1
|
Dörfel RP, Arenas-Gomez JM, Svarer C, Ganz M, Knudsen GM, Svensson JE, Plavén-Sigray P. Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features. GeroScience 2024; 46:4123-4133. [PMID: 38668887 PMCID: PMC11335712 DOI: 10.1007/s11357-024-01148-6] [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: 03/01/2024] [Accepted: 03/28/2024] [Indexed: 08/22/2024] Open
Abstract
To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer's disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean = 38, std = 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE = 6.63 years, std = 0.74 years) and GM volume (mean MAE = 6.95 years, std = 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE = 5.54 years, std = 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.
Collapse
Affiliation(s)
- Ruben P Dörfel
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Joan M Arenas-Gomez
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Claus Svarer
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jonas E Svensson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden
| | - Pontus Plavén-Sigray
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden.
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
| |
Collapse
|
2
|
González-Peñas J, Alloza C, Brouwer R, Díaz-Caneja CM, Costas J, González-Lois N, Gallego AG, de Hoyos L, Gurriarán X, Andreu-Bernabeu Á, Romero-García R, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Arrojo M, Vilella E, Gutiérrez-Zotes A, Perez-Rando M, Moltó MD, Buimer E, van Haren N, Cahn W, O'Donovan M, Kahn RS, Arango C, Pol HH, Janssen J, Schnack H. Accelerated Cortical Thinning in Schizophrenia Is Associated With Rare and Common Predisposing Variation to Schizophrenia and Neurodevelopmental Disorders. Biol Psychiatry 2024; 96:376-389. [PMID: 38521159 DOI: 10.1016/j.biopsych.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Schizophrenia is a highly heritable disorder characterized by increased cortical thinning throughout the life span. Studies have reported a shared genetic basis between schizophrenia and cortical thickness. However, no genes whose expression is related to abnormal cortical thinning in schizophrenia have been identified. METHODS We conducted linear mixed models to estimate the rates of accelerated cortical thinning across 68 regions from the Desikan-Killiany atlas in individuals with schizophrenia compared with healthy control participants from a large longitudinal sample (ncases = 169 and ncontrols = 298, ages 16-70 years). We studied the correlation between gene expression data from the Allen Human Brain Atlas and accelerated thinning estimates across cortical regions. Finally, we explored the functional and genetic underpinnings of the genes that contribute most to accelerated thinning. RESULTS We found a global pattern of accelerated cortical thinning in individuals with schizophrenia compared with healthy control participants. Genes underexpressed in cortical regions that exhibit this accelerated thinning were downregulated in several psychiatric disorders and were enriched for both common and rare disrupting variation for schizophrenia and neurodevelopmental disorders. In contrast, none of these enrichments were observed for baseline cross-sectional cortical thickness differences. CONCLUSIONS Our findings suggest that accelerated cortical thinning, rather than cortical thickness alone, serves as an informative phenotype for neurodevelopmental disruptions in schizophrenia. We highlight the genetic and transcriptomic correlates of this accelerated cortical thinning, emphasizing the need for future longitudinal studies to elucidate the role of genetic variation and the temporal-spatial dynamics of gene expression in brain development and aging in schizophrenia.
Collapse
Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain.
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rachel Brouwer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Noemí González-Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rafael Romero-García
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla, HUVR/CSIC/Universidad de Sevilla/CIBERSAM, Instituto de Salud Carlos III, Sevilla, Spain; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lourdes Fañanás
- CIBERSAM, Madrid, Spain; Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Madrid, Spain; Faculty of Medicine and Health Sciences-Psychiatry, Universidad de Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias, Instituto de Neurociencias del Principado de Asturias, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Madrid, Spain; BIOARABA Health Research Institute, Organización Sanitaria Integrada Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Madrid, Spain; Hospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain
| | - Lourdes Martorell
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Manuel Arrojo
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Elisabet Vilella
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Alfonso Gutiérrez-Zotes
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Marta Perez-Rando
- Fundación Investigación Hospital Clínico de València, Fundación Investigación Hospital Clínico de Valencia, València, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain
| | - María Dolores Moltó
- CIBERSAM, Madrid, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain; Department of Genetics, Universitat de València, Campus of Burjassot, València, Spain
| | - Elizabeth Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Altrecht Mental Health Institute, Altrecht Science, Utrecht, the Netherlands
| | - Michael O'Donovan
- Medical Research Council for Neuropsychiatric Genetics and Genomics and Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Hilleke Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| |
Collapse
|
3
|
Hua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, Mathalon DH. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophr Bull 2024; 50:1159-1170. [PMID: 38815987 PMCID: PMC11349027 DOI: 10.1093/schbul/sbae074] [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: 06/01/2024]
Abstract
BACKGROUND AND HYPOTHESIS Brain development/aging is not uniform across individuals, spawning efforts to characterize brain age from a biological perspective to model the effects of disease and maladaptive life processes on the brain. The brain age gap represents the discrepancy between estimated brain biological age and chronological age (in this case, based on structural magnetic resonance imaging, MRI). Structural MRI studies report an increased brain age gap (biological age > chronological age) in schizophrenia, with a greater brain age gap related to greater negative symptom severity. Less is known regarding the nature of this gap early in schizophrenia (ESZ), if this gap represents a psychosis conversion biomarker in clinical high-risk (CHR-P) individuals, and how altered brain development and/or aging map onto specific symptom facets. STUDY DESIGN Using structural MRI, we compared the brain age gap among CHR-P (n = 51), ESZ (n = 78), and unaffected comparison participants (UCP; n = 90), and examined associations with CHR-P psychosis conversion (CHR-P converters n = 10; CHR-P non-converters; n = 23) and positive and negative symptoms. STUDY RESULTS ESZ showed a greater brain age gap relative to UCP and CHR-P (Ps < .010). CHR-P individuals who converted to psychosis showed a greater brain age gap (P = .043) relative to CHR-P non-converters. A larger brain age gap in ESZ was associated with increased experiential (P = .008), but not expressive negative symptom severity. CONCLUSIONS Consistent with schizophrenia pathophysiological models positing abnormal brain maturation, results suggest abnormal brain development is present early in psychosis. An increased brain age gap may be especially relevant to motivational and functional deficits in schizophrenia.
Collapse
Affiliation(s)
- Jessica P Y Hua
- Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Samantha V Abram
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Barbara Stuart
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Susanna L Fryer
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| |
Collapse
|
4
|
Ho NCW, Bethlehem RAI, Seidlitz J, Nogovitsyn N, Metzak P, Ballester PL, Hassel S, Rotzinger S, Poppenk J, Lam RW, Taylor VH, Milev R, Bullmore ET, Alexander-Bloch AF, Frey BN, Harkness KL, Addington J, Kennedy SH, Dunlop K. Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:786-799. [PMID: 38679324 DOI: 10.1016/j.bpsc.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to those seen in aging. However, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool that quantifies normative neurodevelopmental trajectories. METHODS A total of 304 participants with MDD and 236 control participants without depression were recruited and scanned from 3 studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for 1) differences between participants with MDD and control participants; 2) differences between individuals with versus without severe childhood maltreatment; and 3) correlations with depressive symptom severity, neurocognitive assessment domains, and escitalopram treatment response. RESULTS Brain centiles were significantly lower in the MDD group than in the control group. Brain centile was also significantly correlated with working memory in the control group but not the MDD group. No significant associations were observed between depression severity or antidepressant treatment response and brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS Consistent with previous work on machine learning models that predict brain age, brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications for neurocognitive deficits associated with aging-related cognitive function.
Collapse
Affiliation(s)
- Natalie C W Ho
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Ontario, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Ontario, Canada; Faculty of Arts and Sciences, University of Toronto, Toronto, Ontario, Canada
| | | | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Institute of Translational Medicine & Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Nikita Nogovitsyn
- Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Ontario, Canada
| | - Paul Metzak
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Pedro L Ballester
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Stefanie Hassel
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Susan Rotzinger
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Ontario, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Mood Disorders Treatment and Research Centre, St Joseph's Healthcare, Hamilton, Ontario, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Jordan Poppenk
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada; Department of Psychology, Queen's University, Kingston, Ontario, Canada; School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Valerie H Taylor
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Roumen Milev
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada; Providence Care Hospital, Kingston, Ontario, Canada
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Institute of Translational Medicine & Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Benicio N Frey
- Mood Disorders Treatment and Research Centre, St Joseph's Healthcare, Hamilton, Ontario, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Kate L Harkness
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute and Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta, Canada
| | - Sidney H Kennedy
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Ontario, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Katharine Dunlop
- Keenan Research Centre for Biomedical Research, Unity Health Toronto, Toronto, Ontario, Canada; Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
| |
Collapse
|
5
|
Crestol A, de Lange AMG, Schindler L, Subramaniapillai S, Nerland S, Oppenheimer H, Westlye LT, Andreassen OA, Agartz I, Tamnes CK, Barth C. Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort. Horm Behav 2024; 164:105596. [PMID: 38944998 DOI: 10.1016/j.yhbeh.2024.105596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/02/2024]
Abstract
In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
Collapse
Affiliation(s)
- Arielle Crestol
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Ann-Marie G de Lange
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Louise Schindler
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Sivaniya Subramaniapillai
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Stener Nerland
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hannah Oppenheimer
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway; Department of Clinical Neuroscience, Centre for Psychiatry Research, Stockholm Health Care Services, Karolinska Institute, Stockholm County Council, Stockholm, Sweden; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway.
| |
Collapse
|
6
|
Chen YS, Kuo CY, Lu CH, Wang YW, Chou KH, Lin WC. Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease. Neurobiol Aging 2024; 140:122-129. [PMID: 38776615 DOI: 10.1016/j.neurobiolaging.2024.05.003] [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] [Received: 10/26/2023] [Revised: 04/20/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.
Collapse
Affiliation(s)
- Yueh-Sheng Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Yuan Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yuan-Wei Wang
- The Science & Technology Policy Research and Information Center, National Applied Research Laboratories(NARLabs), Taipei, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
| |
Collapse
|
7
|
Kim WS, Heo DW, Maeng J, Shen J, Tsogt U, Odkhuu S, Zhang X, Cheraghi S, Kim SW, Ham BJ, Rami FZ, Sui J, Kang CY, Suk HI, Chung YC. Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders. Schizophr Bull 2024; 50:804-814. [PMID: 38085061 PMCID: PMC11283195 DOI: 10.1093/schbul/sbad167] [Citation(s) in RCA: 1] [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] [Indexed: 07/29/2024]
Abstract
BACKGROUND AND HYPOTHESIS The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). STUDY DESIGN We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. STUDY RESULTS The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. CONCLUSIONS Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.
Collapse
Affiliation(s)
- Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Da-Woon Heo
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Junyeong Maeng
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Jie Shen
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Yanbian University, Medical School, Yanji, China
| | - Uyanga Tsogt
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Soyolsaikhan Odkhuu
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Xuefeng Zhang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sahar Cheraghi
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chae Yeong Kang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| |
Collapse
|
8
|
Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024:10.1038/s43588-024-00659-8. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
Collapse
Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
| |
Collapse
|
9
|
Berthet P, Haatveit BC, Kjelkenes R, Worker A, Kia SM, Wolfers T, Rutherford S, Alnaes D, Dinga R, Pedersen ML, Dahl A, Fernandez-Cabello S, Dazzan P, Agartz I, Nesvåg R, Ueland T, Andreassen OA, Simonsen C, Westlye LT, Melle I, Marquand A. A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models. Schizophr Bull 2024:sbae107. [PMID: 38970378 DOI: 10.1093/schbul/sbae107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/08/2024]
Abstract
BACKGROUND Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. DESIGN Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. RESULTS LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. CONCLUSIONS This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.
Collapse
Affiliation(s)
- Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Beathe C Haatveit
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Rikka Kjelkenes
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Amanda Worker
- Department of Psychosis Studies, Institute of Psychiatry, King's College, London, UK
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, the Netherlands
- Department Cognitive Science and Artificial Intelligence, Tilburg University, the Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Saige Rutherford
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Dag Alnaes
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Richard Dinga
- Department Cognitive Science and Artificial Intelligence, Tilburg University, the Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Sara Fernandez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, King's College, London, UK
| | - Ingrid Agartz
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ragnar Nesvåg
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Torill Ueland
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Carmen Simonsen
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Andre Marquand
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| |
Collapse
|
10
|
Vakli P, Weiss B, Rozmann D, Erőss G, Nárai Á, Hermann P, Vidnyánszky Z. The effect of head motion on brain age prediction using deep convolutional neural networks. Neuroimage 2024; 294:120646. [PMID: 38750907 DOI: 10.1016/j.neuroimage.2024.120646] [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] [Received: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/23/2024] Open
Abstract
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.
Collapse
Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest 1034, Hungary.
| | - Dorina Rozmann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - György Erőss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary; Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs 7624, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary.
| |
Collapse
|
11
|
Woo MS, Engler JB, Friese MA. The neuropathobiology of multiple sclerosis. Nat Rev Neurosci 2024; 25:493-513. [PMID: 38789516 DOI: 10.1038/s41583-024-00823-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 05/26/2024]
Abstract
Chronic low-grade inflammation and neuronal deregulation are two components of a smoldering disease activity that drives the progression of disability in people with multiple sclerosis (MS). Although several therapies exist to dampen the acute inflammation that drives MS relapses, therapeutic options to halt chronic disability progression are a major unmet clinical need. The development of such therapies is hindered by our limited understanding of the neuron-intrinsic determinants of resilience or vulnerability to inflammation. In this Review, we provide a neuron-centric overview of recent advances in deciphering neuronal response patterns that drive the pathology of MS. We describe the inflammatory CNS environment that initiates neurotoxicity by imposing ion imbalance, excitotoxicity and oxidative stress, and by direct neuro-immune interactions, which collectively lead to mitochondrial dysfunction and epigenetic dysregulation. The neuronal demise is further amplified by breakdown of neuronal transport, accumulation of cytosolic proteins and activation of cell death pathways. Continuous neuronal damage perpetuates CNS inflammation by activating surrounding glia cells and by directly exerting toxicity on neighbouring neurons. Further, we explore strategies to overcome neuronal deregulation in MS and compile a selection of neuronal actuators shown to impact neurodegeneration in preclinical studies. We conclude by discussing the therapeutic potential of targeting such neuronal actuators in MS, including some that have already been tested in interventional clinical trials.
Collapse
Affiliation(s)
- Marcel S Woo
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Broder Engler
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Manuel A Friese
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany.
| |
Collapse
|
12
|
de Lange AMG, Leonardsen EH, Barth C, Schindler LS, Crestol A, Holm MC, Subramaniapillai S, Hill D, Alnæs D, Westlye LT. Parental status and markers of brain and cellular age: A 3D convolutional network and classification study. Psychoneuroendocrinology 2024; 165:107040. [PMID: 38636355 DOI: 10.1016/j.psyneuen.2024.107040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/20/2024]
Abstract
Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain and cellular ageing in 36,323 UK Biobank participants (age range 44.57-82.06 years; 52% female). To assess global effects of parenting on the brain, we trained a 3D convolutional neural network on T1-weighted magnetic resonance images, and estimated brain age in a held-out test set. To investigate regional specificity, we extracted cortical and subcortical volumes using FreeSurfer, and ran hierarchical clustering to group regional volumes based on covariance. Leukocyte telomere length (LTL) derived from DNA was used as a marker of cellular ageing. We employed linear regression models to assess relationships between number of children, brain age, regional brain volumes, and LTL, and included interaction terms to probe sex differences in associations. Lastly, we used the brain measures and LTL as features in binary classification models, to determine if markers of brain and cellular ageing could predict parental status. The results showed associations between a greater number of children born/fathered and younger brain age in both females and males, with stronger effects observed in females. Volume-based analyses showed maternal effects in striatal and limbic regions, which were not evident in fathers. We found no evidence for associations between number of children and LTL. Classification of parental status showed an Area under the ROC Curve (AUC) of 0.57 for the brain age model, while the models using regional brain volumes and LTL as predictors showed AUCs of 0.52. Our findings align with previous population-based studies of middle- and older-aged parents, revealing subtle but significant associations between parental experience and neuroimaging-based surrogate markers of brain health. The findings further corroborate results from longitudinal cohort studies following parents across pregnancy and postpartum, potentially indicating that the parenthood transition is associated with long-term influences on brain health.
Collapse
Affiliation(s)
- Ann-Marie G de Lange
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK.
| | | | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Louise S Schindler
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Arielle Crestol
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | | | - Sivaniya Subramaniapillai
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Dónal Hill
- Swiss Data Science Center (SDSC), EPFL-ETHZ, Switzerland
| | - Dag Alnæs
- Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| |
Collapse
|
13
|
Duan H, Shi R, Kang J, Banaschewski T, Bokde ALW, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland PA, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Papadopoulos Orfanos D, Poustka L, Hohmann S, Holz N, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Lin X, Feng J. Population clustering of structural brain aging and its association with brain development. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301030. [PMID: 38260410 PMCID: PMC10802651 DOI: 10.1101/2024.01.09.24301030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the "last in, first out" mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.
Collapse
Affiliation(s)
- Haojing Duan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Runye Shi
- School of Data Science, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Sylvane Desrivières
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny A Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental Trajectories and Psychiatry", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes; France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental Trajectories and Psychiatry", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental Trajectories and Psychiatry", Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes; France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein Kiel University, Kiel, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität BerlinHumboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS Centre), ISTBI, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Xiaolei Lin
- School of Data Science, Fudan University, Shanghai, China
- Huashan Institute of Medicine, Huashan Hospital affiliated to Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
- School of Data Science, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| |
Collapse
|
14
|
Cho HH, Kim J, Na I, Song HN, Choi JU, Baek IY, Lee JE, Chung JW, Kim CK, Oh K, Bang OY, Kim GM, Seo WK, Park H. Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels. Heliyon 2024; 10:e32375. [PMID: 38947444 PMCID: PMC11214500 DOI: 10.1016/j.heliyon.2024.e32375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 07/02/2024] Open
Abstract
Aging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases.
Collapse
Affiliation(s)
- Hwan-ho Cho
- Department of Electronics Engineering, Incheon National University, Incheon, South Korea
| | - Jonghoon Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Ha-Na Song
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Un Choi
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - In-Young Baek
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Ji-Eun Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Won Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Chi-Kyung Kim
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Oh-Young Bang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gyeong-Moon Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Woo-Keun Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| |
Collapse
|
15
|
Porta-Casteràs D, Vicent-Gil M, Serra-Blasco M, Navarra-Ventura G, Solé B, Montejo L, Torrent C, Martinez-Aran A, De la Peña-Arteaga V, Palao D, Vieta E, Cardoner N, Cano M. Increased grey matter volumes in the temporal lobe and its relationship with cognitive functioning in euthymic patients with bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110962. [PMID: 38365103 DOI: 10.1016/j.pnpbp.2024.110962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is characterized by episodic mood dysregulation, although a significant portion of patients suffer persistent cognitive impairment during euthymia. Previous magnetic resonance imaging (MRI) research suggests BD patients may have accelerated brain aging, observed as lower grey matter volumes. How these neurostructural alterations are related to the cognitive profile of BD is unclear. METHODS We aim to explore this relationship in euthymic BD patients with multimodal structural neuroimaging. A sample of 27 euthymic BD patients and 24 healthy controls (HC) underwent structural grey matter MRI and diffusion-weighted imaging (DWI). BD patient's cognition was also assessed. FreeSurfer algorithms were used to obtain estimations of regional grey matter volumes. White matter pathways were reconstructed using TRACULA, and four diffusion metrics were extracted. ANCOVA models were performed to compare BD patients and HC values of regional grey matter volume and diffusion metrics. Global brain measures were also compared. Bivariate Pearson correlations were explored between significant brain results and five cognitive domains. RESULTS Euthymic BD patients showed higher ventricular volume (F(1, 46) = 6.04; p = 0.018) and regional grey matter volumes in the left fusiform (F(1, 46) = 15.03; pFDR = 0.015) and bilateral parahippocampal gyri compared to HC (L: F(1, 46) = 12.79, pFDR = 0.025/ R: F(1, 46) = 15.25, pFDR = 0.015). Higher grey matter volumes were correlated with greater executive function (r = 0.53, p = 0.008). LIMITATIONS We evaluated a modest sample size with concurrent pharmacological treatment. CONCLUSIONS Higher medial temporal volumes in euthymic BD patients may be a potential signature of brain resilience and cognitive adaptation to a putative illness neuroprogression. This knowledge should be integrated into further efforts to implement imaging into BD clinical management.
Collapse
Affiliation(s)
- D Porta-Casteràs
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Mental Health Department, Unitat de Neurociència Traslacional, Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - M Vicent-Gil
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain
| | - M Serra-Blasco
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Programa eHealth ICOnnecta't, Institut Català d'Oncologia, Barcelona, Spain
| | - G Navarra-Ventura
- Research Institute of Health Sciences (IUNICS), University of the Balearic Islands (UIB), Palma (Mallorca), Spain; Health Research Institute of the Balearic Islands (IdISBa), Son Espases University Hospital (HUSE), Palma (Mallorca), Spain; CIBERES, Carlos III Health Institute, Madrid, Spain
| | - B Solé
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - L Montejo
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - C Torrent
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - A Martinez-Aran
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - V De la Peña-Arteaga
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - D Palao
- Mental Health Department, Unitat de Neurociència Traslacional, Parc Tauli University Hospital, Institut d'Investigació i Innovació Sanitària Parc Taulí (I3PT), Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain
| | - E Vieta
- CIBERSAM, Carlos III Health Institute, Madrid, Spain; Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - N Cardoner
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Department of Psychiatry and Forensic Medicine, School of Medicine Bellaterra, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain.
| | - M Cano
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau (IIB-SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; CIBERSAM, Carlos III Health Institute, Madrid, Spain
| |
Collapse
|
16
|
Salisbury DF, Wulf BM, Seebold D, Coffman BA, Curtis MT, Karim HT. Predicted Brain Age in First-Episode Psychosis: Association with Inexpressivity. Brain Sci 2024; 14:532. [PMID: 38928532 PMCID: PMC11201938 DOI: 10.3390/brainsci14060532] [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: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
Accelerated brain aging is a possible mechanism of pathology in schizophrenia. Advances in MRI-based brain development algorithms allow for the calculation of predicted brain age (PBA) for individuals. Here, we assessed PBA in 70 first-episode schizophrenia-spectrum individuals (FESz) and 76 matched healthy neurotypical comparison individuals (HC) to determine if FESz showed advanced aging proximal to psychosis onset and whether PBA was associated with neurocognitive, social functioning, or symptom severity measures. PBA was calculated with BrainAgeR (v2.1) from T1-weighted MR scans. There were no differences in the PBAs between groups. After controlling for actual age, a "younger" PBA was associated with higher vocabulary scores among all individuals, while an "older" PBA was associated with more severe negative symptom "Inexpressivity" component scores among FESz. Female participants in both groups had an elevated PBA relative to male participants. These results suggest that a relatively younger brain age is associated with a better semantic memory performance. There is no evidence for accelerated aging in FESz with a late adolescent/early adult onset. Despite a normative PBA, FESz with a greater residual PBA showed impairments in a cluster of negative symptoms, which may indicate some underlying age-related pathology proximal to psychosis onset. Although a period of accelerated aging cannot be ruled out with disease course, it does not occur at the time of the first episode.
Collapse
Affiliation(s)
- Dean F. Salisbury
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Brian M. Wulf
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Dylan Seebold
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Brian A. Coffman
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Mark T. Curtis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Helmet T. Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| |
Collapse
|
17
|
Matte Bon G, Kraft D, Comasco E, Derntl B, Kaufmann T. Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders. Biol Sex Differ 2024; 15:42. [PMID: 38750598 PMCID: PMC11097569 DOI: 10.1186/s13293-024-00615-1] [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: 08/10/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.
Collapse
Affiliation(s)
- Gloria Matte Bon
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany.
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Dominik Kraft
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany
| | - Erika Comasco
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany.
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany.
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| |
Collapse
|
18
|
Faris P, Pischedda D, Palesi F, D’Angelo E. New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment. Front Cell Neurosci 2024; 18:1386583. [PMID: 38799988 PMCID: PMC11116653 DOI: 10.3389/fncel.2024.1386583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/26/2024] [Indexed: 05/29/2024] Open
Abstract
Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.
Collapse
Affiliation(s)
- Pawan Faris
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Doris Pischedda
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
| |
Collapse
|
19
|
Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590811. [PMID: 38712238 PMCID: PMC11071351 DOI: 10.1101/2024.04.23.590811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.
Collapse
|
20
|
Beck D, de Lange AG, Gurholt TP, Voldsbekk I, Maximov II, Subramaniapillai S, Schindler L, Hindley G, Leonardsen EH, Rahman Z, van der Meer D, Korbmacher M, Linge J, Leinhard OD, Kalleberg KT, Engvig A, Sønderby I, Andreassen OA, Westlye LT. Dissecting unique and common variance across body and brain health indicators using age prediction. Hum Brain Mapp 2024; 45:e26685. [PMID: 38647042 PMCID: PMC11034003 DOI: 10.1002/hbm.26685] [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: 12/29/2023] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
Collapse
Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Mental Health and Substance AbuseDiakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Sivaniya Subramaniapillai
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Louise Schindler
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Guy Hindley
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Esten H. Leonardsen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Zillur Rahman
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Max Korbmacher
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Jennifer Linge
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Olof D. Leinhard
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | | | - Andreas Engvig
- Department of Endocrinology, Obesity and Preventive Medicine, Section of Preventive CardiologyOslo University HospitalOsloNorway
| | - Ida Sønderby
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Medical GeneticsOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| |
Collapse
|
21
|
Carriedo N, Rodríguez-Villagra OA, Moguilner S, Morales-Sepulveda JP, Huepe-Artigas D, Soto V, Franco-O’Byrne D, Ibáñez A, Bekinschtein TA, Huepe D. Cognitive, emotional, and social factors promoting psychosocial adaptation: a study of latent profiles in people living in socially vulnerable contexts. Front Psychol 2024; 15:1321242. [PMID: 38680276 PMCID: PMC11050042 DOI: 10.3389/fpsyg.2024.1321242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/20/2024] [Indexed: 05/01/2024] Open
Abstract
Introduction Social adaptation is a multifaceted process that encompasses cognitive, social, and affective factors. Previous research often focused on isolated variables, overlooking their interactions, especially in challenging environments. Our study addresses this by investigating how cognitive (working memory, verbal intelligence, self-regulation), social (affective empathy, family networks, loneliness), and psychological (locus of control, self-esteem, perceived stress) factors interact to influence social adaptation. Methods We analyzed data from 254 adults (55% female) aged 18 to 46 in economically vulnerable households in Santiago, Chile. We used Latent profile analysis (LPA) and machine learning to uncover distinct patters of socioadaptive features and identify the most discriminating features. Results LPA showed two distinct psychosocial adaptation profiles: one characterized by effective psychosocial adaptation and another by poor psychosocial adaptation. The adaptive profile featured individuals with strong emotional, cognitive, and behavioral self-regulation, an internal locus of control, high self-esteem, lower stress levels, reduced affective empathy, robust family support, and decreased loneliness. Conversely, the poorly adapted profile exhibited the opposite traits. Machine learning pinpointed six key differentiating factors in various adaptation pathways within the same vulnerable context: high self-esteem, cognitive and behavioral self-regulation, low stress levels, higher education, and increased social support. Discussion This research carries significant policy implications, highlighting the need to reinforce protective factors and psychological resources, such as self-esteem, self-regulation, and education, to foster effective adaptation in adversity. Additionally, we identified critical risk factors impacting social adaptation in vulnerable populations, advancing our understanding of this intricate phenomenon.
Collapse
Affiliation(s)
- Nuria Carriedo
- Departamento de Psicología Evolutiva y de la Educación, National Distance Education University (UNED), Madrid, Spain
| | - Odir A. Rodríguez-Villagra
- Institute for Psychological Research, University of Costa Rica, Sabanilla, San José, Costa Rica
- Neuroscience Research Center, University of Costa Rica, San José, Costa Rica
| | - Sebastián Moguilner
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- Latin American Brain Health, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Juan Pablo Morales-Sepulveda
- University of Sydney Business School, Darlington, NSW, Australia
- Facultad de Educación Psicología y Familia, Universidad Finis Terrae, Santiago, Chile
| | - Daniela Huepe-Artigas
- Facultad de Educación Psicología y Familia, Universidad Finis Terrae, Santiago, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Vicente Soto
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Daniel Franco-O’Byrne
- Latin American Brain Health, Universidad Adolfo Ibáñez, Santiago, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Agustín Ibáñez
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- Latin American Brain Health, Universidad Adolfo Ibáñez, Santiago, Chile
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Tristan A. Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - David Huepe
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| |
Collapse
|
22
|
Aiskovich M, Castro E, Reinen JM, Fadnavis S, Mehta A, Li H, Dhurandhar A, Cecchi GA, Polosecki P. Fusion of biomedical imaging studies for increased sample size and diversity: a case study of brain MRI. FRONTIERS IN RADIOLOGY 2024; 4:1283392. [PMID: 38645773 PMCID: PMC11026619 DOI: 10.3389/fradi.2024.1283392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 03/11/2024] [Indexed: 04/23/2024]
Abstract
Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data descriptors, file system organization, and metadata. In this study, we present an approach to the integration of multiple brain MRI datasets with a focus on homogenization of their organization and preprocessing for ML. We use our own fusion example (approximately 84,000 images from 54,000 subjects, 12 studies, and 88 individual scanners) to illustrate and discuss the issues faced by study fusion efforts, and we examine key decisions necessary during dataset homogenization, presenting in detail a database structure flexible enough to accommodate multiple observational MRI datasets. We believe our approach can provide a basis for future similarly-minded biomedical ML projects.
Collapse
Affiliation(s)
| | - Eduardo Castro
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Jenna M. Reinen
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Shreyas Fadnavis
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Anushree Mehta
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Hongyang Li
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Amit Dhurandhar
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Guillermo A. Cecchi
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Pablo Polosecki
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| |
Collapse
|
23
|
Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform 2024; 11:9. [PMID: 38573551 PMCID: PMC10994881 DOI: 10.1186/s40708-024-00223-0] [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: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024] Open
Abstract
Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
Collapse
Affiliation(s)
- Jamie L Hanson
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dorthea J Adkins
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Eva Bacas
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Peiran Zhou
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| |
Collapse
|
24
|
Cumplido-Mayoral I, Brugulat-Serrat A, Sánchez-Benavides G, González-Escalante A, Anastasi F, Milà-Alomà M, López-Martos D, Akinci M, Falcón C, Shekari M, Cacciaglia R, Arenaza-Urquijo EM, Minguillón C, Fauria K, Molinuevo JL, Suárez-Calvet M, Grau-Rivera O, Vilaplana V, Gispert JD. The mediating role of neuroimaging-derived biological brain age in the association between risk factors for dementia and cognitive decline in middle-aged and older individuals without cognitive impairment: a cohort study. THE LANCET. HEALTHY LONGEVITY 2024; 5:e276-e286. [PMID: 38555920 DOI: 10.1016/s2666-7568(24)00025-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Neuroimaging-based brain-age delta has been shown to be a mediator linking cardiovascular risk factors to cognitive function. We aimed to assess the mediating role of brain-age delta in the association between modifiable risk factors of dementia and longitudinal cognitive decline in middle-aged and older individuals who are asymptomatic, stratified by Alzheimer's disease pathology. We also explored whether the mediation effect is specific to cognitive domain. METHODS In this cohort study, we included participants from the ALFA+ cohort aged between 45 years and 65 years who were cognitively unimpaired and who had available structural MRI, cerebrospinal fluid β-amyloid (Aβ)42 and Aβ40 measurements obtained within 1 year of each other, modifiable risk factors assessment, and cognitive evaluation over 3 years. Participants were recruited from the Barcelonaβeta Brain Research Center (Barcelona, Spain). Included individuals underwent a first assessment between Oct 25, 2016, and Jan 28, 2020, and a follow-up cognitive assessment 3·28 (SD 0·27) years later. We computed brain-age delta and composites of different cognitive function domains (preclinical Alzheimer's cognitive composite [PACC], attention, executive function, episodic memory, visual processing, and language). We used partial least squares path modelling to explore mediation effects in the associations between modifiable risk factors (including cardiovascular, mental health, mood, metabolic or endocrine history, and alcohol use) and changes in cognitive composites. To assess the role of Alzheimer's disease pathology, we computed separate models for Aβ-negative and Aβ-positive individuals. FINDINGS Of the 419 participants enrolled in ALFA+, 302 met our inclusion criteria, of which 108 participants were classified as Aβ-positive and 194 as Aβ-negative. In Aβ-positive individuals, brain-age delta partially mediated (percent mediation proportion 15·73% [95% CI 14·22-16·66]) the association between modifiable risk factors and decline in overall cognition (across cognitive domains). Brain-age delta fully mediated (mediation proportion 28·03% [26·25-29·21]) the effect of modifiable risk factors on the PACC, wherein increased values for risk factors correlated with an older brain-age delta, and, consequently, an older brain-age delta was linked to greater PACC decline. This effect appears to be primarily driven by memory decline. Mediation was not significant in Aβ-negative individuals (3·52% [0·072-4·17]) on PACC, although path coefficients were not significantly different from those in the Aβ-positive group. INTERPRETATION Our findings suggest that brain-age delta captures the association between modifiable risk factors and longitudinal cognitive decline in middle-aged and older people. In asymptomatic middle-aged and older individuals who are Aβ-positive, the pathology might be the strongest driver of cognitive decline, whereas the effect of risk factors is smaller. Our results highlight the potential of brain-age delta as an objective outcome measure for preventive lifestyle interventions targeting cognitive decline. FUNDING La Caixa Foundation, the TriBEKa Imaging Platform, and the Universities and Research Secretariat of the Catalan Government. TRANSLATION For the Spanish translation of the abstract see Supplementary Materials section.
Collapse
Affiliation(s)
- Irene Cumplido-Mayoral
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Anna Brugulat-Serrat
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain; Global Brain Health Institute, San Francisco, CA, USA
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | - Armand González-Escalante
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Federica Anastasi
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Marta Milà-Alomà
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA
| | - David López-Martos
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Muge Akinci
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; Barcelona Institute of Global Health, Barcelona, Spain
| | - Carles Falcón
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Carolina Minguillón
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; H Lundbeck, Copenhagen, Denmark
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain; Servei de Neurologia, Hospital del Mar, Barcelona, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; CIBER Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, Madrid, Spain; Servei de Neurologia, Hospital del Mar, Barcelona, Spain
| | - Verónica Vilaplana
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Neuroimagen de Enfermedades Neurodegenerativas y Envejecimiento Saludable, Hospital del Mar Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain.
| |
Collapse
|
25
|
van der Markt A, Klumpers U, Dols A, Korten N, Boks MP, Ophoff RA, Beekman A, Kupka R, van Haren NEM, Schnack H. Accelerated brain aging as a biomarker for staging in bipolar disorder: an exploratory study. Psychol Med 2024; 54:1016-1025. [PMID: 37749940 DOI: 10.1017/s0033291723002829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
BACKGROUND Two established staging models outline the longitudinal progression in bipolar disorder (BD) based on episode recurrence or inter-episodic functioning. However, underlying neurobiological mechanisms and corresponding biomarkers remain unexplored. This study aimed to investigate if global and (sub)cortical brain structures, along with brain-predicted age difference (brain-PAD) reflect illness progression as conceptualized in these staging models, potentially identifying brain-PAD as a biomarker for BD staging. METHODS In total, 199 subjects with bipolar-I-disorder and 226 control subjects from the Dutch Bipolar Cohort with a high-quality T1-weighted magnetic resonance imaging scan were analyzed. Global and (sub)cortical brain measures and brain-PAD (the difference between biological and chronological age) were estimated. Associations between individual brain measures and the stages of both staging models were explored. RESULTS A higher brain-PAD (higher biological age than chronological age) correlated with an increased likelihood of being in a higher stage of the inter-episodic functioning model, but not in the model based on number of mood episodes. However, after correcting for the confounding factors lithium-use and comorbid anxiety, the association lost significance. Global and (sub)cortical brain measures showed no significant association with the stages. CONCLUSIONS These results suggest that brain-PAD may be associated with illness progression as defined by impaired inter-episodic functioning. Nevertheless, the significance of this association changed after considering lithium-use and comorbid anxiety disorders. Further research is required to disentangle the intricate relationship between brain-PAD, illness stages, and lithium intake or anxiety disorders. This study provides a foundation for potentially using brain-PAD as a biomarker for illness progression.
Collapse
Affiliation(s)
- Afra van der Markt
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ursula Klumpers
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress, Amsterdam, The Netherlands
| | - Annemiek Dols
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
| | - Nicole Korten
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
| | - Marco P Boks
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
| | - Roel A Ophoff
- Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Aartjan Beekman
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ralph Kupka
- Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- GGZ inGeest Specialized Mental Health Care, Amsterdam, The Netherlands
- Mental Health, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Neeltje E M van Haren
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
- Erasmus Medical Center - Sophia, Child and Adolescent Psychiatry and Psychology, Rotterdam, The Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, The Netherlands
- Department of Languages, Literature and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands
| |
Collapse
|
26
|
Montella A, Tranfa M, Scaravilli A, Barkhof F, Brunetti A, Cole J, Gravina M, Marrone S, Riccio D, Riccio E, Sansone C, Spinelli L, Petracca M, Pisani A, Cocozza S, Pontillo G. Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm. Hum Brain Mapp 2024; 45:e26599. [PMID: 38520360 PMCID: PMC10960551 DOI: 10.1002/hbm.26599] [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] [Received: 07/11/2023] [Revised: 12/23/2023] [Accepted: 01/07/2024] [Indexed: 03/25/2024] Open
Abstract
While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.
Collapse
Affiliation(s)
- Alfredo Montella
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Mario Tranfa
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | | | - Frederik Barkhof
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Institute of NeurologyLondonUK
- Department of Radiology and Nuclear MedicineMS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Arturo Brunetti
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - James Cole
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Michela Gravina
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Stefano Marrone
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Daniele Riccio
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Eleonora Riccio
- Department of Public Health, Nephrology UnitUniversity “Federico II”NaplesItaly
| | - Carlo Sansone
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| | - Letizia Spinelli
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Maria Petracca
- Department of Neurosciences and Reproductive and Odontostomatological SciencesUniversity “Federico II”NaplesItaly
- Department of Human NeurosciencesSapienza University of RomeRomeItaly
| | - Antonio Pisani
- Department of Public Health, Nephrology UnitUniversity “Federico II”NaplesItaly
| | - Sirio Cocozza
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
| | - Giuseppe Pontillo
- Department of Advanced Biomedical SciencesUniversity “Federico II”NaplesItaly
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Institute of NeurologyLondonUK
- Department of Radiology and Nuclear MedicineMS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Department of Electrical Engineering and Information Technology (DIETI)University “Federico II”NaplesItaly
| |
Collapse
|
27
|
Hartmann S, Cearns M, Pantelis C, Dwyer D, Cavve B, Byrne E, Scott I, Yuen HP, Gao C, Allott K, Lin A, Wood SJ, Wigman JTW, Amminger GP, McGorry PD, Yung AR, Nelson B, Clark SR. Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:417-428. [PMID: 38052267 DOI: 10.1016/j.bpsc.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/19/2023] [Accepted: 11/26/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging. METHODS In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231). RESULTS The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%). CONCLUSIONS In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.
Collapse
Affiliation(s)
- Simon Hartmann
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Micah Cearns
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, Melbourne, Victoria, Australia; Western Centre for Health Research & Education, Western Hospital Sunshine, The University of Melbourne, St. Albans, Victoria, Australia
| | - Dominic Dwyer
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Blake Cavve
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Enda Byrne
- Child Health Research Center, The University of Queensland, Brisbane, Queensland, Australia
| | - Isabelle Scott
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Caroline Gao
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ashleigh Lin
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Stephen J Wood
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia; School of Psychology, The University of Birmingham, Birmingham, England, United Kingdom
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - G Paul Amminger
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Patrick D McGorry
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alison R Yung
- Institute for Mental and Physical Health and Clinical Translation, Deakin University, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Scott R Clark
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
| |
Collapse
|
28
|
Zhao S, Zhang T, Zhang W, Pan T, Zhang G, Feng S, Zhang X, Nie B, Liu H, Shan B. Harmonizing T1-Weighted Images to Improve Consistency of Brain Morphology Among Different Scanner Manufacturers in Alzheimer's disease. J Magn Reson Imaging 2024; 59:1327-1340. [PMID: 37403942 DOI: 10.1002/jmri.28887] [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: 01/04/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Brain MRI scanner variability can introduce bias in measurements. Harmonizing scanner variability is crucial. PURPOSE To develop a harmonization method aimed at removing scanner variability, and to evaluate the consistency of results in multicenter studies. STUDY TYPE Retrospective. POPULATION Multicenter data from 170 healthy participants (males/females = 98/72; age = 73.8 ± 7.3) and 170 Alzheimer's disease patients (males/females = 98/72; age = 76.2 ± 8.5) were compared with reference data from another 340 participants. FIELD STRENGTH/SEQUENCE 3-T, magnetization prepared rapid gradient echo and turbo field echo; 1.5-T, inversion recovery prepared fast spoiled gradient echo T1-weighted sequences. ASSESSMENT Gray matter (GM) brain images, obtained through segmentation of T1-weighted images, were utilized to evaluate the performance of the harmonization method using common orthogonal basis extraction (HCOBE) and four other methods (removal of artificial voxel effect by linear regression, RAVEL; Z_score; general linear model, GLM; ComBat). Linear discriminant analysis (LDA) was used to access the effectiveness of different methods in reducing scanner variability. The performance of harmonization methods in preserving GM volumes heterogeneity was evaluated by the similarity of the relationship between GM proportion and age in the reference and multicenter data. Furthermore, the consistency of the harmonized multicenter data with the reference data were evaluated based on classification results (train/test = 7/3) and brain atrophy. STATISTICAL TESTS Two-sample t-tests, area under the curve (AUC), and Dice coefficients were used to analyze the consistency of results from the reference and harmonized multicenter data. A P-value <0.01 was considered statistically significant. RESULTS HCOBE reduced the scanner variability from 0.09 before harmonization to 0.003 (ideal: 0, RAVEL/Z_score/GLM/ComBat = 0.087/0.003/0.006/0.013). GM volumes showed no significant difference (P = 0.52) between the reference and HCOBE-harmonized multicenter data. Consistency evaluation showed that AUC values of 0.95 for both reference and HCOBE-harmonized multicenter data (RAVEL/Z_score/GLM/ComBat = 0.86/0.86/0.84/0.89), and the Dice coefficient increased from 0.73 before harmonization to 0.82 (ideal: 1, RAVEL/Z_score/GLM/ComBat = 0.39/0.64/0.59/0.74). DATA CONCLUSION HCOBE may help to remove scanner variability and could improve the consistency of results in multicenter studies. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
Collapse
Affiliation(s)
- Shilun Zhao
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Wei Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Tingting Pan
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Ge Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Shuang Feng
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Xiwan Zhang
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Hua Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Baoci Shan
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
29
|
Bourque VR, Poulain C, Proulx C, Moreau CA, Joober R, Forgeot d'Arc B, Huguet G, Jacquemont S. Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment. Transl Psychiatry 2024; 14:171. [PMID: 38555309 PMCID: PMC10981737 DOI: 10.1038/s41398-024-02866-3] [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: 10/15/2023] [Revised: 03/05/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.
Collapse
Affiliation(s)
| | - Cécile Poulain
- CHU Sainte-Justine Azrieli Research Center, Université de Montréal, Montreal, QC, Canada
| | - Catherine Proulx
- CHU Sainte-Justine Azrieli Research Center, Université de Montréal, Montreal, QC, Canada
| | - Clara A Moreau
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Ridha Joober
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, QC, Canada
| | - Baudouin Forgeot d'Arc
- CHU Sainte-Justine Azrieli Research Center, Université de Montréal, Montreal, QC, Canada
| | - Guillaume Huguet
- CHU Sainte-Justine Azrieli Research Center, Université de Montréal, Montreal, QC, Canada
| | - Sébastien Jacquemont
- CHU Sainte-Justine Azrieli Research Center, Université de Montréal, Montreal, QC, Canada.
| |
Collapse
|
30
|
Gottschewsky N, Kraft D, Kaufmann T. Menarche, pubertal timing and the brain: female-specific patterns of brain maturation beyond age-related development. Biol Sex Differ 2024; 15:25. [PMID: 38532493 DOI: 10.1186/s13293-024-00604-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/18/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce. METHODS We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status. RESULTS The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities-not the brain age gaps-were associated with age at menarche. CONCLUSIONS This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.
Collapse
Affiliation(s)
- Nina Gottschewsky
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany.
- Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Dominik Kraft
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany.
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany.
| |
Collapse
|
31
|
Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The genetic architecture of multimodal human brain age. Nat Commun 2024; 15:2604. [PMID: 38521789 PMCID: PMC10960798 DOI: 10.1038/s41467-024-46796-6] [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: 05/13/2023] [Accepted: 03/06/2024] [Indexed: 03/25/2024] Open
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
32
|
Chen CL, Cheng SY, Montaser-Kouhsari L, Wu WC, Hsu YC, Tai CH, Tseng WYI, Kuo MC, Wu RM. Advanced brain aging in Parkinson's disease with cognitive impairment. NPJ Parkinsons Dis 2024; 10:62. [PMID: 38493188 PMCID: PMC10944471 DOI: 10.1038/s41531-024-00673-7] [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: 08/15/2023] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.
Collapse
Affiliation(s)
- Chang-Le Chen
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shao-Ying Cheng
- Department of Neurology, National Taiwan University Hospital Bei-Hu Branch, Taipei, Taiwan
| | | | - Wen-Chao Wu
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | | | - Chun-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
- Acroviz Inc, Taipei, Taiwan.
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan.
| | - Ming-Che Kuo
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
- Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan.
- Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan.
| | - Ruey-Meei Wu
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| |
Collapse
|
33
|
Petrican R, Fornito A, Boyland E. Lifestyle Factors Counteract the Neurodevelopmental Impact of Genetic Risk for Accelerated Brain Aging in Adolescence. Biol Psychiatry 2024; 95:453-464. [PMID: 37393046 DOI: 10.1016/j.biopsych.2023.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND The transition from childhood to adolescence is characterized by enhanced neural plasticity and a consequent susceptibility to both beneficial and adverse aspects of one's milieu. METHODS To understand the implications of the interplay between protective and risk-enhancing factors, we analyzed longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (n = 834; 394 female). We probed the maturational correlates of positive lifestyle variables (friendships, parental warmth, school engagement, physical exercise, healthy nutrition) and genetic vulnerability to neuropsychiatric disorders (major depressive disorder, Alzheimer's disease, anxiety disorders, bipolar disorder, schizophrenia) and sought to further elucidate their implications for psychological well-being. RESULTS Genetic risk factors and lifestyle buffers showed divergent relationships with later attentional and interpersonal problems. These effects were mediated by distinguishable functional neurodevelopmental deviations spanning the limbic, default mode, visual, and control systems. More specifically, greater genetic vulnerability was associated with alterations in the normative maturation of areas rich in dopamine (D2), glutamate, and serotonin receptors and of areas with stronger expression of astrocytic and microglial genes, a molecular signature implicated in the brain disorders discussed here. Greater availability of lifestyle buffers predicted deviations in the normative functional development of higher density GABAergic (gamma-aminobutyric acidergic) receptor regions. The two profiles of neurodevelopmental alterations showed complementary roles in protection against psychopathology, which varied with environmental stress levels. CONCLUSIONS Our results underscore the importance of educational involvement and healthy nutrition in attenuating the neurodevelopmental sequelae of genetic risk factors. They also underscore the importance of characterizing early-life biomarkers associated with adult-onset pathologies.
Collapse
Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Liverpool, United Kingdom.
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Emma Boyland
- Institute of Population Health, Department of Psychology, University of Liverpool, Liverpool, United Kingdom
| |
Collapse
|
34
|
Bellucci G, Buscarinu MC, Reniè R, Rinaldi V, Bigi R, Mechelli R, Romano S, Salvetti M, Ristori G. Disentangling multiple sclerosis phenotypes through Mendelian disorders: A network approach. Mult Scler 2024; 30:325-335. [PMID: 38333907 DOI: 10.1177/13524585241227119] [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: 02/10/2024]
Abstract
BACKGROUND The increasing knowledge about multiple sclerosis (MS) pathophysiology has reinforced the need for an improved description of disease phenotypes, connected to disease biology. Growing evidence indicates that complex diseases constitute phenotypical and genetic continuums with "simple," monogenic disorders, suggesting shared pathomechanisms. OBJECTIVES The objective of this study was to depict a novel MS phenotypical framework leveraging shared physiopathology with Mendelian diseases and to identify phenotype-specific candidate drugs. METHODS We performed an enrichment testing of MS-associated variants with Mendelian disorders genes. We defined a "MS-Mendelian network," further analyzed to define enriched phenotypic subnetworks and biological processes. Finally, a network-based drug screening was implemented. RESULTS Starting from 617 MS-associated loci, we showed a significant enrichment of monogenic diseases (p < 0.001). We defined an MS-Mendelian molecular network based on 331 genes and 486 related disorders, enriched in four phenotypic classes: neurologic, immunologic, metabolic, and visual. We prioritized a total of 503 drugs, of which 27 molecules active in 3/4 phenotypical subnetworks and 140 in subnetwork pairs. CONCLUSION The genetic architecture of MS contains the seeds of pathobiological multiplicities shared with immune, neurologic, metabolic and visual monogenic disorders. This result may inform future classifications of MS endophenotypes and support the development of new therapies in both MS and rare diseases.
Collapse
Affiliation(s)
- Gianmarco Bellucci
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Maria Chiara Buscarinu
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy Neuroimmunology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
| | - Roberta Reniè
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Virginia Rinaldi
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Rachele Bigi
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Rosella Mechelli
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Pisana, Rome, Italy San Raffaele Roma Open University, Rome, Italy
| | - Silvia Romano
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Marco Salvetti
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Giovanni Ristori
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy Neuroimmunology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
| |
Collapse
|
35
|
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
|
36
|
Cortes-Flores H, Torrandell-Haro G, Brinton RD. Association between CNS-active drugs and risk of Alzheimer's and age-related neurodegenerative diseases. Front Psychiatry 2024; 15:1358568. [PMID: 38487578 PMCID: PMC10937406 DOI: 10.3389/fpsyt.2024.1358568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/08/2024] [Indexed: 03/17/2024] Open
Abstract
Objective As neuropsychiatric conditions can increase the risk of age-related neurodegenerative diseases (NDDs), the impact of CNS-active drugs on the risk of developing Alzheimer's Disease (AD), non-AD dementia, Multiple Sclerosis (MS), Parkinson's Disease (PD) and Amyotrophic Lateral Sclerosis (ALS) was investigated. Research design and methods A retrospective cohort analysis of a medical claims dataset over a 10 year span was conducted in patients aged 60 years or older. Participants were propensity score matched for comorbidity severity and demographic parameters. Relative risk (RR) ratios and 95% confidence intervals (CI) were determined for age-related NDDs. Cumulative hazard ratios and treatment duration were determined to assess the association between CNS-active drugs and NDDs at different ages and treatment duration intervals. Results In 309,128 patients who met inclusion criteria, exposure to CNS-active drugs was associated with a decreased risk of AD (0.86% vs 1.73%, RR: 0.50; 95% CI: 0.47-0.53; p <.0001) and all NDDs (3.13% vs 5.76%, RR: 0.54; 95% CI: 0.53-0.56; p <.0001). Analysis of impact of drug class on risk of AD indicated that antidepressant, sedative, anticonvulsant, and stimulant medications were associated with significantly reduced risk of AD whereas atypical antipsychotics were associated with increased AD risk. The greatest risk reduction for AD and NDDs occurred in patients aged 70 years or older with a protective effect only in patients with long-term therapy (>3 years). Furthermore, responders to these therapeutics were characterized by diagnosed obesity and higher prescriptions of anti-inflammatory drugs and menopausal hormonal therapy, compared to patients with a diagnosis of AD (non-responders). Addition of a second CNS-active drug was associated with greater reduction in AD risk compared to monotherapy, with the combination of a Z-drug and an SNRI associated with greatest AD risk reduction. Conclusion Collectively, these findings indicate that CNS-active drugs were associated with reduced risk of developing AD and other age-related NDDs. The exception was atypical antipsychotics, which increased risk. Potential use of combination therapy with atypical antipsychotics could mitigate the risk conferred by these drugs. Evidence from these analyses advance precision prevention strategies to reduce the risk of age-related NDDs in persons with neuropsychiatric disorders.
Collapse
Affiliation(s)
- Helena Cortes-Flores
- Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, United States
- Department of Pharmacology, University of Arizona College of Medicine, Tucson, AZ, United States
| | - Georgina Torrandell-Haro
- Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, United States
- Department of Pharmacology, University of Arizona College of Medicine, Tucson, AZ, United States
| | - Roberta Diaz Brinton
- Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, United States
- Department of Pharmacology, University of Arizona College of Medicine, Tucson, AZ, United States
- Department of Neurology, University of Arizona College of Medicine, Tucson, AZ, United States
| |
Collapse
|
37
|
Kalc P, Dahnke R, Hoffstaedter F, Gaser C. BrainAGE: Revisited and reframed machine learning workflow. Hum Brain Mapp 2024; 45:e26632. [PMID: 38379519 PMCID: PMC10879910 DOI: 10.1002/hbm.26632] [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: 08/18/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.
Collapse
Affiliation(s)
- Polona Kalc
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
| | - Robert Dahnke
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
| | - Felix Hoffstaedter
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM‐7)JülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- German Center for Mental Health (DZPG)Jena‐Halle‐MagdeburgGermany
| | | |
Collapse
|
38
|
Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
Collapse
Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
| |
Collapse
|
39
|
Korbmacher M, van der Meer D, Beck D, de Lange AMG, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain asymmetries from mid- to late life and hemispheric brain age. Nat Commun 2024; 15:956. [PMID: 38302499 PMCID: PMC10834516 DOI: 10.1038/s41467-024-45282-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024] Open
Abstract
The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
Collapse
Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway.
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
| |
Collapse
|
40
|
Chen YC, Tiego J, Segal A, Chopra S, Holmes A, Suo C, Pang JC, Fornito A, Aquino KM. A multiscale characterization of cortical shape asymmetries in early psychosis. Brain Commun 2024; 6:fcae015. [PMID: 38347944 PMCID: PMC10859637 DOI: 10.1093/braincomms/fcae015] [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: 10/12/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 02/15/2024] Open
Abstract
Psychosis has often been linked to abnormal cortical asymmetry, but prior results have been inconsistent. Here, we applied a novel spectral shape analysis to characterize cortical shape asymmetries in patients with early psychosis across different spatial scales. We used the Human Connectome Project for Early Psychosis dataset (aged 16-35), comprising 56 healthy controls (37 males, 19 females) and 112 patients with early psychosis (68 males, 44 females). We quantified shape variations of each hemisphere over different spatial frequencies and applied a general linear model to compare differences between healthy controls and patients with early psychosis. We further used canonical correlation analysis to examine associations between shape asymmetries and clinical symptoms. Cortical shape asymmetries, spanning wavelengths from about 22 to 75 mm, were significantly different between healthy controls and patients with early psychosis (Cohen's d = 0.28-0.51), with patients showing greater asymmetry in cortical shape than controls. A single canonical mode linked the asymmetry measures to symptoms (canonical correlation analysis r = 0.45), such that higher cortical asymmetry was correlated with more severe excitement symptoms and less severe emotional distress. Significant group differences in the asymmetries of traditional morphological measures of cortical thickness, surface area, and gyrification, at either global or regional levels, were not identified. Cortical shape asymmetries are more sensitive than other morphological asymmetries in capturing abnormalities in patients with early psychosis. These abnormalities are expressed at coarse spatial scales and are correlated with specific symptom domains.
Collapse
Affiliation(s)
- Yu-Chi Chen
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne 3800, Australia
- Brain and Mind Centre, University of Sydney, Sydney 2050, Australia
- Brain Dynamic Centre, Westmead Institute for Medical Research, University of Sydney, Sydney 2145, Australia
| | - Jeggan Tiego
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Ashlea Segal
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Alexander Holmes
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Chao Suo
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- BrainPark, School of Psychological Sciences, Monash University, Melbourne 3800, Australia
| | - James C Pang
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Alex Fornito
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
| | - Kevin M Aquino
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- Monash Biomedical Imaging, Monash University, Melbourne 3800, Australia
- School of Physics, University of Sydney, Sydney 2050, Australia
- Center of Excellence for Integrative Brain Function, University of Sydney, Sydney 2050, Australia
- BrainKey Inc, San Francisco, CA 94103, USA
| |
Collapse
|
41
|
Gao C, Kim ME, Lee HH, Yang Q, Khairi NM, Kanakaraj P, Newlin NR, Archer DB, Jefferson AL, Taylor WD, Boyd BD, Beason-Held LL, Resnick SM, Huo Y, Van Schaik KD, Schilling KG, Moyer D, Išgum I, Landman BA. Predicting Age from White Matter Diffusivity with Residual Learning. ARXIV 2024:arXiv:2311.03500v2. [PMID: 37986731 PMCID: PMC10659451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
Collapse
Affiliation(s)
- Chenyu Gao
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Michael E Kim
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ho Hin Lee
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Qi Yang
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Nazirah Mohd Khairi
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | | | - Nancy R Newlin
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Warren D Taylor
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Brian D Boyd
- Vanderbilt Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Yuankai Huo
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Katherine D Van Schaik
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Kurt G Schilling
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Daniel Moyer
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ivana Išgum
- Dept. of Biomedical Engineering and Physics, Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bennett A Landman
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| |
Collapse
|
42
|
Soumya Kumari LK, Sundarrajan R. A review on brain age prediction models. Brain Res 2024; 1823:148668. [PMID: 37951563 DOI: 10.1016/j.brainres.2023.148668] [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] [Received: 06/16/2023] [Revised: 10/23/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.
Collapse
Affiliation(s)
- L K Soumya Kumari
- Computer Science Engineering, Mohandas College of Engineering and Technology, Anad, India.
| | - R Sundarrajan
- Information Technology, School of Computing, Kalasalingam Academy of Research and Education, India.
| |
Collapse
|
43
|
Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [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: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
Collapse
Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
| |
Collapse
|
44
|
Shah J, Siddiquee MMR, Su Y, Wu T, Li B. Ordinal Classification with Distance Regularization for Robust Brain Age Prediction. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION. IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION 2024; 2024:7867-7876. [PMID: 38606366 PMCID: PMC11008505 DOI: 10.1109/wacv57701.2024.00770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects. This weakens the reliability of predicted brain age as a valid biomarker for downstream clinical applications. Here, we reformulate the brain age prediction task from regression to classification to address the issue of systematic bias. Recognizing the importance of preserving ordinal information from ages to understand aging trajectory and monitor aging longitudinally, we propose a novel ORdinal Distance Encoded Regularization (ORDER) loss that incorporates the order of age labels, enhancing the model's ability to capture age-related patterns. Extensive experiments and ablation studies demonstrate that this framework reduces systematic bias, outperforms state-of-art methods by statistically significant margins, and can better capture subtle differences between clinical groups in an independent AD dataset. Our implementation is publicly available at https://github.com/jaygshah/Robust-Brain-Age-Prediction.
Collapse
Affiliation(s)
- Jay Shah
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | | | - Yi Su
- ASU-Mayo Center for Innovative Imaging
- Banner Alzheimer's Institute
| | - Teresa Wu
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| | - Baoxin Li
- Arizona State University
- ASU-Mayo Center for Innovative Imaging
| |
Collapse
|
45
|
Kozhemiako N, Jiang C, Sun Y, Guo Z, Chapman S, Gai G, Wang Z, Zhou L, Li S, Law RG, Wang LA, Mylonas D, Shen L, Murphy M, Qin S, Zhu W, Zhou Z, Stickgold R, Huang H, Tan S, Manoach DS, Wang J, Hall MH, Pan JQ, Purcell SM. A spectrum of altered non-rapid eye movement sleep in schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.28.573548. [PMID: 38234726 PMCID: PMC10793442 DOI: 10.1101/2023.12.28.573548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Background Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. Results We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. Conclusion We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.
Collapse
Affiliation(s)
- Nataliia Kozhemiako
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School; Boston, USA
| | - Chenguang Jiang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Yifan Sun
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Zhenglin Guo
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Sinéad Chapman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Guanchen Gai
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Zhe Wang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Lin Zhou
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Shen Li
- Department of Psychiatry, McLean Hospital, Harvard Medical School; Boston, USA
| | - Robert G. Law
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School; Boston, USA
| | - Lei A. Wang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Dimitrios Mylonas
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School; Boston, USA
| | - Lu Shen
- Bio-X Institutes, Shanghai Jiao Tong University; Shanghai China
| | - Michael Murphy
- Department of Psychiatry, McLean Hospital, Harvard Medical School; Boston, USA
| | - Shengying Qin
- Bio-X Institutes, Shanghai Jiao Tong University; Shanghai China
| | - Wei Zhu
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Zhenhe Zhou
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Robert Stickgold
- Beth Israel Deaconess Medical Center; Boston, USA
- Department of Psychiatry, Harvard Medical School; Boston, USA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
- ATGU, MGH, Harvard Medical School; Boston, USA
| | - Shuping Tan
- Huilong Guan Hospital, Beijing University; Beijing China
| | - Dara S. Manoach
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School; Boston, USA
| | - Jun Wang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University; Wuxi, China
| | - Mei-Hua Hall
- Department of Psychiatry, McLean Hospital, Harvard Medical School; Boston, USA
| | - Jen Q. Pan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Boston, USA
| | - Shaun M. Purcell
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School; Boston, USA
- Department of Psychiatry, Harvard Medical School; Boston, USA
| |
Collapse
|
46
|
Sun X, Sun J, Lu X, Dong Q, Zhang L, Wang W, Liu J, Ma Q, Wang X, Wei D, Chen Y, Liu B, Huang CC, Zheng Y, Wu Y, Chen T, Cheng Y, Xu X, Gong Q, Si T, Qiu S, Lin CP, Cheng J, Tang Y, Wang F, Qiu J, Xie P, Li L, He Y, Xia M. Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome. Biol Psychiatry 2023; 94:936-947. [PMID: 37295543 DOI: 10.1016/j.biopsych.2023.05.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking. METHODS Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity. RESULTS Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes. CONCLUSIONS These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.
Collapse
Affiliation(s)
- Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; School of Systems Science, Beijing Normal University, Beijing, China
| | - Jinrong Sun
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou Mental Health Centre, Yangzhou, China
| | - Xiaowen Lu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated Wuhan Mental Health Center, Huazhong University of Science and Technology, Wuhan, China
| | - Qiangli Dong
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, China
| | - Liang Zhang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Mental Health Education and Counseling Center, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenxu Wang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qing Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bangshan Liu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Taolin Chen
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingjiang Li
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| |
Collapse
|
47
|
Moon HS, Mahzarnia A, Stout J, Anderson RJ, Badea CT, Badea A. Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.13.571574. [PMID: 38168445 PMCID: PMC10760088 DOI: 10.1101/2023.12.13.571574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.
Collapse
Affiliation(s)
- Hae Sol Moon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Ali Mahzarnia
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Jacques Stout
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Robert J Anderson
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Cristian T. Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|
48
|
Dempsey DA, Deardorff R, Wu YC, Yu M, Apostolova LG, Brosch J, Clark DG, Farlow MR, Gao S, Wang S, Saykin AJ, Risacher SL. BrainAGE Estimation: Influence of Field Strength, Voxel Size, Race, and Ethnicity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.05.23299222. [PMID: 38106123 PMCID: PMC10723496 DOI: 10.1101/2023.12.05.23299222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across- MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used to correct for potential scanner effects. The brain age method was stable within field strength, but less stable across different field strengths. The method was stable across voxel sizes. There was a significant difference in BAG between races, but not ethnicities. Correction procedures are suggested to eliminate variation across scanner field strength while maintaining accurate brain age estimation. Further studies are warranted to determine the factors contributing to racial differences in BAG.
Collapse
Affiliation(s)
- Desarae A. Dempsey
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Rachael Deardorff
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Meichen Yu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Liana G. Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Jared Brosch
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - David G. Clark
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Martin R. Farlow
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sophia Wang
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | |
Collapse
|
49
|
Qiu W, Chen H, Kaeberlein M, Lee SI. ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age. THE LANCET. HEALTHY LONGEVITY 2023; 4:e711-e723. [PMID: 37944549 DOI: 10.1016/s2666-7568(23)00189-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/10/2023] [Accepted: 08/30/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations. METHODS To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses. FINDINGS Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms. INTERPRETATION ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes. FUNDING National Science Foundation and National Institutes of Health.
Collapse
Affiliation(s)
- Wei Qiu
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA
| | - Hugh Chen
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington, Washington, DC, USA
| | - Su-In Lee
- Paul G Allen School of Computer Science and Engineering, University of Washington, Washington, DC, USA.
| |
Collapse
|
50
|
Dörfel RP, Arenas‐Gomez JM, Fisher PM, Ganz M, Knudsen GM, Svensson JE, Plavén‐Sigray P. Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages. Hum Brain Mapp 2023; 44:6139-6148. [PMID: 37843020 PMCID: PMC10619370 DOI: 10.1002/hbm.26502] [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: 05/04/2023] [Revised: 08/14/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023] Open
Abstract
Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.
Collapse
Affiliation(s)
- Ruben P. Dörfel
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Joan M. Arenas‐Gomez
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
| | - Patrick M. Fisher
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Department of Drug Design and PharmacologyUniversity of CopenhagenCopenhagenDenmark
| | - Melanie Ganz
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark
| | - Gitte M. Knudsen
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Jonas E. Svensson
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Pontus Plavén‐Sigray
- Neurobiology Research UnitCopenhagen University Hospital, RigshospitaletCopenhagenDenmark
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| |
Collapse
|