1
|
Constantinides C, Baltramonaityte V, Caramaschi D, Han LKM, Lancaster TM, Zammit S, Freeman TP, Walton E. Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study. Cortex 2024; 172:1-13. [PMID: 38154374 DOI: 10.1016/j.cortex.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/22/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023]
Abstract
Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.
Collapse
Affiliation(s)
| | | | - Doretta Caramaschi
- Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Laura K M Han
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Orygen, Parkville, Australia
| | | | - Stanley Zammit
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom P Freeman
- Addiction and Mental Health Group (AIM), Department of Psychology, University of Bath, UK
| | | |
Collapse
|
2
|
Diniz BS, Seitz-Holland J, Sehgal R, Kasamoto J, Higgins-Chen AT, Lenze E. Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research. Am J Geriatr Psychiatry 2024; 32:1-16. [PMID: 37845116 PMCID: PMC10841054 DOI: 10.1016/j.jagp.2023.09.014] [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: 07/20/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 10/18/2023]
Abstract
The geroscience hypothesis asserts that physiological aging is caused by a small number of biological pathways. Despite the explosion of geroscience research over the past couple of decades, the research on how serious mental illnesses (SMI) affects the biological aging processes is still in its infancy. In this review, we aim to provide a critical appraisal of the emerging literature focusing on how we measure biological aging systematically, and in the brain and how SMIs affect biological aging measures in older adults. We will also review recent developments in the field of cellular senescence and potential targets for interventions for SMIs in older adults, based on the geroscience hypothesis.
Collapse
Affiliation(s)
- Breno S Diniz
- UConn Center on Aging & Department of Psychiatry (BSD), School of Medicine, University of Connecticut Health Center, Farmington, CT.
| | - Johanna Seitz-Holland
- Department of Psychiatry (JSH), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Psychiatry (JSH), Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Raghav Sehgal
- Program in Computational Biology and Bioinformatics (RS, JK), Yale University, New Haven, CT
| | - Jessica Kasamoto
- Program in Computational Biology and Bioinformatics (RS, JK), Yale University, New Haven, CT
| | - Albert T Higgins-Chen
- Department of Psychiatry (ATHC), Yale University School of Medicine, New Haven, CT; Department of Pathology (ATHC), Yale University School of Medicine, New Haven, CT
| | - Eric Lenze
- Department of Psychiatry (EL), School of Medicine, Washington University at St. Louis, St. Louis, MO
| |
Collapse
|
3
|
Chiappelli J, Adhikari BM, Kvarta MD, Bruce HA, Goldwaser EL, Ma Y, Chen S, Ament S, Shuldiner AR, Mitchell BD, Kochunov P, Wang DJJ, Hong LE. Depression, stress and regional cerebral blood flow. J Cereb Blood Flow Metab 2023; 43:791-800. [PMID: 36606600 PMCID: PMC10108192 DOI: 10.1177/0271678x221148979] [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: 03/22/2022] [Revised: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 01/07/2023]
Abstract
Decreased cerebral blood flow (CBF) may be an important mechanism associated with depression. In this study we aimed to determine if the association of CBF and depression is dependent on current level of depression or the tendency to experience depression over time (trait depression), and if CBF is influenced by depression-related factors such as stressful life experiences and antidepressant medication use. CBF was measured in 254 participants from the Amish Connectome Project (age 18-76, 99 men and 154 women) using arterial spin labeling. All participants underwent assessment of symptoms of depression measured with the Beck Depression Inventory and Maryland Trait and State Depression scales. Individuals diagnosed with a unipolar depressive disorder had significantly lower average gray matter CBF compared to individuals with no history of depression or to individuals with a history of depression that was in remission at time of study. Trait depression was significantly associated with lower CBF, with the associations strongest in cingulate gyrus and frontal white matter. Use of antidepressant medication and more stressful life experiences were also associated with significantly lower CBF. Resting CBF in specific brain regions is associated with trait depression, experience of stressful life events, and current antidepressant use, and may provide a valuable biomarker for further studies.
Collapse
Affiliation(s)
- Joshua Chiappelli
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Bhim M Adhikari
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Mark D Kvarta
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Heather A Bruce
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Eric L Goldwaser
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Yizhou Ma
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Shuo Chen
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Seth Ament
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Alan R Shuldiner
- Department of Medicine, University
of Maryland School of Medicine, Baltimore, MD, USA
| | - Braxton D Mitchell
- Department of Medicine, University
of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education
Clinical Center, Baltimore Veterans Administration, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| | - Danny JJ Wang
- Laboratory of Functional MRI
Technology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck
School of Medicine, University of Southern California, Los Angeles, CA,
USA
| | - L Elliot Hong
- Maryland Psychiatric Research
Center, Department of Psychiatry, University of Maryland School of Medicine,
Baltimore, MD, USA
| |
Collapse
|
4
|
Hatch KS, Gao S, Ma Y, Russo A, Jahanshad N, Thompson PM, Adhikari BM, Bruce H, Van der Vaart A, Sotiras A, Kvarta MD, Nichols TE, Schmaal L, Hong LE, Kochunov P. Brain deficit patterns of metabolic illnesses overlap with those for major depressive disorder: A new metric of brain metabolic disease. Hum Brain Mapp 2023; 44:2636-2653. [PMID: 36799565 PMCID: PMC10028678 DOI: 10.1002/hbm.26235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non-psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta-analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer's disease. We used a regional vulnerability index (RVI) for MET (RVI-MET) to measure individual's brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI-MET than for any of the individual brain measures. We replicated elevation of RVI-MET in a sample of MDD participants with MET versus non-MET. RVI-MET scores were significantly correlated with the volume of white matter hyperintensities, a neurological consequence of MET and age, in both groups. Higher RVI-MET in both samples was associated with obesity, tobacco smoking and frequent alcohol use but was unrelated to antidepressant use. In summary, MET effects on the brain were regionally specific and individual similarity to the pattern was more strongly associated with MET than any regional brain structural metric. Effects of MET overlapped with the reported brain differences in MDD, likely due to higher incidence of MET, smoking and alcohol use in subjects with MDD.
Collapse
Affiliation(s)
- Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Alessandro Russo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, California, USA
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Andrew Van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Aristeidis Sotiras
- Institute of Informatics, University of Washington, School of Medicine, St. Louis, Missouri, USA
- Department of Radiology, University of Washington, School of Medicine, St. Louis, Missouri, USA
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Thomas E Nichols
- Nuffield Department of Population Health of the University of Oxford, Oxford, UK
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
- Orygen, Parkville, Australia
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
5
|
Traumatic stress load and stressor reactivity score associated with accelerated gray matter maturation in youths indexed by normative models. Mol Psychiatry 2023; 28:1137-1145. [PMID: 36575305 DOI: 10.1038/s41380-022-01908-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022]
Abstract
Understanding how traumatic stress affects typical brain development during adolescence is critical to elucidate underlying mechanisms related to both maladaptive functioning and resilience after traumatic exposures. The current study aimed to map deviations from normative ranges of brain gray matter for youths with traumatic exposures. For each cortical and subcortical gray matter region, normative percentiles of variations were established using structural MRI from typically developing youths without any traumatic exposure (n = 245; age range = 8-23) from the Philadelphia Neurodevelopmental Cohort (PNC). The remaining PNC participants with neuroimaging data (n = 1129) were classified as either within the normative range (5-95%), delayed (>95%) or accelerated (<5%) maturational ranges for each region using the normative model. An averaged quantile regression index was calculated across all regions. Mediation models revealed that high traumatic stress load was positively associated with poorer cognitive functioning and greater psychopathology, and these associations were mediated by accelerated gray matter maturation. Furthermore, higher stressor reactivity scores, which represent a less resilient response under traumatic stress, were positively correlated with greater acceleration of gray matter maturation (r = 0.224, 95% CI = [0.17, 0.28], p < 0.001), suggesting that more accelerated maturation was linked to greater stressor response regardless of traumatic stress load. We conclude that traumatic stress is a source of deviation from normative brain development associated with poorer cognitive functioning and more psychopathology in the long run.
Collapse
|
6
|
Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies. J Psychiatr Res 2023; 157:180-191. [PMID: 36473289 DOI: 10.1016/j.jpsychires.2022.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/14/2022] [Accepted: 11/12/2022] [Indexed: 11/16/2022]
Abstract
Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature.
Collapse
|
7
|
Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
Collapse
Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
| |
Collapse
|
8
|
Abstract
Bipolar disorder (BD) is a severe mental illness associated with alterations in brain organization. Neuroimaging studies have generated a large body of knowledge regarding brain morphological and functional abnormalities in BD. Current advances in the field have focussed on the need for more precise neuroimaging biomarkers. Here we present a selective overview of precision neuroimaging biomarkers for BD, focussing on personalized metrics and novel neuroimaging methods aiming to provide mechanistic insights into the brain alterations associated with BD. The evidence presented covers (a) machine learning techniques applied to neuroimaging data to differentiate patients with BD from healthy individuals or other clinical groups; (b) the 'brain-age-gap-estimation (brainAGE), which is an individualized measure of brain health; (c) diffusional kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and Positron Emission Tomography (PET) techniques that open new opportunities to measure microstructural changes in neurite/synaptic integrity and function.
Collapse
Affiliation(s)
- Delfina Janiri
- Department of Psychiatry, Fondazione Policlinico Universitario 'Agostino Gemelli' IRCCS, Roma, Italy.,Department of Psychiatry and Neurology, Sapienza University of Rome, Roma, Italy
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver British Columbia, Canada
| |
Collapse
|