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Cosío-Guirado R, Tapia-Medina MG, Kaya C, Peró-Cebollero M, Villuendas-González ER, Guàrdia-Olmos J. A comprehensive systematic review of fMRI studies on brain connectivity in healthy children and adolescents: Current insights and future directions. Dev Cogn Neurosci 2024; 69:101438. [PMID: 39153422 PMCID: PMC11381617 DOI: 10.1016/j.dcn.2024.101438] [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: 03/19/2024] [Revised: 08/07/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024] Open
Abstract
This systematic review considered evidence of children's and adolescents' typical brain connectivity development studied through resting-state functional magnetic resonance imaging (rs-fMRI). With aim of understanding the state of the art, what has been researched thus far and what remains unknown, this paper reviews 58 studies from 2013 to 2023. Considering the results, rs-fMRI stands out as an appropriate technique for studying language and attention within cognitive domains, and personality traits such as impulsivity and empathy. The most used analyses encompass seed-based, independent component analysis (ICA), the amplitude of the low frequency fluctuations (ALFF), and fractional ALFF (fALFF). The findings highlight key themes, including age-related changes in intrinsic connectivity, sex-specific patterns, and the relevance of the Default Mode Network (DMN). Overall, there is a need for longitudinal approaches to trace the typical developmental trajectory of neural networks from childhood through adolescence with fMRI at rest.
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Affiliation(s)
- Raquel Cosío-Guirado
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain; Institute of Neuroscience, Universitat de Barcelona, Barcelona, Spain.
| | - Mérida Galilea Tapia-Medina
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain; Institute of Neuroscience, Universitat de Barcelona, Barcelona, Spain
| | - Ceren Kaya
- Department of Psychology, Faculty of Arts and Sciences, Izmir University of Economics, Izmir, Turkey
| | - Maribel Peró-Cebollero
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain; Institute of Neuroscience, Universitat de Barcelona, Barcelona, Spain
| | | | - Joan Guàrdia-Olmos
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain; Institute of Complex Systems, Universitat de Barcelona, Barcelona, Spain; Institute of Neuroscience, Universitat de Barcelona, Barcelona, Spain
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Orlichenko A, Qu G, Zhou Z, Liu A, Deng HW, Ding Z, Stephen JM, Wilson TW, Calhoun VD, Wang YP. A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594528. [PMID: 38798580 PMCID: PMC11118390 DOI: 10.1101/2024.05.16.594528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.
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Orlichenko A, Su KJ, Shen H, Deng HW, Wang YP. Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort. J Med Imaging (Bellingham) 2024; 11:024010. [PMID: 38618171 PMCID: PMC11009525 DOI: 10.1117/1.jmi.11.2.024010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/26/2024] [Accepted: 03/29/2024] [Indexed: 04/16/2024] Open
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t -test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p < 10 - 15 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.
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Affiliation(s)
- Anton Orlichenko
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
| | - Kuan-Jui Su
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hui Shen
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hong-Wen Deng
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Yu-Ping Wang
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
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Orlichenko A, Su KJ, Tian Q, Shen H, Deng HW, Wang YP. Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.15.23294133. [PMID: 37645791 PMCID: PMC10462217 DOI: 10.1101/2023.08.15.23294133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Kuan-Jui Su
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Qing Tian
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hui Shen
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hong-Wen Deng
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
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Zelco A, Wapeesittipan P, Joshi A. Insights into Sex and Gender Differences in Brain and Psychopathologies Using Big Data. Life (Basel) 2023; 13:1676. [PMID: 37629533 PMCID: PMC10455614 DOI: 10.3390/life13081676] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/30/2023] [Accepted: 07/15/2023] [Indexed: 08/27/2023] Open
Abstract
The societal implication of sex and gender (SG) differences in brain are profound, as they influence brain development, behavior, and importantly, the presentation, prevalence, and therapeutic response to diseases. Technological advances have enabled speed up identification and characterization of SG differences during development and in psychopathologies. The main aim of this review is to elaborate on new technological advancements, such as genomics, imaging, and emerging biobanks, coupled with bioinformatics analyses of data generated from these technologies have facilitated the identification and characterization of SG differences in the human brain through development and psychopathologies. First, a brief explanation of SG concepts is provided, along with a developmental and evolutionary context. We then describe physiological SG differences in brain activity and function, and in psychopathologies identified through imaging techniques. We further provide an overview of insights into SG differences using genomics, specifically taking advantage of large cohorts and biobanks. We finally emphasize how bioinformatics analyses of big data generated by emerging technologies provides new opportunities to reduce SG disparities in health outcomes, including major challenges.
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Affiliation(s)
| | | | - Anagha Joshi
- Department of Clinical Science, Computational Biology Unit, University of Bergen, 5020 Bergen, Norway; (A.Z.); (P.W.)
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Kerr-German A, White SF, Santosa H, Buss AT, Doucet GE. Assessing the relationship between maternal risk for attention deficit hyperactivity disorder and functional connectivity in their biological toddlers. Eur Psychiatry 2022; 65:e66. [PMID: 36226356 PMCID: PMC9641653 DOI: 10.1192/j.eurpsy.2022.2325] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with increased risk for poor educational attainment and compromised social integration. Currently, clinical diagnosis rarely occurs before school-age, despite behavioral signs of ADHD in very early childhood. There is no known brain biomarker for ADHD risk in children ages 2-3 years-old. METHODS The current study aimed to investigate the functional connectivity (FC) associated with ADHD risk in 70 children aged 2.5 and 3.5 years via functional near-infrared spectroscopy (fNIRS) in bilateral frontal and parietal cortices; regions involved in attentional and goal-directed cognition. Children were instructed to passively watch videos for approximately 5 min. Risk for ADHD in each child was assessed via maternal symptoms of ADHD, and brain data was evaluated for FC. RESULTS Higher risk for maternal ADHD was associated with lower FC in a left-sided parieto-frontal network. Further, the interaction between sex and risk for ADHD was significant, where FC reduction in a widespread bilateral parieto-frontal network was associated with higher risk in male, but not female, participants. CONCLUSIONS These findings suggest functional organization differences in the parietal-frontal network in toddlers at risk for ADHD; potentially advancing the understanding of the neural mechanisms underlying the development of ADHD.
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Affiliation(s)
- Anastasia Kerr-German
- Boys Town National Research Hospital, Center for Childhood Deafness, Language and Learning, Omaha, Nebraska68131, USA
| | - Stuart F. White
- Boys Town National Research Hospital, Institute for Human Neuroscience, Boys Town, Nebraska68010, USA
- Department of Pharmacology and Neuroscience, Creighton School of Medicine, Omaha, Nebraska68124, USA
| | - Hendrik Santosa
- Department of Radiology, University of Pittsburg, Pittsburg, Pennsylvania15260, USA
| | - Aaron T. Buss
- Department of Psychology, University of Tennessee, Knoxville, Tennessee37996, USA
| | - Gaelle E. Doucet
- Boys Town National Research Hospital, Institute for Human Neuroscience, Boys Town, Nebraska68010, USA
- Department of Pharmacology and Neuroscience, Creighton School of Medicine, Omaha, Nebraska68124, USA
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Stylianou O, Racz FS, Kim K, Kaposzta Z, Czoch A, Yabluchanskiy A, Eke A, Mukli P. Multifractal Functional Connectivity Analysis of Electroencephalogram Reveals Reorganization of Brain Networks in a Visual Pattern Recognition Paradigm. Front Hum Neurosci 2021; 15:740225. [PMID: 34733145 PMCID: PMC8558231 DOI: 10.3389/fnhum.2021.740225] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain consists of anatomically distant neuronal assemblies that are interconnected via a myriad of synapses. This anatomical network provides the neurophysiological wiring framework for functional connectivity (FC), which is essential for higher-order brain functions. While several studies have explored the scale-specific FC, the scale-free (i.e., multifractal) aspect of brain connectivity remains largely neglected. Here we examined the brain reorganization during a visual pattern recognition paradigm, using bivariate focus-based multifractal (BFMF) analysis. For this study, 58 young, healthy volunteers were recruited. Before the task, 3-3 min of resting EEG was recorded in eyes-closed (EC) and eyes-open (EO) states, respectively. The subsequent part of the measurement protocol consisted of 30 visual pattern recognition trials of 3 difficulty levels graded as Easy, Medium, and Hard. Multifractal FC was estimated with BFMF analysis of preprocessed EEG signals yielding two generalized Hurst exponent-based multifractal connectivity endpoint parameters, H(2) and ΔH 15; with the former indicating the long-term cross-correlation between two brain regions, while the latter captures the degree of multifractality of their functional coupling. Accordingly, H(2) and ΔH 15 networks were constructed for every participant and state, and they were characterized by their weighted local and global node degrees. Then, we investigated the between- and within-state variability of multifractal FC, as well as the relationship between global node degree and task performance captured in average success rate and reaction time. Multifractal FC increased when visual pattern recognition was administered with no differences regarding difficulty level. The observed regional heterogeneity was greater for ΔH 15 networks compared to H(2) networks. These results show that reorganization of scale-free coupled dynamics takes place during visual pattern recognition independent of difficulty level. Additionally, the observed regional variability illustrates that multifractal FC is region-specific both during rest and task. Our findings indicate that investigating multifractal FC under various conditions - such as mental workload in healthy and potentially in diseased populations - is a promising direction for future research.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | | | - Keumbi Kim
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Biochemistry and Molecular Biology, Oklahoma Center for Geroscience and Healthy Brain Aging, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,The Peggy and Charles Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,Department of Health Promotion Sciences, College of Public Health, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States,Andras Eke,
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary,Vascular Cognitive Impairment and Neurodegeneration Program, Department of Biochemistry and Molecular Biology, Oklahoma Center for Geroscience and Healthy Brain Aging, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,*Correspondence: Peter Mukli,
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Luo B, Lu Y, Qiu C, Dong W, Xue C, Zhang L, Liu W, Zhang W. Altered Spontaneous Neural Activity and Functional Connectivity in Parkinson's Disease With Subthalamic Microlesion. Front Neurosci 2021; 15:699010. [PMID: 34354566 PMCID: PMC8329380 DOI: 10.3389/fnins.2021.699010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Background Transient improvement in motor symptoms are immediately observed in patients with Parkinson's disease (PD) after an electrode has been implanted into the subthalamic nucleus (STN) for deep brain stimulation (DBS). This phenomenon is known as the microlesion effect (MLE). However, the underlying mechanisms of MLE is poorly understood. Purpose We utilized resting state functional MRI (rs-fMRI) to evaluate changes in spontaneous brain activity and networks in PD patients during the microlesion period after DBS. Method Overall, 37 PD patients and 13 gender- and age-matched healthy controls (HCs) were recruited for this study. Rs-MRI information was collected from PD patients three days before DBS and one day after DBS, whereas the HCs group was scanned once. We utilized the amplitude of low-frequency fluctuation (ALFF) method in order to analyze differences in spontaneous whole-brain activity among all subjects. Furthermore, functional connectivity (FC) was applied to investigate connections between other brain regions and brain areas with significantly different ALFF before and after surgery in PD patients. Result Relative to the PD-Pre-DBS group, the PD-Post-DBS group had higher ALFF in the right putamen, right inferior frontal gyrus, right precentral gyrus and lower ALFF in right angular gyrus, right precuneus, right posterior cingulate gyrus (PCC), left insula, left middle temporal gyrus (MTG), bilateral middle frontal gyrus and bilateral superior frontal gyrus (dorsolateral). Functional connectivity analysis revealed that these brain regions with significantly different ALFF scores demonstrated abnormal FC, largely in the temporal, prefrontal cortices and default mode network (DMN). Conclusion The subthalamic microlesion caused by DBS in PD was found to not only improve the activity of the basal ganglia-thalamocortical circuit, but also reduce the activity of the DMN and executive control network (ECN) related brain regions. Results from this study provide new insights into the mechanism of MLE.
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Affiliation(s)
- Bei Luo
- Department of Functional Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yue Lu
- Department of Functional Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chang Qiu
- Department of Functional Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Dong
- Department of Functional Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Li Zhang
- Department of Geriatrics, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenbin Zhang
- Department of Functional Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Nephew BC, Febo M, Cali R, Workman KP, Payne L, Moore CM, King JA, Lacreuse A. Robustness of sex-differences in functional connectivity over time in middle-aged marmosets. Sci Rep 2020; 10:16647. [PMID: 33024242 PMCID: PMC7538565 DOI: 10.1038/s41598-020-73811-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 09/14/2020] [Indexed: 02/07/2023] Open
Abstract
Nonhuman primates (NHPs) are an essential research model for gaining a comprehensive understanding of the neural mechanisms of neurocognitive aging in our own species. In the present study, we used resting state functional connectivity (rsFC) to investigate the relationship between prefrontal cortical and striatal neural interactions, and cognitive flexibility, in unanaesthetized common marmosets (Callithrix jacchus) at two time points during late middle age (8 months apart, similar to a span of 5-6 years in humans). Based on our previous findings, we also determine the reproducibility of connectivity measures over the course of 8 months, particularly previously observed sex differences in rsFC. Male marmosets exhibited remarkably similar patterns of stronger functional connectivity relative to females and greater cognitive flexibility between the two imaging time points. Network analysis revealed that the consistent sex differences in connectivity and related cognitive associations were characterized by greater node strength and/or degree values in several prefrontal, premotor and temporal regions, as well as stronger intra PFC connectivity, in males compared to females. The current study supports the existence of robust sex differences in prefrontal and striatal resting state networks that may contribute to differences in cognitive function and offers insight on the neural systems that may be compromised in cognitive aging and age-related conditions such as mild cognitive impairment and Alzheimer's disease.
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Affiliation(s)
- Benjamin C Nephew
- Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
- Center for Comparative Neuroimaging, University of Massachusetts Medical School, Worcester, MA, 01655, USA.
| | - Marcelo Febo
- Department of Psychiatry, University of Florida, Gainesville, FL, 32610, USA
| | - Ryan Cali
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Kathryn P Workman
- Psychological and Brain Sciences, University of Massachusetts, Amherst, MA, 01003, USA
| | - Laurellee Payne
- Center for Comparative Neuroimaging, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Constance M Moore
- Center for Comparative Neuroimaging, University of Massachusetts Medical School, Worcester, MA, 01655, USA
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Jean A King
- Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
- Center for Comparative Neuroimaging, University of Massachusetts Medical School, Worcester, MA, 01655, USA
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Agnès Lacreuse
- Psychological and Brain Sciences, University of Massachusetts, Amherst, MA, 01003, USA
- Neuroscience and Behavior Program, University of Massachusetts, Amherst, MA, 01003, USA
- Center for Neuroendocrine Studies, University of Massachusetts, Amherst, MA, 01003, USA
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