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Deng Z, Wang H, Yang X, Huang K, Li Y, Hu N, Zhou L. Evaluation of imaging indicators in differentiating idiopathic normal pressure hydrocephalus from Alzheimer's disease. Clin Neurol Neurosurg 2024; 242:108362. [PMID: 38823198 DOI: 10.1016/j.clineuro.2024.108362] [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: 04/29/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND There are currently many imaging indicators for idiopathic normal pressure hydrocephalus (iNPH). However, their diagnostic performance has not been well compared, especially in differentiating iNPH from Alzheimer's disease (AD). This study aimed to evaluate the diagnostic performance of these imaging indicators in differentiating iNPH from AD. METHODS We retrospectively collected patients with iNPH from the West China Hospital between June 2016 and December 2023. Age-sex-matched patients with AD and healthy controls (HCs) are included as controls (ChiCTR2300070078, March 2023). Twelve imaging indicators were evaluated on MRI, including disproportionately enlarged subarachnoid space hydrocephalus (DESH), Evans' index (EI), callosal angle, z-EI, temporal horn, dilated Sylvian fissure, focal sulcal dilation, tight high convexity, deep white matter hyperintensities, periventricular hyperintensities, DESH scale, and Simplified Radscale. We analyzed the receiver operating characteristic curves and calculated the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy. RESULTS A total of 46 patients with iNPH (mean age: 73.1 ± 6.5; 35 males), 46 patients with AD (mean age: 73.0 ± 6.6; 35 males), and 46 HCs (mean age: 73.0 ± 5.9; 35 males) were included. The largest area under the receiver operating characteristic curve (AUC) was found in EI (0.93; 95 % CI: 0.89-0.98) and z-EI (0.93; 95 % CI: 0.87-0.98). DESH scale ≥ 6 had the highest specificity (93 %, 43/46). CONCLUSION EI and z-EI had the best diagnostic performance in differentiating iNPH from AD. The DESH scale could assist in diagnosing iNPH due to its high specificity.
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Affiliation(s)
- Ziang Deng
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Haoxiang Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xiyue Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Keru Huang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuanyou Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Liangxue Zhou
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China; Department of Neurosurgery, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China; Department of Neurosurgery, The Fifth people's Hospital of Ningxia, Shizuishan, China.
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2
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Jansen MG, Zwiers MP, Marques JP, Chan KS, Amelink JS, Altgassen M, Oosterman JM, Norris DG. The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale. PLoS One 2024; 19:e0306006. [PMID: 38905233 PMCID: PMC11192316 DOI: 10.1371/journal.pone.0306006] [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: 01/16/2024] [Accepted: 06/07/2024] [Indexed: 06/23/2024] Open
Abstract
To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
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Affiliation(s)
- Michelle G. Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Marcel P. Zwiers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jose P. Marques
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kwok-Shing Chan
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jitse S. Amelink
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Radboud University, Nijmegen, the Netherlands
| | - Mareike Altgassen
- Department of Psychology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Joukje M. Oosterman
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - David G. Norris
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Snytte J, Setton R, Mwilambwe-Tshilobo L, Natasha Rajah M, Sheldon S, Turner GR, Spreng RN. Structure-Function Interactions in the Hippocampus and Prefrontal Cortex Are Associated with Episodic Memory in Healthy Aging. eNeuro 2024; 11:ENEURO.0418-23.2023. [PMID: 38479810 PMCID: PMC10972739 DOI: 10.1523/eneuro.0418-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/11/2023] [Accepted: 12/15/2023] [Indexed: 04/01/2024] Open
Abstract
Aging comes with declines in episodic memory. Memory decline is accompanied by structural and functional alterations within key brain regions, including the hippocampus and lateral prefrontal cortex, as well as their affiliated default and frontoparietal control networks. Most studies have examined how structural or functional differences relate to memory independently. Here we implemented a multimodal, multivariate approach to investigate how interactions between individual differences in structural integrity and functional connectivity relate to episodic memory performance in healthy aging. In a sample of younger (N = 111; mean age, 22.11 years) and older (N = 78; mean age, 67.29 years) adults, we analyzed structural MRI and multiecho resting-state fMRI data. Participants completed measures of list recall (free recall of words from a list), associative memory (cued recall of paired words), and source memory (cued recall of the trial type, or the sensory modality in which a word was presented). The findings revealed that greater structural integrity of the posterior hippocampus and middle frontal gyrus were linked with a pattern of increased within-network connectivity, which together were related to better associative and source memory in older adulthood. Critically, older adults displayed better memory performance in the context of decreased hippocampal volumes when structural differences were accompanied by functional reorganization. This functional reorganization was characterized by a pruning of connections between the hippocampus and the limbic and frontoparietal control networks. Our work provides insight into the neural mechanisms that underlie age-related compensation, revealing that the functional architecture associated with better memory performance in healthy aging is tied to the structural integrity of the hippocampus and prefrontal cortex.
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Affiliation(s)
- Jamie Snytte
- Department of Psychology, McGill University, Montreal, Quebec H3A 1G1, Canada
| | - Roni Setton
- Department of Psychology, Harvard University, Cambridge, Massachusetts 02138
| | - Laetitia Mwilambwe-Tshilobo
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Psychology, Princeton University, Princeton, New Jersey 08540
| | - M Natasha Rajah
- Department of Psychology, McGill University, Montreal, Quebec H3A 1G1, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Signy Sheldon
- Department of Psychology, McGill University, Montreal, Quebec H3A 1G1, Canada
| | - Gary R Turner
- Department of Psychology, York University, Toronto, Ontario M3J 1P3, Canada
| | - R Nathan Spreng
- Department of Psychology, McGill University, Montreal, Quebec H3A 1G1, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 2B4, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, Quebec H3A 2B4, Canada
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4
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Dishner KA, McRae-Posani B, Bhowmik A, Jochelson MS, Holodny A, Pinker K, Eskreis-Winkler S, Stember JN. A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research. J Magn Reson Imaging 2024; 59:450-480. [PMID: 37888298 PMCID: PMC10873125 DOI: 10.1002/jmri.29101] [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: 07/31/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Katharine A. Dishner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- SUNY Downstate College of Medicine, Brooklyn, NY 11203
| | - Bala McRae-Posani
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Weill Cornell Medicine, New York, NY 10065
| | - Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Maxine S. Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrei Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY 10065
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | - Joseph N. Stember
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
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5
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Baracchini G, Zhou Y, da Silva Castanheira J, Hansen JY, Rieck J, Turner GR, Grady CL, Misic B, Nomi J, Uddin LQ, Spreng RN. The biological role of local and global fMRI BOLD signal variability in human brain organization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.22.563476. [PMID: 37961684 PMCID: PMC10634715 DOI: 10.1101/2023.10.22.563476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have yet been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales.
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Affiliation(s)
- Giulia Baracchini
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Yigu Zhou
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jason da Silva Castanheira
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Justine Y. Hansen
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - Cheryl L. Grady
- Rotman Research Institute at Baycrest, and Department of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jason Nomi
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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6
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Navarro-González R, García-Azorín D, Guerrero-Peral ÁL, Planchuelo-Gómez Á, Aja-Fernández S, de Luis-García R. Increased MRI-based Brain Age in chronic migraine patients. J Headache Pain 2023; 24:133. [PMID: 37798720 PMCID: PMC10557155 DOI: 10.1186/s10194-023-01670-6] [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: 06/05/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023] Open
Abstract
INTRODUCTION Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. METHODS We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). RESULTS CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. CONCLUSION The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
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Affiliation(s)
| | - David García-Azorín
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain.
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain.
| | - Ángel L Guerrero-Peral
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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7
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Mwilambwe-Tshilobo L, Setton R, Bzdok D, Turner GR, Spreng RN. Age differences in functional brain networks associated with loneliness and empathy. Netw Neurosci 2023; 7:496-521. [PMID: 37397888 PMCID: PMC10312262 DOI: 10.1162/netn_a_00293] [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: 02/10/2022] [Accepted: 11/18/2022] [Indexed: 03/14/2024] Open
Abstract
Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality-loneliness and empathic responding-and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.
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Affiliation(s)
- Laetitia Mwilambwe-Tshilobo
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Roni Setton
- Department of Psychology, Harvard University, Boston, MA, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- School of Computer Science, McGill University, Montreal, QC, Canada
- Mila–Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Verdun, QC, Canada
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8
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Setton R, Mwilambwe-Tshilobo L, Sheldon S, Turner GR, Spreng RN. Hippocampus and temporal pole functional connectivity is associated with age and individual differences in autobiographical memory. Proc Natl Acad Sci U S A 2022; 119:e2203039119. [PMID: 36191210 PMCID: PMC9564102 DOI: 10.1073/pnas.2203039119] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/02/2022] [Indexed: 11/18/2022] Open
Abstract
Recollection of one's personal past, or autobiographical memory (AM), varies across individuals and across the life span. This manifests in the amount of episodic content recalled during AM, which may reflect differences in associated functional brain networks. We take an individual differences approach to examine resting-state functional connectivity of temporal lobe regions known to coordinate AM content retrieval with the default network (anterior and posterior hippocampus, temporal pole) and test for associations with AM. Multiecho resting-state functional magnetic resonance imaging (fMRI) and autobiographical interviews were collected for 158 younger and 105 older healthy adults. Interviews were scored for internal (episodic) and external (semantic) details. Age group differences in connectivity profiles revealed that older adults had lower connectivity within anterior hippocampus, posterior hippocampus, and temporal pole but greater connectivity with regions across the default network compared with younger adults. This pattern was positively related to posterior hippocampal volumes in older adults, which were smaller than younger adult volumes. Connectivity associations with AM showed two significant patterns. The first dissociated connectivity related to internal vs. external AM across participants. Internal AM was related to anterior hippocampus and temporal pole connectivity with orbitofrontal cortex and connectivity within posterior hippocampus. External AM was related to temporal pole connectivity with regions across the lateral temporal cortex. In the second pattern, younger adults displayed temporal pole connectivity with regions throughout the default network associated with more detailed AMs overall. Our findings provide evidence for discrete ensembles of brain regions that scale with systematic variation in recollective styles across the healthy adult life span.
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Affiliation(s)
- Roni Setton
- Department of Psychology, Harvard University, Cambridge, MA, 02138
| | - Laetitia Mwilambwe-Tshilobo
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Signy Sheldon
- Department of Psychology, McGill University, Montreal, QC, H3A 1G1, Canada
| | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, M3J 1P3, Canada
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, H3A 2B4, Canada
- Department of Psychology, McGill University, Montreal, QC, H3A 1G1, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, H3A 2B4, Canada
- Department of Psychiatry, McGill University, Montreal, QC, H3A 1A1, Canada
- Douglas Mental Health University Institute, Verdun, QC, H4H 1R3, Canada
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9
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Rohlfs C. Forbidden Knowledge and Specialized Training: A Versatile Solution for the Two Main Sources of Overfitting in Linear Regression. AM STAT 2022. [DOI: 10.1080/00031305.2022.2128874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Chris Rohlfs
- Department of Electrical Engineering, Columbia University
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10
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Setton R, Sheldon S, Turner GR, Spreng RN. Temporal pole volume is associated with episodic autobiographical memory in healthy older adults. Hippocampus 2022; 32:373-385. [PMID: 35247210 PMCID: PMC8995350 DOI: 10.1002/hipo.23411] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/09/2022] [Accepted: 02/12/2022] [Indexed: 12/12/2022]
Abstract
Recollection of personal past events differs across the lifespan. Older individuals recall fewer episodic details and convey more semantic information than young. Here we examine how gray matter volumes in temporal lobe regions integral to episodic and semantic memory (hippocampus and temporal poles, respectively) are related to age differences in autobiographical recollection. Gray matter volumes were obtained in healthy young (n = 158) and old (n = 105) adults. The temporal pole was demarcated and hippocampus segmented into anterior and posterior regions to test for volume differences between age groups. The Autobiographical Interview was administered to measure episodic and semantic autobiographical memory. Volume associations with episodic and semantic autobiographical memory were then assessed. Brain volumes were smaller for older adults in the posterior hippocampus. Autobiographical memory was less episodic and more semanticized for older versus younger adults. Older adults also showed positive associations between temporal pole volumes and episodic autobiographical recall; in the young, temporal pole volume was positively associated with performance on standard laboratory measures of semantic memory. Exploratory analyses revealed that age-related episodic autobiographical memory associations with anterior hippocampal volumes depended on sex. These findings suggest that age differences in brain structures implicated in episodic and semantic memory may portend reorganization of neural circuits to support autobiographical memory in later life.
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Affiliation(s)
- Roni Setton
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Signy Sheldon
- Departments of Psychology, McGill University, Montreal, Quebec, Canada
| | - Gary R Turner
- Department of Psychology, York University, Toronto, Ontario, Canada
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Departments of Psychology, McGill University, Montreal, Quebec, Canada.,McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Verdun, Quebec, Canada
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