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Wang M, Shen C, Liu X, Feng Z, Wang H, Han F, Xiao F. Executive function performance in children and adolescent patients with narcolepsy type 1. Sleep Med 2024; 119:342-351. [PMID: 38754344 DOI: 10.1016/j.sleep.2024.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE The executive function profile in patients with narcolepsy type 1 (NT1) has been mentioned; however, limited research exists on children and adolescent patients with NT1.This study aims to assess executive function in children and adolescent patients with NT1 in China, examine potential influencing factors and evaluate the short-term treatment effect on executive function. METHODS 53 NT1 patients (36 males, age 12.2 ± 3.4 years) and 37 healthy controls (23 males, age 12.2 ± 2.5 years) underwent self-reported measures assessing subjective sleepiness, depression, anxiety and sleep quality. A comprehensive neuropsychological test was administered to assess executive function domains, including processing speed, inhibitory control, cognitive flexibility and working memory. These assessments were repeated in NT1 patients after three-day regular drug treatment. RESULTS NT1 patients exhibited higher levels of excessive daytime sleepiness, depression, anxiety, and poor sleep quality compared to healthy controls. Patients showed impaired processing speed, inhibitory control and cognitive flexibility (p < 0.05), whereas working memory was unaffected (p > 0.05). Regression analysis revealed that parameters from sleep monitoring, such as sleep efficiency and sleep latency, were correlated with executive function performance after controlling for age, gender, and education years. The short-term treatment led to improvements in inhibitory control, cognitive flexibility, and working memory. CONCLUSION The findings showed that executive function was impaired among children and adolescent patients with NT1, which was associated with objective sleep parameters. Furthermore, this study emphasizes the necessity of neuropsychological assessments and early interventions among children and adolescent NT1 patients.
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Affiliation(s)
- Mengmeng Wang
- School of Nursing, Peking University, Beijing, China; Division of Sleep Medicine, Peking University People's Hospital, Beijing, China
| | - Chaoran Shen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xinran Liu
- School of Nursing, Peking University, Beijing, China; Division of Sleep Medicine, Peking University People's Hospital, Beijing, China
| | - Zhaoyan Feng
- Division of Sleep Medicine, Peking University People's Hospital, Beijing, China
| | - Huanhuan Wang
- School of Nursing, Peking University, Beijing, China; Division of Sleep Medicine, Peking University People's Hospital, Beijing, China
| | - Fang Han
- Division of Sleep Medicine, Peking University People's Hospital, Beijing, China.
| | - Fulong Xiao
- Division of Sleep Medicine, Peking University People's Hospital, Beijing, China.
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2
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Omidvarnia A, Sasse L, Larabi DI, Raimondo F, Hoffstaedter F, Kasper J, Dukart J, Petersen M, Cheng B, Thomalla G, Eickhoff SB, Patil KR. Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes. Commun Biol 2024; 7:771. [PMID: 38926486 PMCID: PMC11208538 DOI: 10.1038/s42003-024-06438-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.
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Affiliation(s)
- Amir Omidvarnia
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany.
| | - Leonard Sasse
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
- Max Planck School of Cognition, Stephanstrasse 1a, Leipzig, Germany
| | - Daouia I Larabi
- Department of Clinical and Developmental Neuropsychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands
| | - Federico Raimondo
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Jan Kasper
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Jürgen Dukart
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Marvin Petersen
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, Düsseldorf, 40225, Germany
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3
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Kraljević N, Langner R, Küppers V, Raimondo F, Patil KR, Eickhoff SB, Müller VI. Network and state specificity in connectivity-based predictions of individual behavior. Hum Brain Mapp 2024; 45:e26753. [PMID: 38864353 PMCID: PMC11167405 DOI: 10.1002/hbm.26753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 04/17/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
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Affiliation(s)
- Nevena Kraljević
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Vincent Küppers
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital CologneUniversity of CologneCologneGermany
| | - Federico Raimondo
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Kaustubh R. Patil
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Veronika I. Müller
- Institute of Systems Neuroscience, Medical Faculty and University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
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Wiersch L, Friedrich P, Hamdan S, Komeyer V, Hoffstaedter F, Patil KR, Eickhoff SB, Weis S. Sex classification from functional brain connectivity: Generalization to multiple datasets. Hum Brain Mapp 2024; 45:e26683. [PMID: 38647035 PMCID: PMC11034006 DOI: 10.1002/hbm.26683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 03/19/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Patrick Friedrich
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Sami Hamdan
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Vera Komeyer
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
- Department of Biology, Faculty of Mathematics and Natural SciencesHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Felix Hoffstaedter
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Kaustubh R. Patil
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Simon B. Eickhoff
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
| | - Susanne Weis
- Institute of Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine (INM‐7: Brain and Behaviour)Research Centre JülichJülichGermany
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Wiersch L, Friedrich P, Hamdan S, Komeyer V, Hoffstaedter F, Patil KR, Eickhoff SB, Weis S. Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.30.555495. [PMID: 37693374 PMCID: PMC10491190 DOI: 10.1101/2023.08.30.555495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.
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Affiliation(s)
- Lisa Wiersch
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Patrick Friedrich
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Sami Hamdan
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Vera Komeyer
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Department of Biology, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Susanne Weis
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
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6
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Krämer C, Stumme J, da Costa Campos L, Dellani P, Rubbert C, Caspers J, Caspers S, Jockwitz C. Prediction of cognitive performance differences in older age from multimodal neuroimaging data. GeroScience 2024; 46:283-308. [PMID: 37308769 PMCID: PMC10828156 DOI: 10.1007/s11357-023-00831-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/17/2023] [Indexed: 06/14/2023] Open
Abstract
Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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Kraljević N, Langner R, Küppers V, Raimondo F, Patil KR, Eickhoff SB, Müller VI. Network and State Specificity in Connectivity-Based Predictions of Individual Behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540387. [PMID: 37215048 PMCID: PMC10197703 DOI: 10.1101/2023.05.11.540387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.
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Affiliation(s)
- Nevena Kraljević
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Vincent Küppers
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
| | - Veronika I Müller
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf
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Heckner MK, Cieslik EC, Paas Oliveros LK, Eickhoff SB, Patil KR, Langner R. Predicting executive functioning from brain networks: modality specificity and age effects. Cereb Cortex 2023; 33:10997-11009. [PMID: 37782935 PMCID: PMC10646699 DOI: 10.1093/cercor/bhad338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 10/04/2023] Open
Abstract
Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.
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Affiliation(s)
- Marisa K Heckner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Lya K Paas Oliveros
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40204 Düsseldorf, Germany
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9
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Paas Oliveros LK, Cieslik EC, Pieczykolan A, Pläschke RN, Eickhoff SB, Langner R. Brain functional characterization of response-code conflict in dual-tasking and its modulation by age. Cereb Cortex 2023; 33:10155-10180. [PMID: 37540164 PMCID: PMC10502578 DOI: 10.1093/cercor/bhad273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 08/05/2023] Open
Abstract
Crosstalk between conflicting response codes contributes to interference in dual-tasking, an effect exacerbated in advanced age. Here, we investigated (i) brain activity correlates of such response-code conflicts, (ii) activity modulations by individual dual-task performance and related cognitive abilities, (iii) task-modulated connectivity within the task network, and (iv) age-related differences in all these aspects. Young and older adults underwent fMRI while responding to the pitch of tones through spatially mapped speeded button presses with one or two hands concurrently. Using opposing stimulus-response mappings between hands, we induced conflict between simultaneously activated response codes. These response-code conflicts elicited activation in key regions of the multiple-demand network. While thalamic and parietal areas of the conflict-related network were modulated by attentional, working-memory and task-switching abilities, efficient conflict resolution in dual-tasking mainly relied on increasing supplementary motor activity. Older adults showed non-compensatory hyperactivity in left superior frontal gyrus, and higher right premotor activity was modulated by working-memory capacity. Finally, connectivity between premotor or parietal seed regions and the conflict-sensitive network was neither conflict-specific nor age-sensitive. Overall, resolving dual-task response-code conflict recruited substantial parts of the multiple-demand network, whose activity and coupling, however, were only little affected by individual differences in task performance or age.
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Affiliation(s)
- Lya K Paas Oliveros
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Forschungszentrum Jülich, Jülich 52425, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Forschungszentrum Jülich, Jülich 52425, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Aleks Pieczykolan
- Rheinische Fachhochschule – University of Applied Sciences, Cologne 50923, Germany
| | - Rachel N Pläschke
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Forschungszentrum Jülich, Jülich 52425, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Forschungszentrum Jülich, Jülich 52425, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
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10
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Wu J, Li J, Eickhoff SB, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nat Hum Behav 2023; 7:1255-1264. [PMID: 37524932 DOI: 10.1038/s41562-023-01670-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/27/2023] [Indexed: 08/02/2023]
Abstract
Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.
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Affiliation(s)
- Jianxiao Wu
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, New Haven, CT, USA
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
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11
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Heckner MK, Cieslik EC, Oliveros LKP, Eickhoff SB, Patil KR, Langner R. Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.29.547036. [PMID: 37425780 PMCID: PMC10327061 DOI: 10.1101/2023.06.29.547036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.
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Affiliation(s)
- Marisa K. Heckner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Edna C. Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lya K. Paas Oliveros
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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12
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Heckner MK, Cieslik EC, Patil KR, Gell M, Eickhoff SB, Hoffstädter F, Langner R. Predicting executive functioning from functional brain connectivity: network specificity and age effects. Cereb Cortex 2023; 33:6495-6507. [PMID: 36635227 PMCID: PMC10233269 DOI: 10.1093/cercor/bhac520] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 01/14/2023] Open
Abstract
Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.
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Affiliation(s)
- Marisa K Heckner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Edna C Cieslik
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Martin Gell
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Felix Hoffstädter
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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13
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Ahveninen J, Uluç I, Raij T, Nummenmaa A, Mamashli F. Spectrotemporal content of human auditory working memory represented in functional connectivity patterns. Commun Biol 2023; 6:294. [PMID: 36941477 PMCID: PMC10027691 DOI: 10.1038/s42003-023-04675-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 03/07/2023] [Indexed: 03/23/2023] Open
Abstract
Recent research suggests that working memory (WM), the mental sketchpad underlying thinking and communication, is maintained by multiple regions throughout the brain. Whether parts of a stable WM representation could be distributed across these brain regions is, however, an open question. We addressed this question by examining the content-specificity of connectivity-pattern matrices between subparts of cortical regions-of-interest (ROI). These connectivity patterns were calculated from functional MRI obtained during a ripple-sound auditory WM task. Statistical significance was assessed by comparing the decoding results to a null distribution derived from a permutation test considering all comparable two- to four-ROI connectivity patterns. Maintained WM items could be decoded from connectivity patterns across ROIs in frontal, parietal, and superior temporal cortices. All functional connectivity patterns that were specific to maintained sound content extended from early auditory to frontoparietal cortices. Our results demonstrate that WM maintenance is supported by content-specific patterns of functional connectivity across different levels of cortical hierarchy.
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Affiliation(s)
- Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
- Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Işıl Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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14
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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15
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Krämer C, Stumme J, da Costa Campos L, Rubbert C, Caspers J, Caspers S, Jockwitz C. Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach. Netw Neurosci 2023; 7:122-147. [PMID: 37339286 PMCID: PMC10270720 DOI: 10.1162/netn_a_00275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/22/2022] [Indexed: 09/22/2023] Open
Abstract
Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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16
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Wu J, Li J, Eickhoff SB, Hoffstaedter F, Hanke M, Yeo BTT, Genon S. Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns. Neuroimage 2022; 262:119569. [PMID: 35985618 PMCID: PMC9611632 DOI: 10.1016/j.neuroimage.2022.119569] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/04/2022] [Accepted: 08/15/2022] [Indexed: 11/20/2022] Open
Abstract
An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.
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Affiliation(s)
- Jianxiao Wu
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany.
| | - Jingwei Li
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Michael Hanke
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Sarah Genon
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany.
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17
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Turkish Version of the Working Memory Questionnaire: Reliability and Validity. JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.1003491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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18
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Spreng RN, Turner GR. From exploration to exploitation: a shifting mental mode in late life development. Trends Cogn Sci 2021; 25:1058-1071. [PMID: 34593321 PMCID: PMC8844884 DOI: 10.1016/j.tics.2021.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 12/31/2022]
Abstract
Changes in cognition, affect, and brain function combine to promote a shift in the nature of mentation in older adulthood, favoring exploitation of prior knowledge over exploratory search as the starting point for thought and action. Age-related exploitation biases result from the accumulation of prior knowledge, reduced cognitive control, and a shift toward affective goals. These are accompanied by changes in cortical networks, as well as attention and reward circuits. By incorporating these factors into a unified account, the exploration-to-exploitation shift offers an integrative model of cognitive, affective, and brain aging. Here, we review evidence for this model, identify determinants and consequences, and survey the challenges and opportunities posed by an exploitation-biased mental mode in later life.
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Affiliation(s)
- R Nathan Spreng
- Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada; Departments of Psychiatry and Psychology, McGill University, Montreal, QC H3A 0G4, Canada.
| | - Gary R Turner
- Department of Psychology, York University, Toronto, ON M3J 1P3, Canada
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19
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Bagattini C, Brignani D, Bonnì S, Quattrini G, Gasparotti R, Pievani M. Functional Imaging to Guide Network-Based TMS Treatments: Toward a Tailored Medicine Approach in Alzheimer's Disease. Front Neurosci 2021; 15:687493. [PMID: 34290585 PMCID: PMC8287201 DOI: 10.3389/fnins.2021.687493] [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: 03/29/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022] Open
Abstract
A growing number of studies is using fMRI-based connectivity to guide transcranial magnetic stimulation (TMS) target identification in both normal and clinical populations. TMS has gained increasing attention as a potential therapeutic strategy also in Alzheimer's disease (AD), but an endorsed target localization strategy in this population is still lacking. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability. Single-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality. The distance between individual fMRI-derived coordinates and traditionally defined targets was significant for a supposed TMS focality of 12 mm and in some cases up to 20 mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets. The proposed network-based fMRI-guided TMS approach, while accounting for inter-individual functional variability, allows to target core AD networks, and might thus represent a step toward tailored TMS interventions for AD.
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Affiliation(s)
- Chiara Bagattini
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Debora Brignani
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sonia Bonnì
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Giulia Quattrini
- Laboratory Alzheimer’s Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Roberto Gasparotti
- Neuroradiology Unit, ASST Spedali Civili Hospital, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer’s Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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