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Misaki M, Tsuchiyagaito A, Guinjoan SM, Rohan ML, Paulus MP. Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state. J Affect Disord 2023; 340:843-854. [PMID: 37582464 PMCID: PMC10528904 DOI: 10.1016/j.jad.2023.08.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/17/2023]
Abstract
Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process.
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Affiliation(s)
- Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA.
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Salvador M Guinjoan
- Laureate Institute for Brain Research, Tulsa, OK, USA; Department of Psychiatry, Oklahoma University Health Sciences Center at Tulsa, Tulsa, OK, USA
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2
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Winters DE, Guha A, Sakai JT. Connectome-based predictive modeling of empathy in adolescents with and without the low-prosocial emotion specifier. Neurosci Lett 2023; 812:137371. [PMID: 37406728 PMCID: PMC10528031 DOI: 10.1016/j.neulet.2023.137371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 07/02/2023] [Indexed: 07/07/2023]
Abstract
Empathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n = 29) and those that do not (n = 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps.
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Affiliation(s)
- Drew E Winters
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, United States.
| | - Anika Guha
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, United States
| | - Joseph T Sakai
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, United States
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3
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Yang H, Zhang J, Jin Z, Bashivan P, Li L. Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory. Brain Struct Funct 2023; 228:1479-1492. [PMID: 37349540 DOI: 10.1007/s00429-023-02666-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
Working memory plays a crucial role in our daily lives, and brain imaging has been used to predict working memory performance. Here, we present an improved connectome-based predictive modeling approach for building a predictive model of individual working memory performance from whole-brain functional connectivity. The model was built using n-back task-based fMRI and resting-state fMRI data from the Human Connectome Project. Compared to prior models, our model was more interpretable, demonstrated a closer connection to the known anatomical and functional network. The model also demonstrates strong generalization on nine other cognitive behaviors from the HCP database and can well predict the working memory performance of healthy individuals in external datasets. By comparing the differences in prediction effects of different brain networks and anatomical feature analysis on n-back tasks, we found the essential role of some networks in differentiating between high and low working memory loads conditions.
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Affiliation(s)
- Huayi Yang
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- Department of Physiology, McGill University, Montréal, QC, H3G 1Y6, Canada
| | - Junjun Zhang
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zhenlan Jin
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Pouya Bashivan
- Department of Physiology, McGill University, Montréal, QC, H3G 1Y6, Canada
- Mila, University of Montreal, Montréal, QC, H2S 3H1, Canada
| | - Ling Li
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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4
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Zúñiga RG, Davis JRC, Boyle R, De Looze C, Meaney JF, Whelan R, Kenny RA, Knight SP, Ortuño RR. Brain connectivity in frailty: Insights from The Irish Longitudinal Study on Ageing (TILDA). Neurobiol Aging 2023; 124:1-10. [PMID: 36680853 DOI: 10.1016/j.neurobiolaging.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia).
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Affiliation(s)
- Raquel Gutiérrez Zúñiga
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland.
| | - James R C Davis
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Rory Boyle
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Céline De Looze
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - James F Meaney
- Centre for Advanced Medical Imaging (CAMI), St James's Hospital, Dublin, Ireland
| | - Robert Whelan
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Mercer's Institute for Successful Ageing (MISA), St James's Hospital, Dublin, Ireland
| | - Silvin P Knight
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Román Romero Ortuño
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Mercer's Institute for Successful Ageing (MISA), St James's Hospital, Dublin, Ireland
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Hu B, Zhang R, Feng T. Seeing the future: Connectome strength and network efficiency in visual network predict individual ability of episodic future thinking. Neuropsychologia 2023; 179:108451. [PMID: 36535422 DOI: 10.1016/j.neuropsychologia.2022.108451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 11/10/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Episodic future thinking (EFT) refers to the critical ability that enables people to construct and pre-experience the vivid mental imagery about future events, which impacts on the decision-making for individuals and group. Although EFT is generally believed to have a visual nature by theorists, little neuroscience evidence has been provided to verify this assumption. Here, by employing the approach of connectome-based predictive modeling (CPM) and graph-theoretical analysis, we analyzed resting-state functional brain image from 191 participants to predict their variability of EFT ability (leave-one-out cross-validation), and validated the results by applying different parcellation schemas and feature selection thresholds. At the connectome strength level, CPM-based analysis revealed that EFT ability could be predicted by the connectome strength of visual network. Besides, at the network level, graph-theoretical analysis showed that EFT ability could be predicted by the network efficiency of visual network. Moreover, these findings were replicated using different parcellation schemas and feature selection thresholds. These results robustly and collectively supported that the visual network might be one of the neural substrates underlying EFT ability from a comprehensive perspective of resting-state functional connectivity strength and the neural network. This study provides indications on how the function of visual network supports EFT ability, and enhances our understanding of the EFT ability from a neural basis perspective.
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Kim E, Kim S, Kim Y, Cha H, Lee HJ, Lee T, Chang Y. Connectome-based predictive models using resting-state fMRI for studying brain aging. Exp Brain Res 2022; 240:2389-2400. [PMID: 35922524 DOI: 10.1007/s00221-022-06430-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
Abstract
Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.
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Affiliation(s)
- Eunji Kim
- Department of Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Seungho Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Yunheung Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hyunsil Cha
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hui Joong Lee
- Department of Radiology, Kyungpook National University School of Medicine, Daegu, Korea
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea
| | - Taekwan Lee
- Korea Brain Research Institute, Chumdanro 61, Dong-gu, Daegu, 41021, Republic of Korea.
| | - Yongmin Chang
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea.
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea.
- The Department of Molecular Medicine and Radiology, Kyungpook National University School of Medicine, 200 Dongduk-Ro Jung-Gu, Daegu, Korea.
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7
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Suo X, Zuo C, Lan H, Pan N, Zhang X, Kemp GJ, Wang S, Gong Q. COVID-19 vicarious traumatization links functional connectome to general distress. Neuroimage 2022; 255:119185. [PMID: 35398284 PMCID: PMC8986542 DOI: 10.1016/j.neuroimage.2022.119185] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 02/08/2023] Open
Abstract
As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, PR China,Corresponding authors at: Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian Province, PR China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, PR China,Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, PR China,Corresponding authors at: Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian Province, PR China
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Yao G, Wei L, Jiang T, Dong H, Baeken C, Wu GR. Neural mechanisms underlying empathy during alcohol abstinence: evidence from connectome-based predictive modeling. Brain Imaging Behav 2022; 16:2477-2486. [PMID: 35829876 DOI: 10.1007/s11682-022-00702-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/20/2022] [Accepted: 06/27/2022] [Indexed: 01/10/2023]
Abstract
Empathy impairments have been linked to alcohol dependence even during abstinent periods. Nonetheless, the neural underpinnings of abstinence-induced empathy deficits remain unclear. In this study, we employed connectome-based predictive modeling (CPM) by using whole brain resting-state functional connectivity (rs-FC) to predict empathy capability of abstinent alcoholics (n = 47) versus healthy controls (n = 59). In addition, the generalizability of the predictive model (i.e., one group treated as a training dataset and another one treated as a test dataset) was performed to determine whether healthy controls and abstinent alcoholics share common neural fingerprints of empathy. Our results showed that abstinent alcoholics relative to healthy controls had decreased empathy capacity. Although no predictive models were observed in the abstinence group, we found that individual empathy scores in the healthy group can be reliably predicted by functional connectivity from the default mode network (DMN) to the sensorimotor network (SMN), occipital network, and cingulo-opercular network (CON). Moreover, the identified connectivity fingerprints of healthy controls could be generalized to predict empathy in the abstinence group. These findings indicate that neural circuits accounting for empathy may be disrupted by alcohol use and the impaired degree varies greatly among abstinent individuals. The large inter-individual variation may impede identification of the predictive model of empathy in alcohol abstainers.
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Affiliation(s)
- Guanzhong Yao
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Luqing Wei
- School of Psychology, Jiangxi Normal University, Nanchang, China.
| | - Ting Jiang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hui Dong
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chris Baeken
- Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental Psychiatry (GHEP) lab, Ghent University, Ghent, Belgium.,Department of Psychiatry, University Hospital (UZBrussel), Brussels, Belgium.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China. .,Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental Psychiatry (GHEP) lab, Ghent University, Ghent, Belgium.
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Shi AP, Yu Y, Hu B, Li YT, Wang W, Cui GB. Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus. World J Diabetes 2022; 13:110-125. [PMID: 35211248 PMCID: PMC8855139 DOI: 10.4239/wjd.v13.i2.110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/10/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Large-scale functional connectivity (LSFC) patterns in the brain have unique intrinsic characteristics. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), and these patterns predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM, using connectome-based predictive modeling (CPM) and a support vector machine.
AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.
METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used a support vector machine based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the appearance of the classification.
RESULTS CPM could predict the MoCA scores in patients with T2DM (Pearson’s correlation coefficient between predicted and actual MoCA scores, r = 0.32, P=0.0066 [HOA-112 atlas]; r = 0.32, P=0.0078 [AAL-116 atlas]; r = 0.42, P=0.0038 [Power-264 atlas]), indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance; moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia.
CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.
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Affiliation(s)
- An-Ping Shi
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Ying Yu
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Bo Hu
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Yu-Ting Li
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Wen Wang
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
| | - Guang-Bin Cui
- Department of Radiology, Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, The Affiliated Tangdu Hospital of Air Force Medical University (Fourth Military Medical University), Xi'an 710038, Shaanxi Province, China
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Ye S, Zhu B, Zhao L, Tian X, Yang Q, Krueger F. Connectome-based model predicts individual psychopathic traits in college students. Neurosci Lett 2021; 769:136387. [PMID: 34883220 DOI: 10.1016/j.neulet.2021.136387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Psychopathic traits have been suggested to increase the risk of violations of socio-moral norms. Previous studies revealed that abnormal neural signatures are associated with elevated psychopathic traits; however, whether the intrinsic network architecture can predict psychopathic traits at the individual level remains unclear. METHODS The present study utilized connectome-based predictive modeling (CPM) to investigate whether whole-brain resting-state functional connectivity (RSFC) can predict psychopathic traits in the general population. Resting-state fMRI data were collected from 84 college students with varying psychopathic traits measured by the Levenson Self-Report Psychopathy Scale (LSRP). RESULTS Functional connections that were negatively correlated with psychopathic traits predicted individual differences in total LSRP and secondary psychopathy score but not primary score. Particularly, nodes with the most connections in the predictive connectome anchored in the prefrontal cortex (e.g., anterior prefrontal cortex and orbitofrontal cortex) and limbic system (e.g., anterior cingulate cortex and insula). In addition, the connections between the occipital network (OCCN) and cingulo-opercular network (CON) served as a significant predictive connectome for total LSRP and secondary psychopathy score. CONCLUSION CPM constituted by whole-brain RSFC significantly predicted psychopathic traits individually in the general population. The brain areas including the prefrontal cortex and limbic system and large-scale networks including the CON and OCCN play special roles in the predictive model-possibly reflecting atypical cognitive control and affective processing for individuals with elevated psychopathic traits. These findings may facilitate detection and potential intervention of individuals with maladaptive psychopathic tendency.
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Affiliation(s)
- Shuer Ye
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Bing Zhu
- School of Marxism, Zhejiang Yuexiu University, Shaoxing, China
| | - Lei Zhao
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China; Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Xuehong Tian
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Qun Yang
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA; Department of Psychology, University of Mannheim, Mannheim, Germany
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11
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Yang FN, Hassanzadeh-Behbahani S, Bronshteyn M, Dawson M, Kumar P, Moore DJ, Ellis RJ, Jiang X. Connectome-based prediction of global cognitive performance in people with HIV. Neuroimage Clin 2021; 30:102677. [PMID: 34215148 PMCID: PMC8102633 DOI: 10.1016/j.nicl.2021.102677] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/16/2021] [Accepted: 04/12/2021] [Indexed: 11/26/2022]
Abstract
Networks strengths predicted global cognitive performance in PWH. Model generalized to data from an independent PWH sample. Network strengths in PWH with HAND were different from either controls or PWH without HAND. Network strengths may serve as a potential biomarker to assist HAND diagnosis.
Global cognitive performance plays an important role in the diagnosis of HIV-associated neurocognitive disorders (HAND), yet to date, there is no simple way to measure global cognitive performance in people with HIV (PWH). Here, we performed connectome-based predictive modeling (CPM) to pursue a neural biomarker of global cognitive performance in PWH based on whole-brain resting-state functional connectivity. We built a CPM model that successfully predicted individual differences in global cognitive performance in the training set of 67 PWH by using leave-one-out cross-validation. This model generalized to both 33 novel PWH in the testing set and a subset of 39 PWH who completed a follow-up visit two years later. Furthermore, network strengths identified by the CPM model were significantly different between PWH with HAND and without HAND. Together, these results demonstrate that whole-brain functional network strengths could serve as a potential neural biomarker of global cognitive performance in PWH.
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Affiliation(s)
- Fan Nils Yang
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, United States.
| | | | - Margarita Bronshteyn
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, United States
| | - Matthew Dawson
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, United States
| | - Princy Kumar
- Department of Medicine, Georgetown University Medical Center, Washington, DC 20057, United States
| | - David J Moore
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, United States
| | - Ronald J Ellis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, United States; Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, United States
| | - Xiong Jiang
- Departments of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, United States
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12
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Jiang R, Calhoun VD, Cui Y, Qi S, Zhuo C, Li J, Jung R, Yang J, Du Y, Jiang T, Sui J. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav 2021; 14:1979-1993. [PMID: 31278651 DOI: 10.1007/s11682-019-00146-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center, Nankai University Affiliated Anding Hospital, Tianjin, 300222, China
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Rex Jung
- Department of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,University of Electronic Science and Technology of China, Chengdu, 610054, China.,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China.
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13
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Liu P, Yang W, Zhuang K, Wei D, Yu R, Huang X, Qiu J. The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic. Neurobiol Stress 2020; 14:100285. [PMID: 33385021 PMCID: PMC7772572 DOI: 10.1016/j.ynstr.2020.100285] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 11/26/2020] [Accepted: 12/12/2020] [Indexed: 12/05/2022] Open
Abstract
Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations. Perceived stress (PS) can be predicated by resting-state functional connectome. PS can be predicated by interaction between default model and salience network. Key nodes of the prediction model located in limbic systems and temporal lobe. psCPM of regular days generalized to predict PS level in the COVID-19 pandemic.
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Affiliation(s)
- Peiduo Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
- Research Center for Psychology and Social Development, Southwest University, Chongqing, 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
- Corresponding author. Faculty of Psychology, Southwest University, No.2 TianSheng Road, Beibei District, Chongqing, 400715, China.
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Rongjun Yu
- Department of Psychology, National University of Singapore, Singapore
| | - Xiting Huang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
- Corresponding author. Faculty of Psychology, Southwest University, No.2 TianSheng Road, Beibei District, Chongqing, 400715, China.
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14
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Rapuano KM, Rosenberg MD, Maza MT, Dennis NJ, Dorji M, Greene AS, Horien C, Scheinost D, Todd Constable R, Casey BJ. Behavioral and brain signatures of substance use vulnerability in childhood. Dev Cogn Neurosci 2020; 46:100878. [PMID: 33181393 PMCID: PMC7662869 DOI: 10.1016/j.dcn.2020.100878] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 09/17/2020] [Accepted: 10/28/2020] [Indexed: 12/23/2022] Open
Abstract
The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.
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Affiliation(s)
- Kristina M Rapuano
- Department of Psychology, Yale University, New Haven, CT, United States.
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Maria T Maza
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Nicholas J Dennis
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Mila Dorji
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, United States
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, United States; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - B J Casey
- Department of Psychology, Yale University, New Haven, CT, United States
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15
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Yu J, Rawtaer I, Fam J, Feng L, Kua EH, Mahendran R. The individualized prediction of cognitive test scores in mild cognitive impairment using structural and functional connectivity features. Neuroimage 2020; 223:117310. [PMID: 32861786 DOI: 10.1016/j.neuroimage.2020.117310] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 07/31/2020] [Accepted: 08/20/2020] [Indexed: 11/16/2022] Open
Abstract
Neuropsychological assessments are essential in diagnosing age-related neurocognitive disorders. However, they are lengthy in duration and can be unreliable at times. To this end, we explored a modified connectome-based predictive modeling approach to estimating individualized scores from multiple cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. Multi-shell HARDI and resting-state functional magnetic resonance imaging scans, and scores from 10 cognitive measures were acquired from 91 older adults with mild cognitive impairment. SC and FC matrices were derived from these scans and, in various combinations, entered into models along with demographic covariates to predict cognitive scores. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (rpredicted-observed). Across all cognitive measures, significant rpredicted-observed (0.402 to 0.654) were observed from the best-predicting models. Six of these models consisted of multimodal features. For three cognitive measures, their best-predicting models' rpredicted-observed were similar to that of a model that included only demographic covariates- suggesting that SC and/or FC features did not contribute significantly on top of demographics. Cross-prediction models revealed that the best-predicting models were similarly accurate in predicting scores of related cognitive measures- suggesting their limited specificity in predicting cognitive scores. Generally, multimodal connectomes together with demographics, can be exploited as sensitive markers, though with limited specificity, to predict cognitive performance across a spectrum in multiple cognitive domains. In certain situations, it may not be worthwhile to acquire neuroimaging data, considering that demographics alone can be similarly accurate in predicting cognitive scores.
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Affiliation(s)
- Junhong Yu
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore.
| | - Iris Rawtaer
- Department of Psychological Medicine, Sengkang General Hospital, 110 Sengkang E way, Singapore 544886, Singapore
| | - Johnson Fam
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Lei Feng
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ee-Heok Kua
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Rathi Mahendran
- Department of Psychological Medicine, Mind Science Centre, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore; Academic Development Department, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore.
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16
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Scheinost D, Hsu TW, Avery EW, Hampson M, Constable RT, Chun MM, Rosenberg MD. Connectome-based neurofeedback: A pilot study to improve sustained attention. Neuroimage 2020; 212:116684. [PMID: 32114151 DOI: 10.1016/j.neuroimage.2020.116684] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/23/2020] [Accepted: 02/24/2020] [Indexed: 12/27/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
| | - Tiffany W Hsu
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychology, University of Chicago, Chicago, IL, USA.
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17
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Henneghan AM, Gibbons C, Harrison RA, Edwards ML, Rao V, Blayney DW, Palesh O, Kesler SR. Predicting Patient Reported Outcomes of Cognitive Function Using Connectome-Based Predictive Modeling in Breast Cancer. Brain Topogr 2020; 33:135-142. [PMID: 31745689 PMCID: PMC8006573 DOI: 10.1007/s10548-019-00746-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 11/07/2019] [Indexed: 11/29/2022]
Abstract
Being able to predict who will likely experience cancer related cognitive impairment (CRCI) could enhance patient care and potentially reduce economic and human costs associated with this adverse event. We aimed to determine if post-treatment patient reported CRCI could also be predicted from baseline resting state fMRI in patients with breast cancer. 76 newly diagnosed patients (n = 42 planned for chemotherapy; n = 34 not planned for chemotherapy) and 50 healthy female controls were assessed at 3 times points [T1 (prior to treatment); T2 (1 month post chemotherapy); T3 (1 year after T2)], and at yoked intervals for controls. Data collection included self-reported executive dysfunction, memory function, and psychological distress and resting state fMRI data converted to connectome matrices for each participant. Statistical analyses included linear mixed modeling, independent t tests, and connectome-based predictive modeling (CPM). Executive dysfunction increased over time in the chemotherapy group and was stable in the other two groups (p < 0.001). Memory function decreased over time in both patient groups compared to controls (p < 0.001). CPM models successfully predicted executive dysfunction and memory function scores (r > 0.31, p < 0.002). Support vector regression with a radial basis function (SVR RBF) showed the highest performance for executive dysfunction and memory function (r = 0.68; r = 0.44, p's < 0.001). Baseline neuroimaging may be useful for predicting patient reported cognitive outcomes which could assist in identifying patients in need of surveillance and/or early intervention for treatment-related cognitive effects.
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Affiliation(s)
- Ashley M Henneghan
- School of Nursing, University of Texas at Austin, 1710 Red River St., Austin, TX, 78712, USA.
| | - Chris Gibbons
- PROVE Center, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, 02115, USA
| | - Rebecca A Harrison
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 431, Houston, TX, 77030, USA
| | - Melissa L Edwards
- Department of Family Medicine & Institute for Translational Research, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA
| | - Vikram Rao
- School of Nursing, University of Texas at Austin, 1710 Red River St., Austin, TX, 78712, USA
| | - Douglas W Blayney
- Associate Division Chief of Medical Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, CC-2219, Stanford, CA, 94305-5827, USA
| | - Oxana Palesh
- Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, 401 Quarry Road, Office 2318, Stanford, CA, 94305, USA
| | - Shelli R Kesler
- School of Nursing, University of Texas at Austin, 1710 Red River St., Austin, TX, 78712, USA
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18
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Yoo K, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. Neuroimage 2019; 197:212-223. [PMID: 31039408 DOI: 10.1016/j.neuroimage.2019.04.060] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 10/26/2022] Open
Abstract
Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.
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Affiliation(s)
| | | | - Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, USA; Interdepartmental Neuroscience Program, Yale University, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA
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19
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Chen P, Xie Q, Wu X, Huang H, Lv W, Chen L, Guo Y, Zhang S, Hu H, Wang Y, Nie Y, Yu R, Huang R. Abnormal Effective Connectivity of the Anterior Forebrain Regions in Disorders of Consciousness. Neurosci Bull 2018; 34:647-58. [PMID: 29959668 DOI: 10.1007/s12264-018-0250-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Accepted: 04/25/2018] [Indexed: 01/21/2023] Open
Abstract
A number of studies have indicated that disorders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections (ECs) between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients' outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.
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