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Koskinen SM, Ahveninen J, Kujala T, Kaprio J, O'Donnell BF, Osipova D, Viken RJ, Näätänen R, Rose RJ. Association of lifetime major depressive disorder with enhanced attentional sensitivity measured with P3 response in young adult twins. Biol Psychol 2022; 171:108345. [PMID: 35525377 DOI: 10.1016/j.biopsycho.2022.108345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 04/29/2022] [Accepted: 05/01/2022] [Indexed: 11/02/2022]
Abstract
Major depression is associated with alterations in the auditory P3 event-related potential (ERP). However, the persistence of these abnormalities after recovery from depressive episodes, especially in young adults, is not well known. Furthermore, the potential influence of substance use on this association is poorly understood. Young adult twin pairs (N=177) from the longitudinal FinnTwin16 study were studied with a psychiatric interview, and P3a and P3b ERPs elicited by task-irrelevant novel sounds and targets, respectively. Dyadic linear mixed-effect models were used to distinguish the effects of lifetime major depressive disorder from familial factors and effects of alcohol problem drinking and tobacco smoking. P3a amplitude was significantly increased and P3b latency decreased, in individuals with a history of lifetime major depression, when controlling the fixed effects of alcohol abuse, tobacco, gender, twins' birth order, and zygosity. These results suggest that past lifetime major depressive disorder may be associated with enhanced attentional sensitivity.
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Affiliation(s)
- Sini M Koskinen
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, POB 21, FI-00014, Helsinki, Finland.
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, Massachusetts 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, Massachusetts 02115, USA.
| | - Teija Kujala
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, POB 21, FI-00014, Helsinki, Finland.
| | - Jaakko Kaprio
- Department of Public Health & Institute for Molecular Medicine, University of Helsinki, POB 4, FI-00014, Helsinki, Finland.
| | - Brian F O'Donnell
- Department of Psychological & Brain Sciences, Indiana University, 1101 E 10th St, Bloomington, Indiana 47405, USA.
| | - Daria Osipova
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, POB 21, FI-00014, Helsinki, Finland.
| | - Richard J Viken
- Department of Psychological & Brain Sciences, Indiana University, 1101 E 10th St, Bloomington, Indiana 47405, USA.
| | - Risto Näätänen
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, POB 21, FI-00014, Helsinki, Finland.
| | - Richard J Rose
- Department of Psychological & Brain Sciences, Indiana University, 1101 E 10th St, Bloomington, Indiana 47405, USA.
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Sultana A, Zinnah MA, Shozib HB, Howlader ZH, Alauddin M. Functional Profiling and Future Research Direction of Rice Bran Oil in Bangladesh. J Oleo Sci 2021; 70:1551-1563. [PMID: 34732634 DOI: 10.5650/jos.ess21212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Rice bran oil (RBO) has been demonstrated to affect complex malfunctioned conditions such as oxidative stress, hyperlipidemia, hyperglycemia, hypertension, inflammation, abnormal cell growth (cancer), ulceration, immune and cognitive modulation. This unique effect of RBO is due to the presence of well-balanced fatty acid composition and several bioactive compounds, γ- oryzanol (cycloartenyl ferulate, 24-methylenecycloartanyl ferulate, campesterol ferulate, and β-sitosteryl ferulate), vitamin E (tocopherol and tocotrienol), phytosterols (β-sitosterol, campesterol and stigmasterol) and other nutrients. The RBO composition of bioactive compounds varied geographically, thus the clear-cut mechanisms of action on complex disease cascades are still required. This review article summarized the RBO compositional profiling and compared it with other edible oils. This article also summarized Bangladesh RBO profiling and their proposed mechanism of action as well as the first line of defense in the prevention, management, and control of complex disease conditions. This review indicates how Bangladesh RBO increase their opportunity to be functional food for 21st century's ailment.
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Affiliation(s)
- Afroza Sultana
- Department of Nutrition and Food Technology, Jashore University of Science and Technology
| | | | | | | | - Md Alauddin
- Department of Nutrition and Food Technology, Jashore University of Science and Technology
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Chahal R, Weissman DG, Marek S, Rhoads SA, Hipwell AE, Forbes EE, Keenan K, Guyer AE. Girls' brain structural connectivity in late adolescence relates to history of depression symptoms. J Child Psychol Psychiatry 2020; 61:1224-1233. [PMID: 31879977 PMCID: PMC7316589 DOI: 10.1111/jcpp.13184] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/20/2019] [Accepted: 11/22/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Girls' depressive symptoms typically increase in adolescence, with individual differences in course and severity being key risk factors for impaired emotional functioning in young adulthood. Given the continued brain white matter (WM) maturation that occurs in adolescence, the present study tested whether structural connectivity patterns in late adolescence are associated with variation in the course of depression symptom severity throughout adolescence. METHOD Participants were girls (N = 115) enrolled in a multiyear prospective cohort study of risk for depression. Initial depression severity (intercept) at age 10 and change in severity (linear slope) across ages 10-19 were examined in relation to WM tractography collected at age 19. Network-based statistic analyses were used to identify clusters showing variation in structural connectivity in association with depressive symptom intercept, slope, and their interaction. RESULTS Higher initial depressive severity and steeper positive slope (separately) were associated with greater structural connectivity between temporal, subcortical socioaffective, and occipital regions. Intercept showed more connectivity associations than slope. The interaction effect indicated that higher initial symptom severity and a steeper negative slope (i.e., alleviating symptoms) were related to greater connectivity between cognitive control regions. Moderately severe symptoms that worsened over time were followed by greater connectivity between self-referential and cognitive regions (e.g., posterior cingulate and frontal gyrus). CONCLUSIONS Higher depressive symptom severity in early adolescence and increasing symptom severity over time may forecast structural connectivity differences in late adolescence, particularly in pathways involving cognitive and emotion-processing regions. Understanding how clinical course relates to neurobiological correlates may inform new treatment approaches to adolescent depression.
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Affiliation(s)
- Rajpreet Chahal
- Department of Human Ecology, University of California, Davis, Davis, CA 95616
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618
| | | | - Scott Marek
- Department of Psychiatry, Washington University, St. Louis, MO 63110
| | - Shawn A. Rhoads
- Department of Psychology, Georgetown University, Washington, DC 20057
| | - Alison E. Hipwell
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213
| | - Erika E. Forbes
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213
| | - Kate Keenan
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637
| | - Amanda E. Guyer
- Department of Human Ecology, University of California, Davis, Davis, CA 95616
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618
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Tymofiyeva O, Zhou VX, Lee CM, Xu D, Hess CP, Yang TT. MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future. Front Hum Neurosci 2020; 14:237. [PMID: 32733218 PMCID: PMC7359264 DOI: 10.3389/fnhum.2020.00237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.
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Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Vivian X Zhou
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Chuan-Mei Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.,Clinical Excellence Research Center, Stanford University, Stanford, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Tony T Yang
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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Rantamäki T, Kohtala S. Encoding, Consolidation, and Renormalization in Depression: Synaptic Homeostasis, Plasticity, and Sleep Integrate Rapid Antidepressant Effects. Pharmacol Rev 2020; 72:439-465. [DOI: 10.1124/pr.119.018697] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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Tymofiyeva O, Yuan JP, Huang CY, Connolly CG, Henje Blom E, Xu D, Yang TT. Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT). NEUROIMAGE-CLINICAL 2019; 23:101914. [PMID: 31491813 PMCID: PMC6627980 DOI: 10.1016/j.nicl.2019.101914] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 06/14/2019] [Accepted: 06/29/2019] [Indexed: 12/29/2022]
Abstract
Purpose Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT. Methods We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation. Results The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus. Conclusions Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression. Machine learning predicted symptom reduction in depressed teens with 83% accuracy. Resulting prunned classification tree size was 7, with only 3 attributes. Change in depression symptoms correlated with node strength of the right thalamus.
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Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
| | - Justin P Yuan
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Chiung-Yu Huang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA 94143, USA
| | - Colm G Connolly
- Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Biomedical Sciences, Florida State University College of Medicine, 1115 West Call Street, Tallahassee, FL 32306, USA
| | - Eva Henje Blom
- Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Clinical Science/Child- and Adolescent Psychiatry, Umeå University, SE-901 87 Umeå, Sweden
| | - Duan Xu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Tony T Yang
- Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA
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