Brandon M Hager1, Matcheri S Keshavan2. 1. Massachusetts Mental Health Center Division of Public Psychiatry, Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, 75 Fenwood Road, 5th Floor, Boston, MA 02115 USA (617) 754-1244. 2. Massachusetts Mental Health Center Division of Public Psychiatry, Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, 75 Fenwood Road, 5th Floor, Boston, MA 02115 USA (617) 754-1256.
Abstract
BACKGROUND: Biomarkers provide clinicians with a predictable model for the diagnosis, treatment and follow-up of medical ailments. Psychiatry has lagged behind other areas of medicine in the identification of biomarkers for clinical diagnosis and treatment. In this review, we investigated the current state of neuroimaging as it pertains to biomarkers for psychosis. METHODS: We reviewed systematic reviews and meta-analyses of the structural (sMRI), functional (fMRI), diffusion-tensor (DTI), Positron emission tomography (PET) and spectroscopy (MRS) studies of subjects at-risk or those with an established schizophrenic illness. Only articles reporting effect-sizes and confidence intervals were included in an assessment of robustness. RESULTS: Out of the identified meta-analyses and systematic reviews, 21 studies met the inclusion criteria for assessment. There were 13 sMRI, 4 PET, 3 MRS, and 1 DTI studies. The search terms included in the current review encompassed familial high risk (FHR), clinical high risk (CHR), First episode (FES), Chronic (CSZ), schizophrenia spectrum disorders (SSD), and healthy controls (HC). CONCLUSIONS: Currently, few neuroimaging biomarkers can be considered ready for diagnostic use in patients with psychosis. At least in part, this may be related to the challenges inherent in the current symptom-based approach to classifying these disorders. While available studies suggest a possible value of imaging biomarkers for monitoring disease progression, more systematic research is needed. To date, the best value of imaging data in psychoses has been to shed light on questions of disease pathophysiology, especially through the characterization of endophenotypes.
BACKGROUND: Biomarkers provide clinicians with a predictable model for the diagnosis, treatment and follow-up of medical ailments. Psychiatry has lagged behind other areas of medicine in the identification of biomarkers for clinical diagnosis and treatment. In this review, we investigated the current state of neuroimaging as it pertains to biomarkers for psychosis. METHODS: We reviewed systematic reviews and meta-analyses of the structural (sMRI), functional (fMRI), diffusion-tensor (DTI), Positron emission tomography (PET) and spectroscopy (MRS) studies of subjects at-risk or those with an established schizophrenic illness. Only articles reporting effect-sizes and confidence intervals were included in an assessment of robustness. RESULTS: Out of the identified meta-analyses and systematic reviews, 21 studies met the inclusion criteria for assessment. There were 13 sMRI, 4 PET, 3 MRS, and 1 DTI studies. The search terms included in the current review encompassed familial high risk (FHR), clinical high risk (CHR), First episode (FES), Chronic (CSZ), schizophrenia spectrum disorders (SSD), and healthy controls (HC). CONCLUSIONS: Currently, few neuroimaging biomarkers can be considered ready for diagnostic use in patients with psychosis. At least in part, this may be related to the challenges inherent in the current symptom-based approach to classifying these disorders. While available studies suggest a possible value of imaging biomarkers for monitoring disease progression, more systematic research is needed. To date, the best value of imaging data in psychoses has been to shed light on questions of disease pathophysiology, especially through the characterization of endophenotypes.
Authors: Shivani Patel; Katie Mahon; Robin Wellington; Jianping Zhang; William Chaplin; Philip R Szeszko Journal: Schizophr Res Date: 2011-04-29 Impact factor: 4.939
Authors: David C Van Essen; Stephen M Smith; Deanna M Barch; Timothy E J Behrens; Essa Yacoub; Kamil Ugurbil Journal: Neuroimage Date: 2013-05-16 Impact factor: 6.556
Authors: H W Thermenos; M S Keshavan; R J Juelich; E Molokotos; S Whitfield-Gabrieli; B K Brent; N Makris; L J Seidman Journal: Am J Med Genet B Neuropsychiatr Genet Date: 2013-10 Impact factor: 3.568
Authors: P Fusar-Poli; R Smieskova; M J Kempton; B C Ho; N C Andreasen; S Borgwardt Journal: Neurosci Biobehav Rev Date: 2013-06-14 Impact factor: 8.989
Authors: Philippe Conus; Larry J Seidman; Margot Fournier; Lijing Xin; Martine Cleusix; Philipp S Baumann; Carina Ferrari; Ann Cousins; Luis Alameda; Mehdi Gholam-Rezaee; Philippe Golay; Raoul Jenni; T-U Wilson Woo; Matcheri S Keshavan; Chin B Eap; Joanne Wojcik; Michel Cuenod; Thierry Buclin; Rolf Gruetter; Kim Q Do Journal: Schizophr Bull Date: 2018-02-15 Impact factor: 9.306
Authors: Ney Alliey-Rodriguez; Tamar A Grey; Rebecca Shafee; Huma Asif; Olivia Lutz; Nicolas R Bolo; Jaya Padmanabhan; Neeraj Tandon; Madeline Klinger; Katherine Reis; Jonathan Spring; Lucas Coppes; Victor Zeng; Rachal R Hegde; Dung T Hoang; Deepthi Bannai; Uzma Nawaz; Philip Henson; Siyuan Liu; Diane Gage; Steven McCarroll; Jeffrey R Bishop; Scot Hill; James L Reilly; Rebekka Lencer; Brett A Clementz; Peter Buckley; David C Glahn; Shashwath A Meda; Balaji Narayanan; Godfrey Pearlson; Matcheri S Keshavan; Elena I Ivleva; Carol Tamminga; John A Sweeney; David Curtis; Judith A Badner; Sarah Keedy; Judith Rapoport; Chunyu Liu; Elliot S Gershon Journal: Transl Psychiatry Date: 2019-09-17 Impact factor: 6.222
Authors: Sagnik Bhattacharyya; Robin Wilson; Elizabeth Appiah-Kusi; Aisling O'Neill; Michael Brammer; Jesus Perez; Robin Murray; Paul Allen; Matthijs G Bossong; Philip McGuire Journal: JAMA Psychiatry Date: 2018-11-01 Impact factor: 21.596