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Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K. Structural-functional brain network coupling predicts human cognitive ability. Neuroimage 2024; 290:120563. [PMID: 38492685 DOI: 10.1016/j.neuroimage.2024.120563] [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: 08/01/2023] [Revised: 10/14/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.
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Affiliation(s)
- Johanna L Popp
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
| | - Jonas A Thiele
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
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2
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Ji S, Yang F, Li X. Spontaneous neural activity in the three principal networks underlying delay discounting: a resting-state fMRI study. Front Psychiatry 2024; 15:1320830. [PMID: 38370559 PMCID: PMC10869524 DOI: 10.3389/fpsyt.2024.1320830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Delay discounting, the decline in the subjective value of future rewards over time, has traditionally been understood through a tripartite neural network model, comprising the valuation, cognitive control, and prospection networks. To investigate the applicability of this model in a resting-state context, we employed a monetary choice questionnaire to quantify delay discounting and utilized resting-state functional magnetic resonance imaging (rs-fMRI) to explore the role of spontaneous brain activity, specifically regional homogeneity (ReHo), in influencing individual differences in delay discounting across a large cohort (N = 257). Preliminary analyses revealed a significant negative correlation between delay discounting tendencies and the ReHo in both the left insula and the right hippocampus, respectively. Subsequent resting-state functional connectivity (RSFC) analyses, using these regions as seed ROIs, disclosed that all implicated brain regions conform to the three principal networks traditionally associated with delay discounting. Our findings offer novel insights into the role of spontaneous neural activity in shaping individual variations in delay discounting at both regional and network levels, providing the first empirical evidence supporting the applicability of the tripartite network model in a resting-state context.
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Affiliation(s)
| | | | - Xueting Li
- Department of Psychology, Renmin University of China, Beijing, China
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3
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Wu J, Dong Q, Zhang J, Su Y, Wu T, Caselli RJ, Reiman EM, Ye J, Lepore N, Chen K, Thompson PM, Wang Y. Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology. Front Neurosci 2021; 15:762458. [PMID: 34899166 PMCID: PMC8655732 DOI: 10.3389/fnins.2021.762458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 11/01/2021] [Indexed: 12/03/2022] Open
Abstract
Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Richard J. Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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Wu J, Zhu W, Su Y, Gui J, Lepore N, Reiman EM, Caselli RJ, Thompson PM, Chen K, Wang Y. Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 12088:120880O. [PMID: 34961803 PMCID: PMC8710175 DOI: 10.1117/12.2607169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
| | - Wenhui Zhu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, USA
| | - Jie Gui
- School of Cyber Science and Engineering, Southeast University, Nanjing, China
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology Children’s Hospital Los Angeles, Los Angeles, USA
| | | | | | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, USA
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
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5
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Wu J, Dong Q, Gui J, Zhang J, Su Y, Chen K, Thompson PM, Caselli RJ, Reiman EM, Ye J, Wang Y. Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases. Front Neurosci 2021; 15:669595. [PMID: 34421510 PMCID: PMC8377280 DOI: 10.3389/fnins.2021.669595] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/15/2021] [Indexed: 01/04/2023] Open
Abstract
Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.,Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Gui
- School of Cyber Science and Engineering, Southeast University, Nanjing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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6
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Zhang Z, Wang X, Kong L, Zhu H. High-Dimensional Spatial Quantile Function-on-Scalar Regression. J Am Stat Assoc 2021; 117:1563-1578. [PMID: 37008532 PMCID: PMC10065478 DOI: 10.1080/01621459.2020.1870984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on functional responses across different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance.
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Affiliation(s)
- Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC
| | - Xiao Wang
- Department of Statistics, Purdue University, West Lafayette, IN
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
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7
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Fu Y, Zhang J, Li Y, Shi J, Zou Y, Guo H, Li Y, Yao Z, Wang Y, Hu B. A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:109989. [PMID: 32512131 PMCID: PMC9632410 DOI: 10.1016/j.pnpbp.2020.109989] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/19/2020] [Accepted: 05/30/2020] [Indexed: 10/24/2022]
Abstract
Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.
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Affiliation(s)
- Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yuan Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Ying Zou
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Hanning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yongchao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.
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8
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Waschl N, Burns NR. Sex differences in inductive reasoning: A research synthesis using meta-analytic techniques. PERSONALITY AND INDIVIDUAL DIFFERENCES 2020. [DOI: 10.1016/j.paid.2020.109959] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Bartnik-Olson B, Holshouser B, Ghosh N, Oyoyo UE, Nichols JG, Pivonka-Jones J, Tong K, Ashwal S. Evolving White Matter Injury following Pediatric Traumatic Brain Injury. J Neurotrauma 2020; 38:111-121. [PMID: 32515269 DOI: 10.1089/neu.2019.6574] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
This study is unique in that it examines the evolution of white matter injury very early and at 12 months post-injury in pediatric patients following traumatic brain injury (TBI). Diffusion tensor imaging (DTI) was acquired at two time-points: acutely at 6-17 days and 12 months following a complicated mild (cMild)/moderate (mod) or severe TBI. Regional measures of anisotropy and diffusivity were compared between TBI groups and against a group of age-matched healthy controls and used to predict performance on measures of attention, memory, and intellectual functioning at 12-months post-injury. Analysis of the acute DTI data using tract based spatial statistics revealed a small number of regional decreases in fractional anisotropy (FA) in both the cMild/mod and severe TBI groups compared with controls. These changes were observed in the occipital white matter, anterior limb of the internal capsule (ALIC)/basal ganglia, and corpus callosum. The severe TBI group showed regional differences in axial diffusivity (AD) in the brainstem and corpus callosum that were not seen in the cMild/mod TBI group. By 12-months, widespread decreases in FA and increases in apparent diffusion coefficient (ADC) and radial diffusivity (RD) were observed in both TBI groups compared with controls, with the overall number of regions with abnormal DTI metrics increasing over time. The early changes in regional DTI metrics were associated with 12-month performance IQ scores. These findings suggest that there may be regional differences in the brain's reparative processes or that mechanisms associated with the brain's plasticity to recover may also be region based.
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Affiliation(s)
- Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Health, Loma Linda, California, USA
| | - Barbara Holshouser
- Department of Radiology, Loma Linda University Health, Loma Linda, California, USA
| | - Nirmalya Ghosh
- Department of Pediatrics, Loma Linda University Health, Loma Linda, California, USA
| | - Udochukwu E Oyoyo
- Department of Radiology, Loma Linda University Health, Loma Linda, California, USA
| | - Joy G Nichols
- Department of Pediatrics, Loma Linda University Health, Loma Linda, California, USA
| | - Jamie Pivonka-Jones
- Department of Pediatrics, Loma Linda University Health, Loma Linda, California, USA
| | - Karen Tong
- Department of Radiology, Loma Linda University Health, Loma Linda, California, USA
| | - Stephen Ashwal
- Department of Pediatrics, Loma Linda University Health, Loma Linda, California, USA
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10
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Genome wide association study of incomplete hippocampal inversion in adolescents. PLoS One 2020; 15:e0227355. [PMID: 31990937 PMCID: PMC6986744 DOI: 10.1371/journal.pone.0227355] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 12/17/2019] [Indexed: 12/23/2022] Open
Abstract
Incomplete hippocampal inversion (IHI), also called hippocampal malrotation, is an atypical presentation of the hippocampus present in about 20% of healthy individuals. Here we conducted the first genome-wide association study (GWAS) in IHI to elucidate the genetic underpinnings that may contribute to the incomplete inversion during brain development. A total of 1381 subjects contributed to the discovery cohort obtained from the IMAGEN database. The incidence rate of IHI was 26.1%. Loci with P<1e-5 were followed up in a validation cohort comprising 161 subjects from the PING study. Summary statistics from the discovery cohort were used to compute IHI heritability as well as genetic correlations with other traits. A locus on 18q11.2 (rs9952569; OR = 1.999; Z = 5.502; P = 3.755e-8) showed a significant association with the presence of IHI. A functional annotation of the locus implicated genes AQP4 and KCTD1. However, neither this locus nor the other 16 suggestive loci reached a significant p-value in the validation cohort. The h2 estimate was 0.54 (sd: 0.30) and was significant (Z = 1.8; P = 0.036). The top three genetic correlations of IHI were with traits representing either intelligence or education attainment and reached nominal P< = 0.013.
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11
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Wang Y, Xu Q, Luo J, Hu M, Zuo C. Effects of Age and Sex on Subcortical Volumes. Front Aging Neurosci 2019; 11:259. [PMID: 31616285 PMCID: PMC6775221 DOI: 10.3389/fnagi.2019.00259] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 09/02/2019] [Indexed: 12/29/2022] Open
Abstract
Purpose In an increasingly aging society, it is of great importance to consider trajectories of subcortical volumes at different ages for understanding biological markers of aging. Thus, we investigated sex, age, and their interactions on subcortical volumes, including the basal ganglia (caudate, putamen, accumbens, and pallidum), thalamus, hippocampus, and amygdala. Methods We analyzed the adult lifespan trajectory of subcortical volumes and asymmetries in 563 healthy subjects aged from 19 to 86 using magnetic resonance imaging (MRI) data from the publicly available 7IXI data set. Results The sex made strong contributions to the trajectories of subcortical volumes with aging, including the right putamen, right pallidum, bilateral thalamus, hippocampus, and amygdala. The volume of the right putamen, right pallidum, and right thalamus decreased more rapidly in males than in females, and the volume of the left thalamus, bilateral hippocampus, and amygdala in males followed a quadratic model, while those in females followed a linear decline model. The asymmetries in the caudate and hippocampus showed a linear decline, and a sex and age interaction was found in the hippocampus; that is, the asymmetry in the hippocampus decreased only in the males and not in the females. Changes in the accumbens and pallidum fit quadratic trajectories, in which females increased until 39.26 years old in the accumbens asymmetry and then began to rapidly decline, and males showed a linear decline. The asymmetry in the pallidum in males and females showed a slow decreasing period until almost 45 years of age and then increased. Conclusion The results suggest that compared with females, males have a faster decline in the volume of the right putamen, right pallidum, and right thalamus, while aging occurred later but also faster in the left thalamus, bilateral hippocampus, and amygdala. Interestingly, we found the inflection point in the thalamus, bilateral hippocampus, and amygdala volume in the quadratic model, and after this point, the volume change accelerated with aging, which may have resulted from the stronger work pressure in the middle-aged men and the low levels of testosterone in the older adults. The interaction of age and sex on individual subcortical structures provides evidence to support the impact of sex on psychopathologies associated with degenerative brain disorders in the elderly. The findings may be significant to investigate the occurrence and prevalence of degenerative brain disorders in males and females. Future studies can focus on the functional and behavioral relations with subcortical structures for preventive measures of related disorders.
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Affiliation(s)
- Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qinfang Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Jiangsu Provincial Key Laboratory of Special Children's Impairment and Intervention, Nanjing Normal University of Special Education, Nanjing, China
| | - Jie Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Chenyi Zuo
- College of Educational Science, Anhui Normal University, Wuhu, China
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12
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Abstract
Cognitive training and brain stimulation studies have suggested that human cognition, primarily working memory and attention control processes, can be enhanced. Some authors claim that gains (i.e., post-test minus pretest scores) from such interventions are unevenly distributed among people. The magnification account (expressed by the evangelical “who has will more be given”) predicts that the largest gains will be shown by the most cognitively efficient people, who will also be most effective in exploiting interventions. In contrast, the compensation account (“who has will less be given”) predicts that such people already perform at ceiling, so interventions will yield the largest gains in the least cognitively efficient people. Evidence for this latter account comes from reported negative correlations between the pretest and the training/stimulation gain. In this paper, with the use of mathematical derivations and simulation methods, we show that such correlations are pure statistical artifacts caused by the widely known methodological error called “regression to the mean”. Unfortunately, more advanced methods, such as alternative measures, linear models, and control groups do not guarantee correct assessment of the compensation effect either. The only correct method is to use direct modeling of correlations between latent true measures and gain. As to date no training/stimulation study has correctly used this method to provide evidence in favor of the compensation account, we must conclude that most (if not all) of the evidence should be considered inconclusive.
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13
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Arribas-Aguila D, Abad FJ, Colom R. Testing the developmental theory of sex differences in intelligence using latent modeling: Evidence from the TEA Ability Battery (BAT-7). PERSONALITY AND INDIVIDUAL DIFFERENCES 2019. [DOI: 10.1016/j.paid.2018.09.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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14
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Martini M, Zamarian L, Sachse P, Martini C, Delazer M. Wakeful resting and memory retention: a study with healthy older and younger adults. Cogn Process 2019; 20:125-131. [PMID: 30377871 PMCID: PMC6397711 DOI: 10.1007/s10339-018-0891-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 10/21/2018] [Indexed: 01/17/2023]
Abstract
Studies indicate that a brief period of wakeful rest after learning supports memory retention, whereas distraction weakens it. It is open for investigation whether advanced age has a significant effect on the impact of post-learning wakeful rest on memory retention for verbal information when compared to a cognitively demanding distraction task. In this study, we examined (1) whether post-learning rest promotes verbal memory retention in younger and older adults and (2) whether the magnitude of the rest benefit changes with increasing age. Younger adults and older adults learned and immediately recalled two consecutive word lists. After one word list, participants rested wakefully for 8 min; after the other list, they solved matrices. Memory performance was again tested in a surprise free recall test at the end of the experimental session. We found that, overall, younger adults outperformed older adults. Also, memory retention was higher following a wakeful rest phase compared to distraction. A detailed analysis revealed that this wakeful rest benefit was significant for the older adults group, whereas the younger adults group retained a similar amount of information in both post-encoding conditions. We assume that older adults can profit more from a wakeful rest phase after learning and are more prone to distraction than younger adults. With increasing age, a short break immediately after information uptake may help better retain the previously learned information, while distraction after learning tends to weaken memory retention.
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Affiliation(s)
- Markus Martini
- University of Innsbruck, Innrain 52, 6020, Innsbruck, Austria.
| | - Laura Zamarian
- Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Pierre Sachse
- University of Innsbruck, Innrain 52, 6020, Innsbruck, Austria
| | | | - Margarete Delazer
- Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
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15
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Zappasodi F, Perrucci MG, Saggino A, Croce P, Mercuri P, Romanelli R, Colom R, Ebisch SJH. EEG microstates distinguish between cognitive components of fluid reasoning. Neuroimage 2019; 189:560-573. [PMID: 30710677 DOI: 10.1016/j.neuroimage.2019.01.067] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 01/14/2019] [Accepted: 01/26/2019] [Indexed: 01/31/2023] Open
Abstract
Fluid reasoning is considered central to general intelligence. How its psychometric structure relates to brain function remains poorly understood. For instance, what is the dynamic composition of ability-specific processes underlying fluid reasoning? We investigated whether distinct fluid reasoning abilities could be differentiated by electroencephalography (EEG) microstate profiles. EEG microstates specifically capture rapidly altering activity of distributed cortical networks with a high temporal resolution as scalp potential topographies that dynamically vary over time in an organized manner. EEG was recorded simultaneously with functional magnetic resonance imaging (fMRI) in twenty healthy adult participants during cognitively distinct fluid reasoning tasks: induction, spatial relationships and visualization. Microstate parameters successfully discriminated between fluid reasoning and visuomotor control tasks as well as between the fluid reasoning tasks. Mainly, microstate B coverage was significantly higher during spatial relationships and visualization, compared to induction, while microstate C coverage was significantly decreased during spatial relationships and visualization, compared to induction. Additionally, microstate D coverage was highest during spatial relationships and microstate A coverage was most strongly reduced during the same condition. Consistently, multivariate analysis with a leave-one-out cross-validation procedure accurately classified the fluid reasoning tasks based on the coverage parameter. These EEG data and their correlation with fMRI data suggest that especially the tasks most strongly relying on visuospatial processing modulated visual and default mode network activity. We propose that EEG microstates can provide valuable information about neural activity patterns with a dynamic and complex temporal structure during fluid reasoning, suggesting cognitive ability-specific interplays between multiple brain networks.
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Affiliation(s)
- Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute of Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Mauro Gianni Perrucci
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute of Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Aristide Saggino
- School of Medicine and Health Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Pasqua Mercuri
- School of Medicine and Health Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Roberta Romanelli
- School of Medicine and Health Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | | | - Sjoerd J H Ebisch
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute of Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
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16
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Geary DC. Evolution of Human Sex-Specific Cognitive Vulnerabilities. QUARTERLY REVIEW OF BIOLOGY 2017. [DOI: 10.1086/694934] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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17
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Abstract
Visual-spatial working memory measures are widely used in clinical and experimental settings. Furthermore, it has been argued that the male advantage in spatial abilities can be explained by a sex difference in visual-spatial working memory. Therefore, sex differences in visual-spatial working memory have important implication for research, theory, and practice, but they have yet to be quantified. The present meta-analysis quantified the magnitude of sex differences in visual-spatial working memory and examined variables that might moderate them. The analysis used a set of 180 effect sizes from healthy males and females drawn from 98 samples ranging in mean age from 3 to 86 years. Multilevel meta-analysis was used on the overall data set to account for non-independent effect sizes. The data also were analyzed in separate task subgroups by means of multilevel and mixed-effects models. Results showed a small but significant male advantage (mean d = 0.155, 95 % confidence interval = 0.087-0.223). All the tasks produced a male advantage, except for memory for location, where a female advantage emerged. Age of the participants was a significant moderator, indicating that sex differences in visual-spatial working memory appeared first in the 13-17 years age group. Removing memory for location tasks from the sample affected the pattern of significant moderators. The present results indicate a male advantage in visual-spatial working memory, although age and specific task modulate the magnitude and direction of the effects. Implications for clinical applications, cognitive model building, and experimental research are discussed.
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18
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Matosin N, Newell KA, Quidé Y, Andrews JL, Teroganova N, Green MJ, Fernandez F. Effects of common GRM5 genetic variants on cognition, hippocampal volume and mGluR5 protein levels in schizophrenia. Brain Imaging Behav 2017; 12:509-517. [DOI: 10.1007/s11682-017-9712-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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19
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Abstract
The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data. We develop an efficient penalized total variation optimization to estimate the unknown slope function and other parameters. We also establish nonasymptotic error bounds on the excess risk. These bounds are explicitly specified in terms of sample size, image size, and image smoothness. Our simulations demonstrate a superior performance of GSIRM-TV against many existing approaches. We apply GSIRM-TV to the analysis of hippocampus data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset.
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Affiliation(s)
- Xiao Wang
- Associate Professor of Statistics, Department of Statistics, Purdue University, West Lafayette, IN 47907
| | - Hongtu Zhu
- Professor of Biostatistics, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, and University of North Carolina, Chapel Hill, NC 27599
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20
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Zhu B, Chen C, Dang X, Dong Q, Lin C. Hippocampal subfields' volumes are more relevant to fluid intelligence than verbal working memory. INTELLIGENCE 2017. [DOI: 10.1016/j.intell.2017.02.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Valdés Hernández MDC, Cox SR, Kim J, Royle NA, Muñoz Maniega S, Gow AJ, Anblagan D, Bastin ME, Park J, Starr JM, Wardlaw JM, Deary IJ. Hippocampal morphology and cognitive functions in community-dwelling older people: the Lothian Birth Cohort 1936. Neurobiol Aging 2016; 52:1-11. [PMID: 28104542 PMCID: PMC5364373 DOI: 10.1016/j.neurobiolaging.2016.12.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 11/18/2016] [Accepted: 12/13/2016] [Indexed: 01/18/2023]
Abstract
Structural measures of the hippocampus have been linked to a variety of memory processes and also to broader cognitive abilities. Gross volumetry has been widely used, yet the hippocampus has a complex formation, comprising distinct subfields which may be differentially sensitive to the deleterious effects of age, and to different aspects of cognitive performance. However, a comprehensive analysis of multidomain cognitive associations with hippocampal deformations among a large group of cognitively normal older adults is currently lacking. In 654 participants of the Lothian Birth Cohort 1936 (mean age = 72.5, SD = 0.71 years), we examined associations between the morphology of the hippocampus and a variety of memory tests (spatial span, letter-number sequencing, verbal recall, and digit backwards), as well as broader cognitive domains (latent measures of speed, fluid intelligence, and memory). Following correction for age, sex, and vascular risk factors, analysis of memory subtests revealed that only right hippocampal associations in relation to spatial memory survived type 1 error correction in subiculum and in CA1 at the head (β = 0.201, p = 5.843 × 10-4, outward), and in the ventral tail section of CA1 (β = -0.272, p = 1.347 × 10-5, inward). With respect to latent measures of cognitive domains, only deformations associated with processing speed survived type 1 error correction in bilateral subiculum (βabsolute ≤ 0.247, p < 1.369 × 10-4, outward), bilaterally in the ventral tail section of CA1 (βabsolute ≤ 0.242, p < 3.451 × 10-6, inward), and a cluster at the left anterior-to-dorsal region of the head (β = 0.199, p = 5.220 × 10-6, outward). Overall, our results indicate that a complex pattern of both inward and outward hippocampal deformations are associated with better processing speed and spatial memory in older age, suggesting that complex shape-based hippocampal analyses may provide valuable information beyond gross volumetry.
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Affiliation(s)
- Maria Del Carmen Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - Jaeil Kim
- School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Natalie A Royle
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, Heriot-Watt University, Edinburgh, UK
| | - Devasuda Anblagan
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Jinah Park
- School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
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22
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O'Shea A, Cohen RA, Porges EC, Nissim NR, Woods AJ. Cognitive Aging and the Hippocampus in Older Adults. Front Aging Neurosci 2016; 8:298. [PMID: 28008314 PMCID: PMC5143675 DOI: 10.3389/fnagi.2016.00298] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 11/22/2016] [Indexed: 11/13/2022] Open
Abstract
The hippocampus is one of the most well studied structures in the human brain. While age-related decline in hippocampal volume is well documented, most of our knowledge about hippocampal structure-function relationships was discovered in the context of neurological and neurodegenerative diseases. The relationship between cognitive aging and hippocampal structure in the absence of disease remains relatively understudied. Furthermore, the few studies that have investigated the role of the hippocampus in cognitive aging have produced contradictory results. To address these issues, we assessed 93 older adults from the general community (mean age = 71.9 ± 9.3 years) on the Montreal Cognitive Assessment (MoCA), a brief cognitive screening measure for dementia, and the NIH Toolbox-Cognitive Battery (NIHTB-CB), a computerized neurocognitive battery. High-resolution structural magnetic resonance imaging (MRI) was used to estimate hippocampal volume. Lower MoCA Total (p = 0.01) and NIHTB-CB Fluid Cognition (p < 0.001) scores were associated with decreased hippocampal volume, even while controlling for sex and years of education. Decreased hippocampal volume was significantly associated with decline in multiple NIHTB-CB subdomains, including episodic memory, working memory, processing speed and executive function. This study provides important insight into the multifaceted role of the hippocampus in cognitive aging.
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Affiliation(s)
- Andrew O'Shea
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida Gainesville, FL, USA
| | - Ronald A Cohen
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida Gainesville, FL, USA
| | - Eric C Porges
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida Gainesville, FL, USA
| | - Nicole R Nissim
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, University of FloridaGainesville, FL, USA; Department of Neuroscience, University of FloridaGainesville, FL, USA
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health Psychology, University of FloridaGainesville, FL, USA; Department of Neuroscience, University of FloridaGainesville, FL, USA
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23
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Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan Y, Murali Doraiswamy P, Zhu H. Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. J Alzheimers Dis 2016; 46:695-702. [PMID: 25869783 DOI: 10.3233/jad-150164] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment.
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Affiliation(s)
- Dehan Kong
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychology, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Eunjee Lee
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - P Murali Doraiswamy
- Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
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24
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Shi J, Collignon O, Xu L, Wang G, Kang Y, Leporé F, Lao Y, Joshi AA, Leporé N, Wang Y. Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry. Neuroinformatics 2016; 13:321-336. [PMID: 25649876 DOI: 10.1007/s12021-014-9259-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Blindness represents a unique model to study how visual experience may shape the development of brain organization. Exploring how the structure of the corpus callosum (CC) reorganizes ensuing visual deprivation is of particular interest due to its important functional implication in vision (e.g., via the splenium of the CC). Moreover, comparing early versus late visually deprived individuals has the potential to unravel the existence of a sensitive period for reshaping the CC structure. Here, we develop a novel framework to capture a complete set of shape differences in the CC between congenitally blind (CB), late blind (LB) and sighted control (SC) groups. The CCs were manually segmented from T1-weighted brain MRI and modeled by 3D tetrahedral meshes. We statistically compared the combination of local area and thickness at each point between subject groups. Differences in area are found using surface tensor-based morphometry; thickness is estimated by tracing the streamlines in the volumetric harmonic field. Group differences were assessed on this combined measure using Hotelling's T(2) test. Interestingly, we observed that the total callosal volume did not differ between the groups. However, our fine-grained analysis reveals significant differences mostly localized around the splenium areas between both blind groups and the sighted group (general effects of blindness) and, importantly, specific dissimilarities between the LB and CB groups, illustrating the existence of a sensitive period for reorganization. The new multivariate statistics also gave better effect sizes for detecting morphometric differences, relative to other statistics. They may boost statistical power for CC morphometric analyses.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Liang Xu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Gang Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Yue Kang
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Franco Leporé
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Yi Lao
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Anand A Joshi
- Signal and Image Processing Institute, Brain and Creativity Institute, University of Southern California, Los Angeles, CA, USA
| | - Natasha Leporé
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
- Department of Radiology & Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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25
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Lee E, Zhu H, Kong D, Wang Y, Giovanello KS, Ibrahim JG. BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE. Ann Appl Stat 2015; 9:2153-2178. [PMID: 26900412 DOI: 10.1214/15-aoas879] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.
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Affiliation(s)
- Eunjee Lee
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dehan Kong
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering Arizona State University Tempe, AZ 85287-8809
| | - Kelly Sullivan Giovanello
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Joseph G Ibrahim
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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26
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Drollette ES, Scudder MR, Raine LB, Davis Moore R, Pontifex MB, Erickson KI, Hillman CH. The sexual dimorphic association of cardiorespiratory fitness to working memory in children. Dev Sci 2015; 19:90-108. [DOI: 10.1111/desc.12291] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 12/22/2014] [Indexed: 11/30/2022]
Affiliation(s)
- Eric S. Drollette
- Department of Kinesiology and Community Health; University of Illinois at Urbana-Champaign; USA
| | - Mark R. Scudder
- Department of Kinesiology and Community Health; University of Illinois at Urbana-Champaign; USA
| | - Lauren B. Raine
- Department of Kinesiology and Community Health; University of Illinois at Urbana-Champaign; USA
| | - R. Davis Moore
- Department of Kinesiology and Community Health; University of Illinois at Urbana-Champaign; USA
| | | | - Kirk I. Erickson
- Department of Psychology; University of Pittsburgh; USA
- The Center for the Neural Basis of Cognition; University of Pittsburgh; USA
| | - Charles H. Hillman
- Department of Kinesiology and Community Health; University of Illinois at Urbana-Champaign; USA
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27
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Shi J, Stonnington CM, Thompson PM, Chen K, Gutman B, Reschke C, Baxter LC, Reiman EM, Caselli RJ, Wang Y. Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry. Neuroimage 2015; 104:1-20. [PMID: 25285374 PMCID: PMC4252650 DOI: 10.1016/j.neuroimage.2014.09.062] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2014] [Revised: 09/20/2014] [Accepted: 09/29/2014] [Indexed: 11/29/2022] Open
Abstract
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Boris Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Cole Reschke
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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Escorial S, Román FJ, Martínez K, Burgaleta M, Karama S, Colom R. Sex differences in neocortical structure and cognitive performance: A surface-based morphometry study. Neuroimage 2014; 104:355-65. [PMID: 25255941 DOI: 10.1016/j.neuroimage.2014.09.035] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 09/04/2014] [Accepted: 09/16/2014] [Indexed: 11/29/2022] Open
Abstract
On average, men show larger brain volumes than women. Regional differences have been also observed, although most of the available studies apply voxel-based morphometry (VBM). Reports applying surface-based morphometry (SBM) have been focused mainly on cortical thickness (CT). Here we apply SBM for obtaining global and regional indices of CT, cortical surface area (CSA), and cortical gray matter volume (CGMV) from samples of men (N=40) and women (N=40) matched for their performance on four cognitive factors varying in their complexity: processing speed, attention control, working memory capacity, and fluid intelligence. These were the main findings: 1) CT and CSA produced very weak correlations in both sexes, 2) men showed larger values in CT, CSA, and CGMV, and 3) cognitive performance was unrelated to brain structural variation within sexes. Therefore, we found substantial group differences in brain structure, but there was no relationship with cognitive performance both between and within-sexes.
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Affiliation(s)
| | | | - Kenia Martínez
- Universidad Autónoma de Madrid, Spain; Hospital Gregorio Marañón, Madrid, Spain
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29
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Jäncke L, Mérillat S, Liem F, Hänggi J. Brain size, sex, and the aging brain. Hum Brain Mapp 2014; 36:150-69. [PMID: 25161056 DOI: 10.1002/hbm.22619] [Citation(s) in RCA: 154] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Revised: 07/17/2014] [Accepted: 08/18/2014] [Indexed: 12/13/2022] Open
Abstract
This study was conducted to examine the statistical influence of brain size on cortical, subcortical, and cerebellar compartmental volumes. This brain size influence was especially studied to delineate interactions with Sex and Age. Here, we studied 856 healthy subjects of which 533 are classified as young and 323 as old. Using an automated segmentation procedure cortical (gray and white matter [GM and WM] including the corpus callosum), cerebellar (GM and WM), and subcortical (thalamus, putamen, pallidum, caudatus, hippocampus, amygdala, and accumbens) volumes were measured and subjected to statistical analyses. These analyses revealed that brain size and age exert substantial statistical influences on nearly all compartmental volumes. Analyzing the raw compartmental volumes replicated the frequently reported Sex differences in compartmental volumes with men showing larger volumes. However, when statistically controlling for brain size Sex differences and Sex × Age interactions practically disappear. Thus, brain size is more important than Sex in explaining interindividual differences in compartmental volumes. The influence of brain size is discussed in the context of an allometric scaling of the compartmental volumes.
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Affiliation(s)
- Lutz Jäncke
- Division Neuropsychology, Institute of Psychology, University of Zurich, Switzerland; Center for Integrative Human Physiology, University of Zurich, Switzerland; International Normal Aging and Plasticity Imaging Center (INAPIC), University of Zurich, Switzerland; University Research Priority Program (URPP) "Dynamics of Healthy Aging", University of Zurich, Switzerland; Department of Special Education, King Abdulaziz University, Jeddah, Saudi Arabia
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30
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Burgaleta M, MacDonald PA, Martínez K, Román FJ, Álvarez‐Linera J, González AR, Karama S, Colom R. Subcortical regional morphology correlates with fluid and spatial intelligence. Hum Brain Mapp 2014; 35:1957-68. [PMID: 23913782 PMCID: PMC6869737 DOI: 10.1002/hbm.22305] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Revised: 02/28/2013] [Accepted: 03/25/2013] [Indexed: 11/09/2022] Open
Abstract
Neuroimaging studies have revealed associations between intelligence and brain morphology. However, researchers have focused primarily on the anatomical features of the cerebral cortex, whereas subcortical structures, such as the basal ganglia (BG), have often been neglected despite extensive functional evidence on their relation with higher-order cognition. Here we performed shape analyses to understand how individual differences in BG local morphology account for variability in cognitive performance. Structural MRI was acquired in 104 young adults (45 men, 59 women, mean age = 19.83, SD = 1.64), and the outer surface of striatal structures (caudate, nucleus accumbens, and putamen), globus pallidus, and thalamus was estimated for each subject and hemisphere. Further, nine cognitive tests were used to measure fluid (Gf), crystallized (Gc), and spatial intelligence (Gv). Latent scores for these factors were computed by means of confirmatory factor analysis and regressed vertex-wise against subcortical shape (local displacements of vertex position), controlling for age, sex, and adjusted for brain size. Significant results (FDR < 5%) were found for Gf and Gv, but not Gc, for the right striatal structures and thalamus. The main results show a relative enlargement of the rostral putamen, which is functionally connected to the right dorsolateral prefrontal cortex and other intelligence-related prefrontal areas.
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Affiliation(s)
- Miguel Burgaleta
- Center for Brain and CognitionUniversitat Pompeu FabraBarcelonaSpain
- Facultad de PsicologíaUniversidad Autónoma de MadridMadridSpain
- Fundación CIEN‐Fundación Reina SofíaMadridSpain
| | - Penny A. MacDonald
- Brain and Mind Institute, University of Western OntarioLondonOntarioCanada
| | - Kenia Martínez
- Facultad de PsicologíaUniversidad Autónoma de MadridMadridSpain
- Fundación CIEN‐Fundación Reina SofíaMadridSpain
| | - Francisco J. Román
- Facultad de PsicologíaUniversidad Autónoma de MadridMadridSpain
- Fundación CIEN‐Fundación Reina SofíaMadridSpain
| | - Juan Álvarez‐Linera
- Fundación CIEN‐Fundación Reina SofíaMadridSpain
- Ruber International HospitalMadridSpain
| | - Ana Ramos González
- Sección de NeurorradiologíaHospital Universitario 12 de OctubreMadridSpain
| | - Sherif Karama
- Douglas Mental Health University InstituteMcGill UniversityMontrealQuebec
| | - Roberto Colom
- Facultad de PsicologíaUniversidad Autónoma de MadridMadridSpain
- Fundación CIEN‐Fundación Reina SofíaMadridSpain
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31
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Rhein C, Mühle C, Richter-Schmidinger T, Alexopoulos P, Doerfler A, Kornhuber J. Neuroanatomical correlates of intelligence in healthy young adults: the role of basal ganglia volume. PLoS One 2014; 9:e93623. [PMID: 24699871 PMCID: PMC3974758 DOI: 10.1371/journal.pone.0093623] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Accepted: 03/06/2014] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In neuropsychiatric diseases with basal ganglia involvement, higher cognitive functions are often impaired. In this exploratory study, we examined healthy young adults to gain detailed insight into the relationship between basal ganglia volume and cognitive abilities under non-pathological conditions. METHODOLOGY/PRINCIPAL FINDINGS We investigated 137 healthy adults that were between the ages of 21 and 35 years with similar educational backgrounds. Magnetic resonance imaging (MRI) was performed, and volumes of basal ganglia nuclei in both hemispheres were calculated using FreeSurfer software. The cognitive assessment consisted of verbal, numeric and figural aspects of intelligence for either the fluid or the crystallised intelligence factor using the intelligence test Intelligenz-Struktur-Test (I-S-T 2000 R). Our data revealed significant correlations of the caudate nucleus and pallidum volumes with figural and numeric aspects of intelligence, but not with verbal intelligence. Interestingly, figural intelligence associations were dependent on sex and intelligence factor; in females, the pallidum volumes were correlated with crystallised figural intelligence (r = 0.372, p = 0.01), whereas in males, the caudate volumes were correlated with fluid figural intelligence (r = 0.507, p = 0.01). Numeric intelligence was correlated with right-lateralised caudate nucleus volumes for both females and males, but only for crystallised intelligence (r = 0.306, p = 0.04 and r = 0.459, p = 0.04, respectively). The associations were not mediated by prefrontal cortical subfield volumes when controlling with partial correlation analyses. CONCLUSIONS/SIGNIFICANCE The findings of our exploratory analysis indicate that figural and numeric intelligence aspects, but not verbal aspects, are strongly associated with basal ganglia volumes. Unlike numeric intelligence, the type of figural intelligence appears to be related to distinct basal ganglia nuclei in a sex-specific manner. Subcortical brain structures thus may contribute substantially to cognitive performance.
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Affiliation(s)
- Cosima Rhein
- Department of Psychiatry and Psychotherapy, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Christiane Mühle
- Department of Psychiatry and Psychotherapy, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Tanja Richter-Schmidinger
- Department of Psychiatry and Psychotherapy, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Panagiotis Alexopoulos
- Department of Psychiatry and Psychotherapy, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | - Arnd Doerfler
- Department of Neuroradiology, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
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32
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Román FJ, Abad FJ, Escorial S, Burgaleta M, Martínez K, Álvarez-Linera J, Quiroga MÁ, Karama S, Haier RJ, Colom R. Reversed hierarchy in the brain for general and specific cognitive abilities: a morphometric analysis. Hum Brain Mapp 2014; 35:3805-18. [PMID: 24677433 DOI: 10.1002/hbm.22438] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Revised: 09/16/2013] [Accepted: 11/11/2013] [Indexed: 11/12/2022] Open
Abstract
Intelligence is composed of a set of cognitive abilities hierarchically organized. General and specific abilities capture distinguishable, but related, facets of the intelligence construct. Here, we analyze gray matter with three morphometric indices (volume, cortical surface area, and cortical thickness) at three levels of the intelligence hierarchy (tests, first-order factors, and a higher-order general factor, g). A group of one hundred and four healthy young adults completed a cognitive battery and underwent high-resolution structural MRI. Latent scores were computed for the intelligence factors and tests were also analyzed. The key finding reveals substantial variability in gray matter correlates at the test level, which is substantially reduced for the first-order and the higher-order factors. This supports a reversed hierarchy in the brain with respect to cognitive abilities at different psychometric levels: the greater the generality, the smaller the number of relevant gray matter clusters accounting for individual differences in intelligent performance.
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Affiliation(s)
- Francisco J Román
- Facultad de Psicología, Universidad Autónoma de Madrid, 28049, Madrid, Spain; Fundación CIEN - Fundación Reina Sofía, 28031, Madrid, Spain
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