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Cho KIK, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrieli S, Niznikiewicz M, Stone WS, Wang J, Shenton ME, Pasternak O. Excessive interstitial free-water in cortical gray matter preceding accelerated volume changes in individuals at clinical high risk for psychosis. Mol Psychiatry 2024; 29:3623-3634. [PMID: 38830974 DOI: 10.1038/s41380-024-02597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 06/05/2024]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is suggested to represent atypical developmental or degenerative changes accompanying an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate into volume loss is crucial. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Of the CHR individuals, 33 developed psychosis (CHR-P), while 127 did not (CHR-NP). Among all participants, longitudinal data was available for 45 HCs, 17 CHR-P, and 66 CHR-NP. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the CHR-P from the CHR-NP. In addition, for completeness, we also investigated changes in cortical thickness and in white matter (WM) microstructure. At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in many brain areas, the CHR-P group demonstrated significantly accelerated changes (iFW increase and volume reduction) with time than the CHR-NP group. Cortical thickness provided similar results as volume, and there were no significant changes in WM microstructure. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes or microstructural WM changes, and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes, as reflected by the increased iFW, are thus an early pathology at the prodromal stage of psychosis that may be useful for a better mechanistic understanding of psychosis development.
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Affiliation(s)
- Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Huijun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL, USA
| | - Matcheri Keshavan
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA
- The McGovern Institute for Brain Research and the Poitras Center for Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Margaret Niznikiewicz
- The Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - William S Stone
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Bergé D, Carter CS, Smucny J. Identification of distinct clinical profiles and trajectories in individuals at high risk of developing psychosis: A latent profile analysis of the north American prodrome longitudinal study consortium-3 dataset. Early Interv Psychiatry 2024; 18:739-749. [PMID: 38351643 PMCID: PMC11323210 DOI: 10.1111/eip.13514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/10/2023] [Accepted: 01/24/2024] [Indexed: 08/16/2024]
Abstract
AIM People at clinical high risk (CHR) for psychosis are a heterogeneous population in regard to clinical presentation and outcome. It is unclear, however, if their baseline clinical characteristics can be used to construct orthogonal subgroups that differ in their clinical trajectory to provide early identification of individuals in need of tailored interventions. METHODS We used latent profile analysis (LPA) to determine the number of distinct clinical profiles within the CHR population using the NAPLS-3 dataset, focusing on the clinical features incorporated in the NAPLS psychosis risk calculator (including age, unusual thought content and suspiciousness, processing speed, verbal learning and memory function, social functioning decline, life events, childhood trauma, and family history of psychosis). We then conducted a between-profile comparisons of clinical trajectories based on psychotic and depressive symptoms as well as substance use disorder (SUD) related features over time. RESULTS Two distinct profiles emerged. One profile, comprising approximately 25% of the sample, was significantly older, displayed better cognitive performance, experienced more types of traumatic and undesirable life events, exhibited a greater decline in functioning in the past year, and was more likely to have relatives with psychosis. This group showed worse positive symptoms and SUD-related features over time, although groups did not differ in the proportion of individuals who developed psychosis. CONCLUSIONS LPA results suggest CHRs can be segregated into two profiles with different clinical trajectories. Characterizing individuals within these clinical profiles may help understand the divergent outcomes of this population and ultimately facilitate the development of specialized interventions.
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Affiliation(s)
- Daniel Bergé
- Hospital del Mar Research Institute; Centro de Investigación Biomédica en Red, Área de Salud Mental (CIBERSAM); Pompeu Fabra University, Spain
| | | | - Jason Smucny
- Department of Psychiatry, University of California, Davis
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3
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Reinen JM, Polosecki P, Castro E, Corcoran CM, Cecchi GA, Colibazzi T. Multimodal fusion of brain signals for robust prediction of psychosis transition. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:54. [PMID: 38773120 PMCID: PMC11109212 DOI: 10.1038/s41537-024-00464-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/15/2024] [Indexed: 05/23/2024]
Abstract
The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.
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Affiliation(s)
- Jenna M Reinen
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
| | | | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Tiziano Colibazzi
- Department of Psychiatry, The New York State Psychiatric Institute, Columbia College of Physicians and Surgeons, New York, NY, USA
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Cooper R, Hayes RA, Corcoran M, Sheth KN, Arnold TC, Stein JM, Glahn DC, Jalbrzikowski M. Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people. Front Neurol 2024; 15:1339223. [PMID: 38585353 PMCID: PMC10995930 DOI: 10.3389/fneur.2024.1339223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/19/2024] [Indexed: 04/09/2024] Open
Abstract
Background Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. Methods T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Results Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Conclusion Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.
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Affiliation(s)
- Rebecca Cooper
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Rebecca A. Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
| | - Mary Corcoran
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
| | - Kevin N. Sheth
- Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, United States
| | - Thomas Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
| | - Joel M. Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David C. Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, United States
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
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Iznak AF, Iznak EV, Damyanovich EV, Shishkovskaya TI, Oleichik IV. [EEG features in young female patients with depressive states at different stages of endogenous mental diseases]. Zh Nevrol Psikhiatr Im S S Korsakova 2024; 124:100-103. [PMID: 39435784 DOI: 10.17116/jnevro2024124091100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2024]
Abstract
OBJECTIVE To search for neurophysiological correlates of the characteristics of the brain functional state in patients with endogenous depression with an ultra-high risk of developing psychosis in comparison with EEG parameters of patients without symptoms of a risk of developing psychosis and patients who have suffered psychotic episode. MATERIAL AND METHODS The study included 92 female patients, aged 16-26 years, at the stage of remission, divided into three groups: with depression without symptoms of ultra-high risk of developing psychosis (group 1, n=42), with depression and attenuated psychotic symptoms, but without a history of a psychotic episode (group 2, n=32) and with depression that developed after experiencing a psychotic episode (group 3, n=18). In all patients, pre-treatment multichannel background EEG was recorded with spectral power analysis in narrow frequency sub-bands. RESULTS According to EEG data, the functional state of the cerebral cortex of patients in group 1 at the stage of remission was approaching normal. The EEG of group 2 and group 3 differed from the EEG of group 1 by significantly lower values of EEG spectral power in the alpha3 sub-band (11-13 Hz) in the occipital leads and a significantly increased content of theta1 (4-6 Hz) activity in the central-parietal areas. Such EEG frequency structure of patients in groups 2 and 3 reflects a reduced functional state of associative areas, and may also indicate dysfunction of the frontal parts of the cerebral cortex. These EEG features of patients in groups 2 and 3 are consistent with a significantly greater severity of their positive and negative symptoms on SAPS and SANS compared to group 1. CONCLUSION In patients with depression at the stage of remission who have symptoms of an ultra-high risk of developing psychosis and in those who have suffered a psychotic episode, a reduced functional state of the associative and frontal areas of the cerebral cortex is noted, which may underlie the characteristics of their clinical condition.
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Affiliation(s)
- A F Iznak
- Mental Health Research Centre, Moscow, Russia
| | - E V Iznak
- Mental Health Research Centre, Moscow, Russia
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6
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Kang IC, Pasternak O, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrielli S, Niznikiewicz M, Stone W, Wang J, Shenton M. Microstructural Cortical Gray Matter Changes Preceding Accelerated Volume Changes in Individuals at Clinical High Risk for Psychosis. RESEARCH SQUARE 2023:rs.3.rs-3179575. [PMID: 37841868 PMCID: PMC10571628 DOI: 10.21203/rs.3.rs-3179575/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is thought to result from an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate is crucial, as volume reduction likely indicates an underlying neurodegenerative process. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the 33 individuals at CHR who developed psychosis (CHR-P) from the 127 individuals at CHR who did not (CHR-NP). At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in most brain areas, the CHR-P group demonstrated significantly accelerated iFW increase and volume reduction with time than the CHR-NP group. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes are thus an early pathology at the prodromal stage of psychosis that may be useful for early detection and a better mechanistic understanding of psychosis development.
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Affiliation(s)
| | | | | | | | - Johanna Seitz-Holland
- Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China
| | | | | | | | | | | | | | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
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Mamah D. A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2023; 8:e230005. [PMID: 37427077 PMCID: PMC10327607 DOI: 10.20900/jpbs.20230005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The risk for developing schizophrenia is increased among first-degree relatives of those with psychotic disorders, but the risk is even higher in those meeting established criteria for clinical high risk (CHR), a clinical construct most often comprising of attenuated psychotic experiences. Conversion to psychosis among CHR youth has been reported to be about 15-35% over three years. Accurately identifying individuals whose psychotic symptoms will worsen would facilitate earlier intervention, but this has been difficult to do using behavior measures alone. Brain-based risk markers have the potential to improve the accuracy of predicting outcomes in CHR youth. This narrative review provides an overview of neuroimaging studies used to investigate psychosis risk, including studies involving structural, functional, and diffusion imaging, functional connectivity, positron emission tomography, arterial spin labeling, magnetic resonance spectroscopy, and multi-modality approaches. We present findings separately in those observed in the CHR state and those associated with psychosis progression or resilience. Finally, we discuss future research directions that could improve clinical care for those at high risk for developing psychotic disorders.
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Affiliation(s)
- Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, 63110, USA
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Sefik E, Boamah M, Addington J, Bearden CE, Cadenhead KS, Cornblatt BA, Keshavan MS, Mathalon DH, Perkins DO, Stone WS, Tsuang MT, Woods SW, Cannon TD, Walker EF. Sex- and Age-Specific Deviations in Cerebellar Structure and Their Link With Symptom Dimensions and Clinical Outcome in Individuals at Clinical High Risk for Psychosis. Schizophr Bull 2023; 49:350-363. [PMID: 36394426 PMCID: PMC10016422 DOI: 10.1093/schbul/sbac169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND The clinical high-risk (CHR) period offers a temporal window into neurobiological deviations preceding psychosis onset, but little attention has been given to regions outside the cerebrum in large-scale studies of CHR. Recently, the North American Prodrome Longitudinal Study (NAPLS)-2 revealed altered functional connectivity of the cerebello-thalamo-cortical circuitry among individuals at CHR; however, cerebellar morphology remains underinvestigated in this at-risk population, despite growing evidence of its involvement in psychosis. STUDY DESIGN In this multisite study, we analyzed T1-weighted magnetic resonance imaging scans obtained from N = 469 CHR individuals (61% male, ages = 12-36 years) and N = 212 healthy controls (52% male, ages = 12-34 years) from NAPLS-2, with a focus on cerebellar cortex and white matter volumes separately. Symptoms were rated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). The outcome by two-year follow-up was categorized as in-remission, symptomatic, prodromal-progression, or psychotic. General linear models were used for case-control comparisons and tests for volumetric associations with baseline SIPS ratings and clinical outcomes. STUDY RESULTS Cerebellar cortex and white matter volumes differed between the CHR and healthy control groups at baseline, with sex moderating the difference in cortical volumes, and both sex and age moderating the difference in white matter volumes. Baseline ratings for major psychosis-risk dimensions as well as a clinical outcome at follow-up had tissue-specific associations with cerebellar volumes. CONCLUSIONS These findings point to clinically relevant deviations in cerebellar cortex and white matter structures among CHR individuals and highlight the importance of considering the complex interplay between sex and age when studying the neuromaturational substrates of psychosis risk.
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Affiliation(s)
- Esra Sefik
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Michelle Boamah
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Carrie E Bearden
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
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Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, Palagini L, Zerbinati L, Folesani F, Murri MB, Caruso R, Grassi L. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Curr Psychiatry Rep 2022; 24:925-936. [PMID: 36399236 PMCID: PMC9780131 DOI: 10.1007/s11920-022-01399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE OF REVIEW This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment. RECENT FINDINGS Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.
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Affiliation(s)
- Maria Ferrara
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy.
- Department of Psychiatry, Yale School of Medicine, 34 Park Street, New Haven, CT, USA.
| | - Giorgia Franchini
- Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Via Campi 213/B, Modena, Italy
- Department of Mathematics and Computer Science, University of Ferrara, Via Macchiavelli 33, Ferrara, Italy
| | - Melissa Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, 333 Cedar St., New Haven, CT, USA
| | - Marcello Cutroni
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Beatrice Valier
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Tommaso Toffanin
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Laura Palagini
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Zerbinati
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Federica Folesani
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Martino Belvederi Murri
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Rosangela Caruso
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Grassi
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
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10
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Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence. Transl Psychiatry 2022; 12:481. [PMID: 36385133 PMCID: PMC9668814 DOI: 10.1038/s41398-022-02242-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/21/2022] [Accepted: 10/27/2022] [Indexed: 11/17/2022] Open
Abstract
Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk subjects with no later transition to psychosis (CHR_NT), 15 clinical high-risk subjects with later transition (CHR_T), and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two-classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject's clinical status was predicted by importing separately (a) the difference of entropy feature map and (b) the contrast feature map, resulting in classification balanced accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT, and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.
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Hallucinations and Brain Morphology Across Early Adolescence: A Longitudinal Neuroimaging Study. Biol Psychiatry 2022; 92:781-790. [PMID: 35871096 DOI: 10.1016/j.biopsych.2022.05.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/27/2022] [Accepted: 05/09/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Psychotic disorders have been widely associated with structural brain abnormalities. However, it is unclear whether brain structure predicts psychotic experiences in youth from the general population, owing to an overall paucity of studies and predominantly cross-sectional designs. Here, the authors investigated longitudinal associations between brain morphology and hallucinations from childhood to early adolescence. METHODS This study was embedded in the population-based Generation R Study. Children underwent structural neuroimaging at age 10 years (N = 2042); a subsample received a second scan at age 14 years (n = 964). Hallucinations were assessed at ages 10 and 14 years and studied as a binary variable. Cross-lagged panel models and generalized linear mixed-effects models were fitted to examine longitudinal associations between brain morphology and hallucinations. RESULTS Smaller total gray and white matter volumes and total cortical surface area at baseline were associated with a higher occurrence of hallucinations between ages 10 and 14 years. The regions associated with hallucinations were widespread, including the frontal, parietal, temporal, and occipital lobes, as well as the insula and cingulate cortex. Analyses of subcortical structures revealed that smaller baseline hippocampal volumes were longitudinally associated with hallucinations, although this association was no longer significant following adjustment for intracranial volume. No evidence for reverse temporality was observed (i.e., hallucinations predicting brain differences). CONCLUSIONS The findings from this longitudinal study suggest that global structural brain differences are associated with the development of hallucinations. These results extend findings from clinical populations and provide evidence for a neurodevelopmental vulnerability across the psychosis continuum.
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Ku BS, Addington J, Bearden CE, Cadenhead KS, Cannon TD, Compton MT, Cornblatt BA, Druss BG, Keshavan M, Mathalon DH, Perkins DO, Stone WS, Tsuang MT, Woods SW, Walker EF. The associations between area-level residential instability and gray matter volumes from the North American Prodrome Longitudinal Study (NAPLS) consortium. Schizophr Res 2022; 241:1-9. [PMID: 35066429 PMCID: PMC8960350 DOI: 10.1016/j.schres.2021.12.050] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 12/21/2021] [Accepted: 12/30/2021] [Indexed: 01/16/2023]
Abstract
INTRODUCTION Area-level residential instability (ARI), an index of social fragmentation, has been shown to explain the association between urbanicity and psychosis. Urban upbringing has been shown to be associated with reduced gray matter volumes (GMV)s of brain regions corresponding to the right caudal middle frontal gyrus (CMFG) and rostral anterior cingulate cortex (rACC). We hypothesize that greater ARI will be associated with reduced right CMFG and rACC GMVs. METHODS Data were collected at baseline as part of the North American Prodrome Longitudinal Study Phase 2. Counties where participants resided during childhood were geographically coded using the US Census to area-level factors. ARI was defined as the percentage of residents living in a different house 5 years ago. Generalized linear mixed models tested associations between ARI and GMVs. RESULTS This study included 29 healthy controls (HC)s and 64 clinical high risk for psychosis (CHR-P) individuals who were aged 12 to 24 years, had remained in their baseline residential area, and had magnetic resonance imaging scans. ARI was associated with reduced right CMFG (adjusted β = -0.258; 95% CI = -0.502 to -0.015) and right rACC volumes (adjusted β = -0.318; 95% CI = -0.612 to -0.023). The interaction term (ARI-by-diagnostic group) in the prediction of both brain regions was not significant, indicating that the relationships between ARI and regional brain volumes held for both CHR-P and HCs. CONCLUSIONS ARI may adversely impact similar brain regions as urban upbringing. Further investigation into the potential mechanisms of the relationship between ARI and neurobiology, including social stress, is needed.
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Affiliation(s)
- Benson S Ku
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States.
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, United States
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California, San Diego, CA, United States
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University, New Haven, CT, United States; Department of Psychology, Yale University, New Haven, CT, United States
| | - Michael T Compton
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States; New York State Psychiatric Institute, New York, NY, United States
| | - Barbara A Cornblatt
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Benjamin G Druss
- Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, United States; San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States
| | - William S Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Ming T Tsuang
- Department of Psychiatry, University of California, San Diego, CA, United States
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, United States
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, United States
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Insula volumes in first-episode and chronic psychosis: A longitudinal MRI study. Schizophr Res 2022; 241:14-23. [PMID: 35074528 DOI: 10.1016/j.schres.2021.12.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/21/2021] [Accepted: 12/28/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Alterations in insular grey matter (GM) volume has been consistently reported for affective and non-affective psychoses both in chronic and first-episode patients, ultimately suggesting that the insula might represent a good region to study in order to assess the longitudinal course of psychotic disorders. Therefore, in this longitudinal Magnetic Resonance Imaging (MRI) study, we aimed at further investigating the key role of insular volumes in psychosis. MATERIAL AND METHODS 68 First-Episode Psychosis (FEP) patients, 68 patients with Schizophrenia (SCZ), 47 Bipolar Disorder (BD) patients, and 94 Healthy Controls (HC) were enrolled and underwent a 1.5 T MRI evaluation. A subsample of 99 subjects (10 HC, 23 BD, 29 SCZ, 37 FEP) was rescanned after 2,53 ± 1,68 years. The insular cortex was manually traced and then divided into an anterior and posterior portion. Group and correlation analyses were then performed both at baseline and at follow-up. RESULTS At baseline, greater anterior and lower posterior insular GM volumes were observed in chronic patients. At follow-up, we found that FEP patients had a significant GM volume increase from baseline to follow-up, especially in the posterior insula whereas chronic patients showed a relative stability. Finally, significant negative correlations between illness severity and pharmacological treatment and insular GM volumes were observed in the whole group of psychotic patients. CONCLUSIONS The longitudinal assessment of both chronic and first-episode patients allowed us to detect a complex pattern of GM abnormalities in selective sub-portions of insular volumes, ultimately suggesting that this structure could represent a key biological marker of psychotic disorders.
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Kristensen TD, Glenthøj LB, Ambrosen K, Syeda W, Raghava JM, Krakauer K, Wenneberg C, Fagerlund B, Pantelis C, Glenthøj BY, Nordentoft M, Ebdrup BH. Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra-high risk for psychosis. Acta Psychiatr Scand 2021; 144:448-463. [PMID: 34333760 DOI: 10.1111/acps.13355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/26/2021] [Accepted: 07/29/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR). METHODS 110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression. RESULTS Ten UHR individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ2 = 17.595, p = 0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R2 = 0.055, F = 6.084, p = 0.016) and functional level (R2 = 0.040, F = 4.57, p = 0.036) at 6 months, but not at 12 months. CONCLUSION Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.
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Affiliation(s)
- Tina D Kristensen
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Copenhagen Research Centre for Mental Health (CORE), Copenhagen University Hospital, Copenhagen, Denmark
| | - Louise B Glenthøj
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Copenhagen Research Centre for Mental Health (CORE), Copenhagen University Hospital, Copenhagen, Denmark
| | - Karen Ambrosen
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Warda Syeda
- Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne, Melbourne, Vic., Australia
| | - Jayachandra M Raghava
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, University of Copenhagen, Glostrup, Denmark
| | - Kristine Krakauer
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Copenhagen Research Centre for Mental Health (CORE), Copenhagen University Hospital, Copenhagen, Denmark
| | - Christina Wenneberg
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Copenhagen Research Centre for Mental Health (CORE), Copenhagen University Hospital, Copenhagen, Denmark
| | - Birgitte Fagerlund
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Department of Psychology, Faculty of Social Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christos Pantelis
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne, Melbourne, Vic., Australia
| | - Birte Y Glenthøj
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Nordentoft
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Copenhagen Research Centre for Mental Health (CORE), Copenhagen University Hospital, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bjørn H Ebdrup
- Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, and Center for Neuropsychiatric Schizophrenia Research, CNSR, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark.,Department of Psychology, Faculty of Social Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Albores-Garcia D, McGlothan JL, Guilarte TR. Early-life lead exposure and neurodevelopmental disorders. CURRENT OPINION IN TOXICOLOGY 2021; 26:22-27. [PMID: 34013137 DOI: 10.1016/j.cotox.2021.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Lead (Pb2+) exposure is a global public health problem of major proportion with an alarming number of children with blood Pb2+ levels > 10 >g/dL, twice the current CDC reference level for Pb2+ exposure. Mounting evidence from population-based studies suggests an association between chronic early life Pb2+ exposure (CELLE) and psychiatric disorders, specifically schizophrenia (SZ). Preclinical studies suggest a common mechanism in the pathophysiology of CELLE and SZ, NMDA receptor hypofunction. Here we describe human and experimental animal studies providing the evidence for such an association. Further, recent preclinical studies indicate that Pb2+-induced changes in neurotransmitter receptors that mediate the action(s) of drugs of abuse are increased in brain regions associated with addiction circuits in adolescence, a period of increased susceptibility to drug use and abuse and expression of psychiatric disease in humans. In summary, the relationship between the global burden of childhood Pb2+ exposure and the latent onset of psychiatric disorders and predisposition to drug use requires further investigations in human populations.
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Affiliation(s)
- D Albores-Garcia
- Brain, Behavior & the Environment Program Department of Environmental Health Sciences Robert Stempel College of Public Health & Social Work Florida International University Miami, FL 33199, United States
| | - J L McGlothan
- Brain, Behavior & the Environment Program Department of Environmental Health Sciences Robert Stempel College of Public Health & Social Work Florida International University Miami, FL 33199, United States
| | - T R Guilarte
- Brain, Behavior & the Environment Program Department of Environmental Health Sciences Robert Stempel College of Public Health & Social Work Florida International University Miami, FL 33199, United States
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Worthington MA, Cannon TD. Prediction and Prevention in the Clinical High-Risk for Psychosis Paradigm: A Review of the Current Status and Recommendations for Future Directions of Inquiry. Front Psychiatry 2021; 12:770774. [PMID: 34744845 PMCID: PMC8569129 DOI: 10.3389/fpsyt.2021.770774] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
Prediction and prevention of negative clinical and functional outcomes represent the two primary objectives of research conducted within the clinical high-risk for psychosis (CHR-P) paradigm. Several multivariable "risk calculator" models have been developed to predict the likelihood of developing psychosis, although these models have not been translated to clinical use. Overall, less progress has been made in developing effective interventions. In this paper, we review the existing literature on both prediction and prevention in the CHR-P paradigm and, primarily, outline ways in which expanding and combining these paths of inquiry could lead to a greater improvement in individual outcomes for those most at risk.
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Affiliation(s)
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, United States.,Department of Psychiatry, Yale University, New Haven, CT, United States
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