian, Vargas, Eduard, Vieta, Annabel, Vreeker, Lena, Waltemate, Lakshmi N, Yatham, Christopher R K, Ching, Ole A, Andreassen, Paul M, Thompson, Tomas, Hajek, Mcwhinney, Sean R, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J, Cannon, Dara M, Dannlowski, Udo, Diaz-Zuluaga, Ana M, Dietze, Lorielle, Elvsåshagen, Torbjørn, Eyler, Lisa T, Fullerton, Janice M, Goikolea, Jose M, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C M, Hahn, Tim, Howells, Fleur M, Ingvar, Martin, Kircher, Tilo T J, Krug, Axel, Kuplicki, Rayus T, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlo, Malt, Ulrik F, Martyn, Fiona M, Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M T, Mitchell, Philip B, Nabulsi, Leila, Nenadic, Igor, Opel, Nil, Ophoff, Roel A, Overs, Bronwyn J, Pfarr, Julia-Katharina, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R, Sim, Kang, Stein, Dan J, Stein, Frederike, Temmingh, Henk S, Thiel, Katharina, Thomopoulos, Sophia I, van Haren, Neeltje E M, Van Gestel, Holly, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N, Ching, Christopher R K, Andreassen, Ole, Thompson, Paul M, Hajek, Tomas, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Psychiatry, Child and Adolescent Psychiatry / Psychology, and Clinical Child and Family Studies
- Subjects
obesity ,Bipolar Disorder ,body mass index ,03 medical and health sciences ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,SCHIZOPHRENIA ,Cluster Analysis ,Humans ,BRAIN ,Biological Psychiatry ,Body mass index ,GRAY-MATTER VOLUME ,METABOLIC SYNDROME ,2. Zero hunger ,MAJOR DEPRESSIVE DISORDER ,INSULIN-RESISTANCE ,ABNORMALITIES ,1ST-EPISODE ,heterogeneit ,surface area ,cortical thickness ,Magnetic Resonance Imaging ,Temporal Lobe ,3. Good health ,030227 psychiatry ,Psychiatry and Mental health ,bipolar disorders ,WHITE ,heterogeneity ,030217 neurology & neurosurgery - Abstract
Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.
- Published
- 2022
16. Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review.
- Author
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Sangchooli A, Zare-Bidoky M, Fathi Jouzdani A, Schacht J, Bjork JM, Claus ED, Prisciandaro JJ, Wilson SJ, Wüstenberg T, Potvin S, Ahmadi P, Bach P, Baldacchino A, Beck A, Brady KT, Brewer JA, Childress AR, Courtney KE, Ebrahimi M, Filbey FM, Garavan H, Ghahremani DG, Goldstein RZ, Goudriaan AE, Grodin EN, Hanlon CA, Haugg A, Heilig M, Heinz A, Holczer A, Van Holst RJ, Joseph JE, Juliano AC, Kaufman MJ, Kiefer F, Khojasteh Zonoozi A, Kuplicki RT, Leyton M, London ED, Mackey S, McClernon FJ, Mellick WH, Morley K, Noori HR, Oghabian MA, Oliver JA, Owens M, Paulus MP, Perini I, Rafei P, Ray LA, Sinha R, Smolka MN, Soleimani G, Spanagel R, Steele VR, Tapert SF, Vollstädt-Klein S, Wetherill RR, Witkiewitz K, Yuan K, Zhang X, Verdejo-Garcia A, Potenza MN, Janes AC, Kober H, Zilverstand A, and Ekhtiari H
- Subjects
- Humans, Brain diagnostic imaging, Brain physiopathology, Brain metabolism, Functional Neuroimaging, Cues, Magnetic Resonance Imaging, Substance-Related Disorders physiopathology, Substance-Related Disorders diagnostic imaging, Biomarkers
- Abstract
Importance: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers., Objective: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts., Evidence Review: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders., Findings: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes., Conclusions and Relevance: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.
- Published
- 2024
- Full Text
- View/download PDF
17. Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.
- Author
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McWhinney SR, Abé C, Alda M, Benedetti F, Bøen E, Del Mar Bonnin C, Borgers T, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Diaz-Zuluaga AM, Dietze LMF, Elvsåshagen T, Eyler LT, Fullerton JM, Goikolea JM, Goltermann J, Grotegerd D, Haarman BCM, Hahn T, Howells FM, Ingvar M, Jahanshad N, Kircher TTJ, Krug A, Kuplicki RT, Landén M, Lemke H, Liberg B, Lopez-Jaramillo C, Malt UF, Martyn FM, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadic I, Opel N, Ophoff RA, Overs BJ, Pfarr JK, Pineda-Zapata JA, Pomarol-Clotet E, Raduà J, Repple J, Richter M, Ringwald KG, Roberts G, Ross A, Salvador R, Savitz J, Schmitt S, Schofield PR, Sim K, Stein DJ, Stein F, Temmingh HS, Thiel K, Thomopoulos SI, van Haren NEM, Vargas C, Vieta E, Vreeker A, Waltemate L, Yatham LN, Ching CRK, Andreassen OA, Thompson PM, and Hajek T
- Abstract
Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact., Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations., Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI., Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.
- Published
- 2023
- Full Text
- View/download PDF
18. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.
- Author
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McWhinney SR, Abé C, Alda M, Benedetti F, Bøen E, Del Mar Bonnin C, Borgers T, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Diaz-Zuluaga AM, Dietze L, Elvsåshagen T, Eyler LT, Fullerton JM, Goikolea JM, Goltermann J, Grotegerd D, Haarman BCM, Hahn T, Howells FM, Ingvar M, Kircher TTJ, Krug A, Kuplicki RT, Landén M, Lemke H, Liberg B, Lopez-Jaramillo C, Malt UF, Martyn FM, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadic I, Opel N, Ophoff RA, Overs BJ, Pfarr JK, Pineda-Zapata JA, Pomarol-Clotet E, Raduà J, Repple J, Richter M, Ringwald KG, Roberts G, Ross A, Salvador R, Savitz J, Schmitt S, Schofield PR, Sim K, Stein DJ, Stein F, Temmingh HS, Thiel K, Thomopoulos SI, van Haren NEM, Van Gestel H, Vargas C, Vieta E, Vreeker A, Waltemate L, Yatham LN, Ching CRK, Andreassen OA, Thompson PM, and Hajek T
- Subjects
- Body Mass Index, Cluster Analysis, Humans, Magnetic Resonance Imaging, Obesity complications, Obesity diagnostic imaging, Temporal Lobe pathology, Bipolar Disorder diagnosis
- Abstract
Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry., Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles., Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex., Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)
- Published
- 2022
- Full Text
- View/download PDF
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