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1. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Author

Jiang, Yuchao, Luo, Cheng, Wang, Jijun, Palaniyappan, Lena, Chang, Xiao, Xiang, Shitong, Zhang, Jie, Duan, Mingjun, Huang, Huan, Gaser, Christian, Nemoto, Kiyotaka, Miura, Kenichiro, Hashimoto, Ryota, Westlye, Lars, Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole, Kircher, Tilo, Nenadić, Igor, Stein, Frederike, Thomas-Odenthal, Florian, Teutenberg, Lea, Usemann, Paula, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Meinert, Susanne, Lencer, Rebekka, Tang, Yingying, Zhang, Tianhong, Li, Chunbo, Yue, Weihua, Zhang, Yuyanan, Yu, Xin, Zhou, Enpeng, Lin, Ching-Po, Tsai, Shih-Jen, Rodrigue, Amanda, Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Pomarol-Clotet, Edith, Salvador, Raymond, Fuentes-Claramonte, Paola, Garcia-León, María, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali, Uslu, Ozgul, Burhanoglu, Birce, Uyar Demir, Aslihan, Rootes-Murdy, Kelly, Calhoun, Vince, Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young, Kim, Woo-Sung, Sponheim, Scott, Demro, Caroline, Ramsay, Ian, Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min, Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn, Rossell, Susan, Hughes, Matthew, Woods, William, Carruthers, Sean, Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Homan, Stephanie, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana, Preda, Adrian, Thomopoulos, Sophia, Jahanshad, Neda, Cui, Long-Biao, and Yao, Dezhong

Subjects
Humans, Schizophrenia, Male, Female, Adult, Algorithms, Magnetic Resonance Imaging, Gray Matter, Machine Learning, Middle Aged, Brain, Cross-Sectional Studies, Europe, Neuroimaging, Reproducibility of Results, North America, Hippocampus
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.

Published
2024

2. Protocol for the Development and Initial Validation of the COG-IMPACT Tool: A Purpose-Built Unmet Needs Assessment for Cancer-Related Cognitive Impairment.

Author

Haywood, Darren, OConnor, Moira, Baughman, Frank, Chan, Alexandre, Chan, Raymond, Dauer, Evan, Dhillon, Haryana, Henneghan, Ashley, Lawrence, Blake, Lustberg, Maryam, Vardy, Janette, Rossell, Susan, and Hart, Nicolas

Subjects
cancer fog, cancer survivors, cancer-related cognitive impairment, challenges, health professionals, measure development, mixed methods, qualitative, quality of life, unmet needs
Abstract

(1) Background: A significant proportion of cancer survivors report experiencing a cognitive fog that affects their ability to think coherently and quickly, and reason with clarity. This has been referred to as cancer-related cognitive impairment (CRCI). CRCI has extensive impacts on the daily lives of people living with or beyond cancer, including occupational, social, and psychological functioning. Oncology health professionals report feeling under-resourced to effectively assess the needs of an individual with CRCI and then provide optimal care and referral. (2) Methods: The objective of this project is to develop and provide an initial validation of the first purpose-built unmet needs assessment for CRCI: the Unmet Needs Assessment of Cancer-Related Cognitive Impairment Impact (COG-IMPACT). We will use a multiple-stage, co-design, mixed-methods approach to develop and provide an initial validation of the COG-IMPACT. (3) Results: The primary anticipated result of this research is the production of the COG-IMPACT, the first purpose-built unmet needs assessment for CRCI. The assessment could be used by health professionals to understand the unmet needs and facilitate optimal care and referral for cancer survivors, by survivors to elucidate their supportive needs and advocate for their care, and by researchers to examine the correlates of unmet needs relating to CRCI, as well as how best to support people with CRCI.

Published
2024

3. Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Author

Jiang, Yuchao, Luo, Cheng, Wang, Jijun, Palaniyappan, Lena, Chang, Xiao, Xiang, Shitong, Zhang, Jie, Duan, Mingjun, Huang, Huan, Gaser, Christian, Nemoto, Kiyotaka, Miura, Kenichiro, Hashimoto, Ryota, Westlye, Lars T, Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole A, Kircher, Tilo, Nenadić, Igor, Stein, Frederike, Thomas-Odenthal, Florian, Teutenberg, Lea, Usemann, Paula, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Meinert, Susanne, Lencer, Rebekka, Tang, Yingying, Zhang, Tianhong, Li, Chunbo, Yue, Weihua, Zhang, Yuyanan, Yu, Xin, Zhou, Enpeng, Lin, Ching-Po, Tsai, Shih-Jen, Rodrigue, Amanda L, Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Pomarol-Clotet, Edith, Salvador, Raymond, Fuentes-Claramonte, Paola, Garcia-León, María Ángeles, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali Saffet, Uslu, Ozgul, Burhanoglu, Birce Begum, Demir, Aslihan Uyar, Rootes-Murdy, Kelly, Calhoun, Vince D, Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young Chul, Kim, Woo-Sung, Sponheim, Scott R, Demro, Caroline, Ramsay, Ian S, Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min Tae M, Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn E, Rossell, Susan L, Hughes, Matthew, Woods, William, Carruthers, Sean P, Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Homan, Stephanie, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana D, Preda, Adrian, Thomopoulos, Sophia, Jahanshad, Neda, Cui, Long-Biao, and Yao, Dezhong

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Machine Learning and Artificial Intelligence, Mental Illness, Clinical Research, Brain Disorders, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Behavioral and Social Science, Biomedical Imaging, Mental health, Good Health and Well Being, ENIGMA, artificial intelligence, brain gray matter, schizophrenia, structural MRI, subtype
Abstract

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Published
2023

16. Deep Brain Stimulation for Obsessive-Compulsive Disorder: Optimal Stimulation Sites

Author

Meyer, Garance M., Hollunder, Barbara, Li, Ningfei, Butenko, Konstantin, Dembek, Till A., Hart, Lauren, Nombela, Cristina, Mosley, Philip, Akram, Harith, Acevedo, Nicola, Borron, Benjamin M., Chou, Tina, Castaño Montoya, Juan Pablo, Strange, Bryan, Barcia, Juan A., Tyagi, Himanshu, Castle, David J., Smith, Andrew H., Choi, Ki Sueng, Kopell, Brian H., Mayberg, Helen S., Sheth, Sameer A., Goodman, Wayne K., Leentjens, Albert F.G., Richardson, R. Mark, Rossell, Susan L., Bosanac, Peter, Cosgrove, G. Rees, Kuhn, Jens, Visser-Vandewalle, Veerle, Figee, Martijn, Dougherty, Darin D., Siddiqi, Shan H., Zrinzo, Ludvic, Joyce, Eileen, Baldermann, Juan Carlos, Fox, Michael D., Neudorfer, Clemens, and Horn, Andreas

Published
2024
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31. Effect of time of day on reward circuitry: Further thoughts on methods, prompted by Steel et al 2018

Author

Murray, Greg, Orr, Catherine, Byrne, Jamie E. M., Hughes, Matthew E., Rossell, Susan L., and Johnson, Sheri L.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

The interplay between circadian and reward function is well understood in animal models, and is of growing interest as an aetiological explanation in psychopathologies. Circadian modulation of reward function has been demonstrated in human behavioural data, but understanding at the neural level is limited. In 2017, our group published results of a first step in addressing this deficit, demonstrating a diurnal rhythm in fMRI-measured reward activation. In 2018, Steel et al wrote a constructive critique of our findings, and the aim of this paper is to outline how future research could improve on our first proof-of-concept study. Key challenges include addressing divergent and convergent validity (by addressing non-reward neural variation, and testing for absence of variation in threat-related pathways), preregistration and power analysis to protect against false positives, wider range of fMRI methods (to directly test our post-hoc hypothesis of some form of reward prediction error, and multiple phases of reward), the parallel collection of behavioural data (particularly self-reported positive affect, and actigraphically measured activity) to illuminate the nature of the reward activation across the day, and some attempt to parse out circadian versus homeostatic/masking influences on any observed diurnal rhythm in neural reward activation., Comment: 9 pages, 1 figure

Published
2018

40. N-acetyl cysteine (NAC) augmentation in the treatment of obsessive-compulsive disorder: A phase III, 20-week, double-blind, randomized, placebo-controlled trial

Author

Sarris, Jerome, Byrne, Gerard, Castle, David, Bousman, Chad, Oliver, Georgina, Cribb, Lachlan, Blair-West, Scott, Brakoulias, Vlasios, Camfield, David, Ee, Carolyn, Chamoli, Suneel, Boschen, Mark, Dean, Olivia M., Dowling, Nathan, Menon, Ranjit, Murphy, Jenifer, Metri, Najwa-Joelle, Nguyen, Thomas P., Wong, Andrew, Jordan, Rebecca, Karamacoska, Diana, Rossell, Susan L., Berk, Michael, and Ng, Chee H.

Published
2022
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48. Psychiatric comorbidity and severity in anorexia nervosa: a comparative study of the DSM-5, the ICD-11, and overvaluation of Weight/Shape severity ratings.

Author

Dang, An Binh, Kiropoulos, Litza, Castle, David, Jenkins, Zoe, Phillipou, Andrea, Rossell, Susan, and Krug, Isabel

Subjects
MENTAL illness risk factors, RISK assessment, PEARSON correlation (Statistics), DATA analysis, FISHER exact test, BODY weight, SEVERITY of illness index, CLASSIFICATION of mental disorders, DESCRIPTIVE statistics, CHI-squared test, BODY image, ANOREXIA nervosa, ANALYSIS of variance, STATISTICS, COMPARATIVE studies, SOCIODEMOGRAPHIC factors, COMORBIDITY, NOSOLOGY, DISEASE risk factors, PSYCHOSOCIAL factors
Abstract

This study assessed the rate of a.) the total and b.) specific psychiatric comorbidities among the three severity ratings for Anorexia Nervosa (AN): DSM-5, ICD-11 and overvaluation of weight and shape (OWS). The sample comprised 312 treatment-seeking patients with AN (mean age = 26.9). Weight and height were taken at intake to calculate BMI, the foundation for the DSM-5 and ICD-11 severity indices. The EDE-Q was used to assess OWS, and the Mini International Neuropsychiatric Interview was conducted to assess psychiatric comorbidities. For the DSM-5, the mild severity group showed a higher total number of psychiatric comorbidities, especially for panic, social anxiety, generalised anxiety, and post-traumatic stress disorders compared to the severe and extremely severe groups. ICD-11 and OWS severity groups did not significantly differ in total comorbidities, except for major depressive disorder and obsessive-compulsive disorders being more prevalent in the "significantly low BMI" ICD-11 group. The high OWS group displayed a notably higher rate of major depressive disorder than the low OWS group. The study underscores inconsistent patterns across the three severity systems, emphasising the need to recognise the current limitations of the assessed severity classification systems in AN assessment and guiding treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Development of a harmonized sociodemographic and clinical questionnaire for mental health research: A Delphi-method-based consensus recommendation.

Author

Lotfaliany, Mojtaba, Agustini, Bruno, Walker, Adam J, Turner, Alyna, Wrobel, Anna L, Williams, Lana J, Dean, Olivia M, Miles, Stephanie, Rossell, Susan L, Berk, Michael, and Mohebbi, Mohammadreza

Subjects
CONSENSUS (Social sciences), PSYCHIATRY, PSYCHIATRIC treatment, QUESTIONNAIRES, EXPERIMENTAL design, RESEARCH methodology, SOCIODEMOGRAPHIC factors
Abstract

Objective: Harmonized tools are essential for reliable data sharing and accurate identification of relevant factors in mental health research. The primary objective of this study was to create a harmonized questionnaire to collect demographic, clinical and behavioral data in diverse clinical trials in adult psychiatry. Methods: We conducted a literature review and examined 24 questionnaires used in previously published randomized controlled trials in psychiatry, identifying a total of 27 domains previously explored. Using a Delphi-method process, a task force team comprising experts in psychiatry, epidemiology and statistics selected 15 essential domains for inclusion in the final questionnaire. Results: The final selection resulted in a concise set of 22 questions. These questions cover factors such as age, sex, gender, ancestry, education, living arrangement, employment status, home location, relationship status, and history of medical and mental illness. Behavioral factors like physical activity, diet, smoking, alcohol and illicit drug use were also included, along with one question addressing family history of mental illness. Income was excluded due to high confounding and redundancy, while language was included as a measure of migration status. Conclusion: The recommendation and adoption of this harmonized tool for the assessment of demographic, clinical and behavioral data in mental health research can enhance data consistency and enable comparability across clinical trials. [ABSTRACT FROM AUTHOR]

Published
2024
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