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Fletcher E, Farias S, DeCarli C, Gavett B, Widaman K, De Leon F, Mungas D. Toward a statistical validation of brain signatures as robust measures of behavioral substrates. Hum Brain Mapp 2023; 44:3094-3111. [PMID: 36939069 PMCID: PMC10171525 DOI: 10.1002/hbm.26265] [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: 05/15/2022] [Revised: 02/10/2023] [Accepted: 02/21/2023] [Indexed: 03/21/2023] Open
Abstract
The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.
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Affiliation(s)
- Evan Fletcher
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Sarah Farias
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Brandon Gavett
- School of Psychological ScienceUniversity of Western AustraliaPerthAustralia
| | - Keith Widaman
- School of EducationUniversity of California, RiversideRiversideCaliforniaUSA
| | - Fransia De Leon
- School of MedicineUniversity of California, DavisDavisCaliforniaUSA
| | - Dan Mungas
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
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2
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Fletcher E, Gavett B, Crane P, Soldan A, Hohman T, Farias S, Widaman K, Groot C, Renteria MA, Zahodne L, DeCarli C, Mungas D. A robust brain signature region approach for episodic memory performance in older adults. Brain 2021; 144:1089-1102. [PMID: 33895818 PMCID: PMC8105039 DOI: 10.1093/brain/awab007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 10/11/2020] [Accepted: 10/30/2020] [Indexed: 01/26/2023] Open
Abstract
The brain signature concept aims to characterize brain regions most strongly associated with an outcome of interest. Brain signatures derive their power from data-driven searches that select features based solely on performance metrics of prediction or classification. This approach has important potential to delineate biologically relevant brain substrates for prediction or classification of future trajectories. Recent work has used exploratory voxel-wise or atlas-based searches, with some using machine learning techniques to define salient features. These have shown undoubted usefulness, but two issues remain. The preponderance of recent work has been aimed at categorical rather than continuous outcomes, and it is rare for non-atlas reliant voxel-based signatures to be reported that would be useful for modelling and hypothesis testing. We describe a cross-validated signature region model for structural brain components associated with baseline and longitudinal episodic memory across cognitively heterogeneous populations including normal, mild impairment and dementia. We used three non-overlapping cohorts of older participants: from the UC Davis Aging and Diversity cohort (n = 255; mean age 75.3 ± 7.1 years; 128 cognitively normal, 97 mild cognitive impairment, 30 demented and seven unclassified); from Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 (n = 379; mean age 75.1 ± 7.2; 82 cognitively normal, 176 mild cognitive impairment, 121 Alzheimer's dementia); and from ADNI2/GO (n = 680; mean age 72.5 ± 7.1; 220 cognitively normal, 381 mild cognitive impairment and 79 Alzheimer's dementia). We used voxel-wise regression analysis, correcting for multiple comparisons, to generate an array of regional masks corresponding to different association strength levels of cortical grey matter with baseline memory and brain atrophy with memory change. Cognitive measures were episodic memory using Spanish and English Neuropsychological Assessment Scales instruments for UC Davis and ADNI-Mem for ADNI 1 and ADNI2/GO. Performance metric was the adjusted R2 coefficient of determination of each model explaining outcomes in two cohorts other than where it was computed. We compared within-cohort performances of signature models against each other and against other recent signature models of episodic memory. Findings were: (i) two independently generated signature region of interest models performed similarly in a third separate cohort; (ii) a signature region of interest generated in one imaging cohort replicated its performance level when explaining cognitive outcomes in each of other, separate cohorts; and (iii) this approach better explained baseline and longitudinal memory than other recent theory-driven and data-driven models. This suggests our approach can generate signatures that may be easily and robustly applied for modelling and hypothesis testing in mixed cognition cohorts.
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Affiliation(s)
- Evan Fletcher
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Brandon Gavett
- School of Psychological Science, University of Western Australia, Perth, Australia
| | - Paul Crane
- University of Washington, Seattle, WA, USA
| | - Anja Soldan
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Timothy Hohman
- Department of Neurology, Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah Farias
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Keith Widaman
- Graduate School of Education, UC Riverside, Riverside, CA, USA
| | - Colin Groot
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Laura Zahodne
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Charles DeCarli
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - Dan Mungas
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
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Mohajer B, Abbasi N, Mohammadi E, Khazaie H, Osorio RS, Rosenzweig I, Eickhoff CR, Zarei M, Tahmasian M, Eickhoff SB. Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing. Hum Brain Mapp 2020; 41:3034-3044. [PMID: 32239749 PMCID: PMC7336142 DOI: 10.1002/hbm.24995] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/29/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's disease (AD) and sleep-disordered breathing (SDB) are prevalent conditions with a rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self-reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HCs). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self-reported history of SDB, and matched across diagnoses for age, sex and presence of the Apolipoprotein E4 allele, from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Gray-matter volume was measured using voxel-wise morphometry and group differences in terms of SDB, cognitive status, and their interaction were assessed. Further, using an age-prediction model fitted on gray-matter data of external datasets, we predicted study participants' age from their structural images. Cognitive decline and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. Brains age was well predicted from the morphological data in HCs and, as expected, elevated in MCI and particularly in AD subjects. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status, nor in SDB-by-cognitive status interaction. Moreover, we found no difference in estimated chronological age gap related to SDB, or by-cognitive status interaction. Contrary to our hypothesis, we were not able to find a general or a diagnostic-dependent association of SDB with either gray-matter volumetric or brain aging.
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Affiliation(s)
- Bahram Mohajer
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
- Non‐Communicable Diseases Research CenterEndocrinology and Metabolism Population Sciences Institute, Tehran University of Medical SciencesTehranIran
| | - Nooshin Abbasi
- McConnell Brain Imaging CentreMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Esmaeil Mohammadi
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
- Non‐Communicable Diseases Research CenterEndocrinology and Metabolism Population Sciences Institute, Tehran University of Medical SciencesTehranIran
| | - Habibolah Khazaie
- Sleep Disorders Research CenterKermanshah University of Medical SciencesKermanshahIran
| | - Ricardo S. Osorio
- Department of Psychiatry, Center for Brain Health, NYU Langone Medical CenterNew YorkNew YorkUSA
- Nathan S. Kline Institute for Psychiatric ResearchNew YorkNew YorkUSA
| | - Ivana Rosenzweig
- Sleep Disorders CentreGuy's and St Thomas' Hospital, GSTT NHSLondonUK
- Sleep and Brain Plasticity Centre, Department of NeuroimagingIOPPN, King's College LondonLondonUK
| | - Claudia R. Eickhoff
- Institute of Neuroscience and Medicine (INM‐1; INM‐7), Research Center JülichJülichGermany
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine UniversityDüsseldorfGermany
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine (INM‐1; INM‐7), Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich‐Heine UniversityDüsseldorfGermany
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Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105348. [PMID: 31995745 DOI: 10.1016/j.cmpb.2020.105348] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. METHODS We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. RESULTS After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. CONCLUSIONS Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
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Affiliation(s)
- Gerard Martí-Juan
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8915961. [PMID: 32549888 PMCID: PMC7256687 DOI: 10.1155/2020/8915961] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 05/06/2020] [Indexed: 12/20/2022]
Abstract
Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N = 9), mild cognitive impairment (N = 19), and subjective cognitive complaints (N = 4) and healthy controls (N = 18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.
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Svenningsson AL, Stomrud E, Insel PS, Mattsson N, Palmqvist S, Hansson O. β-amyloid pathology and hippocampal atrophy are independently associated with memory function in cognitively healthy elderly. Sci Rep 2019; 9:11180. [PMID: 31371787 PMCID: PMC6671981 DOI: 10.1038/s41598-019-47638-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 07/11/2019] [Indexed: 11/25/2022] Open
Abstract
The independent effects of different brain pathologies on age-dependent cognitive decline are unclear. We examined this in 300 cognitively unimpaired elderly individuals from the BioFINDER study. Using cognition as outcome we studied the effects of cerebrospinal fluid biomarkers for amyloid-β (Aβ42/40), neuroinflammation (YKL-40), and neurodegeneration and tau pathology (T-tau and P-tau) as well as MRI measures of white-matter lesions, hippocampal volume (HV), and regional cortical thickness. We found that Aβ positivity and HV were independently associated with memory. Results differed depending on age, with memory being associated with HV (but not Aβ) in older participants (73.3–88.4 years), and with Aβ (but not HV) in relatively younger participants (65.2–73.2 years). This indicates that Aβ and atrophy are independent contributors to memory variability in cognitively healthy elderly and that Aβ mainly affects memory in younger elderly individuals. With advancing age, the effect of brain atrophy overshadows the effect of Aβ on memory function.
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Affiliation(s)
- Anna L Svenningsson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund/Malmö, Sweden. .,Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund/Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Philip S Insel
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund/Malmö, Sweden
| | - Niklas Mattsson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund/Malmö, Sweden.,Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund/Malmö, Sweden.,Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund/Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
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7
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ten Kate M, Ingala S, Schwarz AJ, Fox NC, Chételat G, van Berckel BNM, Ewers M, Foley C, Gispert JD, Hill D, Irizarry MC, Lammertsma AA, Molinuevo JL, Ritchie C, Scheltens P, Schmidt ME, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer's dementia: neuroimaging contributions. Alzheimers Res Ther 2018; 10:112. [PMID: 30376881 PMCID: PMC6208183 DOI: 10.1186/s13195-018-0438-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/10/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.
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Affiliation(s)
- Mara ten Kate
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Adam J. Schwarz
- Takeda Pharmaceuticals Comparny, Cambridge, MA USA
- Eli Lilly and Company, Indianapolis, Indiana USA
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Gaël Chételat
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR-S U1237, Université de Caen-Normandie, GIP Cyceron, Caen, France
| | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | | | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Craig Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Adam Waldman
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sven Haller
- Affidea Centre de Diagnostic Radiologique de Carouge, Geneva, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
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Ranlund S, Rosa MJ, de Jong S, Cole JH, Kyriakopoulos M, Fu CHY, Mehta MA, Dima D. Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition. Neuroimage Clin 2018; 20:1026-1036. [PMID: 30340201 PMCID: PMC6197704 DOI: 10.1016/j.nicl.2018.10.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 10/04/2018] [Accepted: 10/08/2018] [Indexed: 12/24/2022]
Abstract
Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.
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Affiliation(s)
- Siri Ranlund
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Maria Joao Rosa
- Department of Computer Science, University College London, London, UK
| | - Simone de Jong
- NIHR BRC for Mental Health, Institute of Psychiatry, Psychology and Neuroscience, King's College London and SLaM NHS Trust, London, UK; MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Computational, Cognitive & Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
| | - Marinos Kyriakopoulos
- National and Specialist Acorn Lodge Inpatient Children Unit, South London and Maudsley NHS Foundation Trust, London, UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Cynthia H Y Fu
- School of Psychology, University of East London, London, UK; Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mitul A Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Danai Dima
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK.
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9
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Mansur RB, Zugman A, Ahmed J, Cha DS, Subramaniapillai M, Lee Y, Lovshin J, Lee JG, Lee JH, Drobinin V, Newport J, Brietzke E, Reininghaus EZ, Sim K, Vinberg M, Rasgon N, Hajek T, McIntyre RS. Treatment with a GLP-1R agonist over four weeks promotes weight loss-moderated changes in frontal-striatal brain structures in individuals with mood disorders. Eur Neuropsychopharmacol 2017; 27:1153-1162. [PMID: 28867303 DOI: 10.1016/j.euroneuro.2017.08.433] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 08/09/2017] [Accepted: 08/21/2017] [Indexed: 10/18/2022]
Abstract
Cognitive deficits are a core feature across psychiatric disorders. Emerging evidence indicates that metabolic pathways are highly relevant for the substrates and phenomenology of the cognitive domain. Herein, we aimed to determine the effects of liraglutide, a GLP-1R agonist, on brain structural/volumetric parameters in adults with a mood disorder. This is the secondary analysis of a 4-week, pilot, proof-of-concept, open-label study. Participants (N=19) exhibiting impairments in executive function with either major depressive disorder (MDD) or bipolar disorder (BD) were recruited. Liraglutide 1.8mg/day was added as an adjunct to existing pharmacotherapy. Structural magnetic resonance imaging (MRI) scanning was obtained at baseline and endpoint. Results showed that at endpoint there was significant weight loss (mean: 3.15%; p<0.001). Changes in frontal and striatal volumes were significantly correlated with changes in body mass index (BMI), indicating the weight loss was associated with volume increase in most regions (e.g. r=-0.561, p=0.042 in the left superior frontal area). After adjusting for intracranial volume, age, gender, and BMI, we observed significant changes from baseline to endpoint in multiple regions (e.g. RR: 1.011, p=0.049 in the left rostral middle frontal area). Changes in regional volumes were associated with improvement in executive function (e.g. r=0.698, p=0.003 for the right superior frontal area). Adjunctive liraglutide results in clinically significant weight loss, with corresponding improvement in cognitive function; changes in cognitive function were partially moderated by changes in brain morphometry, underscoring the interrelationship between weight and brain structure/function.
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Affiliation(s)
- Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada; Research Group in Molecular and Behavioral Neuroscience of Bipolar Disorder, Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.
| | - Andre Zugman
- Interdiscipinary Laboratory of Clinical Neurosciences (LINC), Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Juhie Ahmed
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Danielle S Cha
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Mehala Subramaniapillai
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Yena Lee
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Julie Lovshin
- Division of Endocrinology, Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - Jung G Lee
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada; Paik Institute for Clinical Research, Inje University, Busan, Republic of Korea
| | - Jae-Hon Lee
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada; Department of Psychiatry, Samsung Seoul Hospital, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea
| | | | - Jason Newport
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Elisa Brietzke
- Research Group in Molecular and Behavioral Neuroscience of Bipolar Disorder, Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | | | - Kang Sim
- Research Division, Institute of Mental Health, Singapore
| | - Maj Vinberg
- Psychiatric Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Natalie Rasgon
- Department of Psychiatry, Stanford University, Palo Alto, CA, United states
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
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