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Gui A, Throm E, da Costa PF, Penza F, Aguiló Mayans M, Jordan-Barros A, Haartsen R, Leech R, Jones EJH. Neuroadaptive Bayesian optimisation to study individual differences in infants' engagement with social cues. Dev Cogn Neurosci 2024; 68:101401. [PMID: 38870603 DOI: 10.1016/j.dcn.2024.101401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
Infants' motivation to engage with the social world depends on the interplay between individual brain's characteristics and previous exposure to social cues such as the parent's smile or eye contact. Different hypotheses about why specific combinations of emotional expressions and gaze direction engage children have been tested with group-level approaches rather than focusing on individual differences in the social brain development. Here, a novel Artificial Intelligence-enhanced brain-imaging approach, Neuroadaptive Bayesian Optimisation (NBO), was applied to infant electro-encephalography (EEG) to understand how selected neural signals encode social cues in individual infants. EEG data from 42 6- to 9-month-old infants looking at images of their parent's face were analysed in real-time and used by a Bayesian Optimisation algorithm to identify which combination of the parent's gaze/head direction and emotional expression produces the strongest brain activation in the child. This individualised approach supported the theory that the infant's brain is maximally engaged by communicative cues with a negative valence (angry faces with direct gaze). Infants attending preferentially to faces with direct gaze had increased positive affectivity and decreased negative affectivity. This work confirmed that infants' attentional preferences for social cues are heterogeneous and shows the NBO's potential to study diversity in neurodevelopmental trajectories.
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Affiliation(s)
- A Gui
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom; Department of Psychology, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.
| | - E Throm
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - P F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and, Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - F Penza
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - M Aguiló Mayans
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - A Jordan-Barros
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - R Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - R Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and, Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - E J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
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Kurtin DL, Araña‐Oiarbide G, Lorenz R, Violante IR, Hampshire A. Planning ahead: Predictable switching recruits task-active and resting-state networks. Hum Brain Mapp 2023; 44:5030-5046. [PMID: 37471699 PMCID: PMC10502652 DOI: 10.1002/hbm.26430] [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: 01/29/2023] [Revised: 06/08/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023] Open
Abstract
Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.
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Affiliation(s)
- Danielle L. Kurtin
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
- Department of Brain Sciences, Faculty of MedicineImperial College LondonLondonUK
| | | | - Romy Lorenz
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- The Poldrack LabStanford UniversityStanfordCaliforniaUSA
- Department of NeurophysicsMax‐Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Ines R. Violante
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Adam Hampshire
- Department of Brain Sciences, Faculty of MedicineImperial College LondonLondonUK
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3
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Gao Y, Guo X, Wang S, Huang Z, Zhang B, Hong J, Zhong Y, Weng C, Wang H, Zha Y, Sun J, Lu L, Wang G. Frontoparietal network homogeneity as a biomarker for mania and remitted bipolar disorder and a predictor of early treatment response in bipolar mania patient. J Affect Disord 2023; 339:486-494. [PMID: 37437732 DOI: 10.1016/j.jad.2023.07.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 06/13/2023] [Accepted: 07/08/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVE Previous studies have revealed the frontoparietal network (FPN) plays a key role in the imaging pathophysiology of bipolar disorder (BD). However, network homogeneity (NH) in the FPN among bipolar mania (BipM), remitted bipolar disorder (rBD), and healthy controls (HCs) remains unknown. The present study aimed to explore whether NH within the FPN can be used as an imaging biomarker to differentiate BipM from rBD and to predict treatment efficacy for patients with BipM. METHODS Sixty-six patients with BD (38 BipM and 28 rBD) and 60 HCs participated in resting-state functional magnetic resonance imaging and neuropsychological tests. Independent component analysis and NH analysis were applied to analyze the imaging data. RESULTS Relative to HCs, BipM patients displayed increased NH in the left middle frontal gyrus (MFG), and rBD patients displayed increased NH in the right inferior parietal lobule (IPL). Compared to rBD patients, BipM patients displayed reduced NH in the right IPL. Furthermore, support vector machine results exhibited that NH values in the right IPL could distinguish BipM patients from rBD patients with 69.70 %, 57.89 %, and 91.67 % for accuracy, sensitivity, and specificity, respectively, and support vector regression results exhibited a significant association between predicted and actual symptomatic improvement based on the reduction ratio of the Young` Mania Rating Scale total scores (r = 0.466, p < 0.01). CONCLUSION The study demonstrated distinct NH values in the FPN could serve as a valuable neuroimaging biomarker capable of differentiating patients with BipM and rBD, and NH values of the left MFG as a potential predictor of early treatment response in patients with BipM.
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Affiliation(s)
- Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China; Clinical and Translational Sciences Lab, The Douglas Research Centre, McGill University, Montreal, Canada
| | - Xin Guo
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Sanwang Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhengyuan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Baoli Zhang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiayu Hong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yi Zhong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China; Department of Neuroscience, City University of Hong Kong, Hong Kong, China
| | - Chao Weng
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Haibo Wang
- Department of Medical Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunfei Zha
- Department of Medical Imaging, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Peking University, Beijing, China.
| | - Lin Lu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China; National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
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Throm E, Gui A, Haartsen R, da Costa PF, Leech R, Jones EJH. Real-time monitoring of infant theta power during naturalistic social experiences. Dev Cogn Neurosci 2023; 63:101300. [PMID: 37741087 PMCID: PMC10523417 DOI: 10.1016/j.dcn.2023.101300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/30/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Infant-directed speech and direct gaze are important social cues that shape infant's attention to their parents. Traditional methods for probing their effect on infant attention involve a small number of pre-selected screen-based stimuli, which do not capture the complexity of real-world interactions. Here, we used neuroadaptive Bayesian Optimization (NBO) to search a large 'space' of different naturalistic social experiences that systematically varied in their visual (gaze direct to averted) and auditory properties (infant directed speech to nonvocal sounds). We measured oscillatory brain responses (relative theta power) during episodes of naturalistic social experiences in 57 typically developing 6- to 12-month-old infants. Relative theta power was used as input to the NBO algorithm to identify the naturalistic social context that maximally elicited attention in each individual infant. Results showed that individual infants were heterogeneous in the stimulus that elicited maximal theta with no overall stronger attention for direct gaze or infant-directed speech; however, individual differences in attention towards averted gaze were related to interpersonal skills and greater likelihood of preferring speech and direct gaze was observed in infants whose parents showed more positive affect. Our work indicates NBO may be a fruitful method for probing the role of distinct social cues in eliciting attention in naturalistic social contexts at the individual level.
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Affiliation(s)
- Elena Throm
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London WC1E 7HX, United Kingdom
| | - Anna Gui
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London WC1E 7HX, United Kingdom
| | - Rianne Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, TodderLab, Malet Street, London WC1E 7HX, United Kingdom
| | - Pedro F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London WC1E 7HX, United Kingdom.
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5
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Wass S, Jones EJH. Editorial perspective: Leaving the baby in the bathwater in neurodevelopmental research. J Child Psychol Psychiatry 2023; 64:1256-1259. [PMID: 36597852 DOI: 10.1111/jcpp.13750] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/11/2022] [Indexed: 01/05/2023]
Abstract
Neurodevelopmental conditions are characterised by differences in the way children interact with the people and environments around them. Despite extensive investigation, attempts to uncover the brain mechanisms that underpin neurodevelopmental conditions have yet to yield any translatable insights. We contend that one key reason is that psychologists and cognitive neuroscientists study brain function by taking children away from their environment, into a controlled lab setting. Here, we discuss recent research that has aimed to take a different approach, moving away from experimental control through isolation and stimulus manipulation, and towards approaches that embrace the measurement and targeted interrogation of naturalistic, user-defined and complex, multivariate datasets. We review three worked examples (of stress processing, early activity level in ADHD and social brain development in autism) to illustrate how these new approaches might lead to new conceptual and translatable insights into neurodevelopment.
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Affiliation(s)
- Sam Wass
- School of Psychology, University of East London, London, UK
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, London, UK
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6
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Kumar VJ, Beckmann CF, Scheffler K, Grodd W. Relay and higher-order thalamic nuclei show an intertwined functional association with cortical-networks. Commun Biol 2022; 5:1187. [PMID: 36333448 PMCID: PMC9636420 DOI: 10.1038/s42003-022-04126-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Almost all functional processing in the cortex strongly depends on thalamic interactions. However, in terms of functional interactions with the cerebral cortex, the human thalamus nuclei still partly constitute a terra incognita. Hence, for a deeper understanding of thalamic-cortical cooperation, it is essential to know how the different thalamic nuclei are associated with cortical networks. The present work examines network-specific connectivity and task-related topical mapping of cortical areas with the thalamus. The study finds that the relay and higher-order thalamic nuclei show an intertwined functional association with different cortical networks. In addition, the study indicates that relay-specific thalamic nuclei are not only involved with relay-specific behavior but also in higher-order functions. The study enriches our understanding of interactions between large-scale cortical networks and the thalamus, which may interest a broader audience in neuroscience and clinical research.
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Affiliation(s)
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Klaus Scheffler
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical MagneticResonance, University Hospital Tübingen, Tübingen, Germany
| | - Wolfgang Grodd
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
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7
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Abstract
For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.
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8
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Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
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9
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Ouyang G, Dien J, Lorenz R. Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration. J Neural Eng 2022; 19. [PMID: 34902847 DOI: 10.1088/1741-2552/ac42b6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/13/2021] [Indexed: 02/02/2023]
Abstract
Objective.Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research.Approach.We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio.Main results.SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI.Significance.The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.
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Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Joseph Dien
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States of America
| | - Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Psychology, Stanford University, Stanford, CA, United States of America
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10
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Frey D, Shin JH, Musco C, Modestino MA. Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research. REACT CHEM ENG 2022. [DOI: 10.1039/d2re00005a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A method combining information from both experiments and physics-based models is used to improve experimental Bayesian optimization.
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Affiliation(s)
- Daniel Frey
- Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, 6 Metrotech Center, Brooklyn, NY, USA
| | - Ju Hee Shin
- Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, 6 Metrotech Center, Brooklyn, NY, USA
| | - Christopher Musco
- Computer Science and Engineering, Tandon School of Engineering, New York University, 370 Jay Street, Brooklyn, NY, USA
| | - Miguel A. Modestino
- Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University, 6 Metrotech Center, Brooklyn, NY, USA
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11
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Frey D, Neyerlin KC, Modestino MA. Bayesian optimization of electrochemical devices for electrons-to-molecules conversions: the case of pulsed CO 2 electroreduction. REACT CHEM ENG 2022. [DOI: 10.1039/d2re00285j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Bayesian optimization (BO) was implemented to improve a membrane electrode assembly CO2 electroreduction device undergoing pulsed operation.
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Affiliation(s)
- Daniel Frey
- New York University, Tandon School of Engineering, Department of Chemical and Biomolecular Engineering, 6 Metrotech Center, Brooklyn, NY, USA
| | - K. C. Neyerlin
- National Renewable Energy Laboratory, Chemistry and Nanoscience Center, Golden, CO, USA
| | - Miguel A. Modestino
- New York University, Tandon School of Engineering, Department of Chemical and Biomolecular Engineering, 6 Metrotech Center, Brooklyn, NY, USA
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12
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The Modulation of Working-Memory Performance Using Gamma-Electroacupuncture and Theta-Electroacupuncture in Healthy Adults. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2021; 2021:2062718. [PMID: 34824588 PMCID: PMC8610651 DOI: 10.1155/2021/2062718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 10/07/2021] [Accepted: 10/26/2021] [Indexed: 11/17/2022]
Abstract
Working memory (WM), a central component of general cognition, plays an essential role in human beings' daily lives. WM impairments often occur in psychiatric, neurodegenerative, and neurodevelopmental disorders, mainly presenting as loss of high-load WM. In previous research, electroacupuncture (EA) has been shown to be an effective treatment for cognitive impairments. Frequency parameters are an important factor in therapeutic results, but the optimal frequency parameters of EA have not yet been identified. In this study, we chose theta-EA (θ-EA; 6 Hz) and gamma-EA (γ-EA; 40 Hz), corresponding to the transcranial alternating-current stimulation (tACS) frequency parameters at the Baihui (DU20) and Shenting (DU24) acupoints, in order to compare the effects of different EA frequencies on WM. We evaluated WM performance using visual 1-back, 2-back, and 3-back WM tasks involving digits. Each participant (N = 30) attended three different sessions in accordance with a within-subject crossover design. We performed θ-EA, γ-EA, and sham-EA in a counterbalanced order, conducting the WM task both before and after intervention. The results showed that d-prime (d′) under all three stimulation conditions had no significance in the 1-back and 2-back tasks. However, in the 3-back task, there was a significant improvement in d′ after intervention compared to d′ before intervention under θ-EA (F [1, 29] = 22.64; P < 0.001), while we saw no significant difference in the γ-EA and sham-EA groups. Reaction times for hits (RT-hit) under all three stimulation conditions showed decreasing trends in 1-, 2-, and 3-back tasks but without statistically significant differences. These findings suggest that the application of θ-EA might facilitate high-load WM performance.
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13
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Lorenz R, Johal M, Dick F, Hampshire A, Leech R, Geranmayeh F. A Bayesian optimization approach for rapidly mapping residual network function in stroke. Brain 2021; 144:2120-2134. [PMID: 33725125 PMCID: PMC8370405 DOI: 10.1093/brain/awab109] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 11/16/2022] Open
Abstract
Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional 'one-size-fits-all' approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.
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Affiliation(s)
- Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
- Stanford University, Stanford, CA 94305, USA
- Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
| | - Michelle Johal
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - Frederic Dick
- Birkbeck/UCL Centre for Neuroimaging, Birkbeck University, London WC1H 0AP, UK
| | - Adam Hampshire
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - Robert Leech
- Centre for Neuroimaging Science, King’s College London, London SE5 8AF, UK
| | - Fatemeh Geranmayeh
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK
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14
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Carr SJA, Chen W, Fondran J, Friel H, Sanchez-Gonzalez J, Zhang J, Tatsuoka C. Early Stopping in Experimentation With Real-Time Functional Magnetic Resonance Imaging Using a Modified Sequential Probability Ratio Test. Front Neurosci 2021; 15:643740. [PMID: 34803577 PMCID: PMC8600259 DOI: 10.3389/fnins.2021.643740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 10/13/2021] [Indexed: 11/22/2022] Open
Abstract
Introduction: Functional magnetic resonance imaging (fMRI) often involves long scanning durations to ensure the associated brain activity can be detected. However, excessive experimentation can lead to many undesirable effects, such as from learning and/or fatigue effects, discomfort for the subject, excessive motion artifacts and loss of sustained attention on task. Overly long experimentation can thus have a detrimental effect on signal quality and accurate voxel activation detection. Here, we propose dynamic experimentation with real-time fMRI using a novel statistically driven approach that invokes early stopping when sufficient statistical evidence for assessing the task-related activation is observed. Methods: Voxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLMs) were implemented on fMRI scans of a mathematical 1-back task from 12 healthy teenage subjects and 11 teenage subjects born extremely preterm (EPT). This approach is based on likelihood ratios and allows for systematic early stopping based on target statistical error thresholds. We adopt a two-stage estimation approach that allows for accurate estimates of GLM parameters before stopping is considered. Early stopping performance is reported for different first stage lengths, and activation results are compared with full durations. Finally, group comparisons are conducted with both early stopped and full duration scan data. Numerical parallelization was employed to facilitate completion of computations involving a new scan within every repetition time (TR). Results: Use of SPRT demonstrates the feasibility and efficiency gains of automated early stopping, with comparable activation detection as with full protocols. Dynamic stopping of stimulus administration was achieved in around half of subjects, with typical time savings of up to 33% (4 min on a 12 min scan). A group analysis produced similar patterns of activity for control subjects between early stopping and full duration scans. The EPT group, individually, demonstrated more variability in location and extent of the activations compared to the normal term control group. This was apparent in the EPT group results, reflected by fewer and smaller clusters. Conclusion: A systematic statistical approach for early stopping with real-time fMRI experimentation has been implemented. This dynamic approach has promise for reducing subject burden and fatigue effects.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
| | - Weicong Chen
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Jeremy Fondran
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Harry Friel
- Philips Healthcare, Highland Heights, OH, United States
| | | | - Jing Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
- *Correspondence: Curtis Tatsuoka,
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15
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Atypical Development of Attentional Control Associates with Later Adaptive Functioning, Autism and ADHD Traits. J Autism Dev Disord 2020; 50:4085-4105. [PMID: 32221749 PMCID: PMC7557503 DOI: 10.1007/s10803-020-04465-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Autism is frequently associated with difficulties with top-down attentional control, which impact on individuals’ mental health and quality of life. The developmental processes involved in these attentional difficulties are not well understood. Using a data-driven approach, 2 samples (N = 294 and 412) of infants at elevated and typical likelihood of autism were grouped according to profiles of parent report of attention at 10, 15 and 25 months. In contrast to the normative profile of increases in attentional control scores between infancy and toddlerhood, a minority (7–9%) showed plateauing attentional control scores between 10 and 25 months. Consistent with pre-registered hypotheses, plateaued growth of attentional control was associated with elevated autism and ADHD traits, and lower adaptive functioning at age 3 years.
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16
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Hernández SE, Dorta R, Suero J, Barros-Loscertales A, González-Mora JL, Rubia K. Larger whole brain grey matter associated with long-term Sahaja Yoga Meditation: A detailed area by area comparison. PLoS One 2020; 15:e0237552. [PMID: 33370272 PMCID: PMC7769288 DOI: 10.1371/journal.pone.0237552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 12/05/2020] [Indexed: 12/30/2022] Open
Abstract
Objectives Our previous study showed that long-term practitioners of Sahaja Yoga Meditation (SYM) had around 7% larger grey matter volume (GMV) in the whole brain compared with healthy controls; however, when testing individual regions, only 5 small brain areas were statistically different between groups. Under the hypothesis that those results were statistically conservative, with the same dataset, we investigated in more detail the regional differences in GMV associated with the practice of SYM, with a different statistical approach. Design Twenty-three experienced practitioners of SYM and 23 healthy non-meditators matched on age, sex and education level, were scanned using structural magnetic resonance imaging (MRI). Their GMV were extracted and compared using Voxel-Based Morphometry (VBM). Using a novel ad-hoc general linear model, statistical comparisons were made to observe if the GMV differences between meditators and controls were statistically significant. Results In the 16 lobe area subdivisions, GMV was statistically significantly different in 4 out of 16 areas: in right hemispheric temporal and frontal lobes, left frontal lobe and brainstem. In the 116 AAL area subdivisions, GMV difference was statistically significant in 11 areas. The GMV differences were statistically more significant in right hemispheric brain areas. Conclusions The study shows that long-term practice of SYM is associated with larger GMV overall, and with significant differences mainly in temporal and frontal areas of the right hemisphere and the brainstem. These neuroplastic changes may reflect emotional and attentional control mechanisms developed with SYM. On the other hand, our statistical ad-hoc method shows that there were more brain areas with statistical significance compared to the traditional methodology which we think is susceptible to conservative Type II errors.
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Affiliation(s)
| | - Roberto Dorta
- Department of Matemáticas, Estadística e Investigación Operativa, Universidad de La Laguna, Tenerife, Spain
| | - José Suero
- Centro de Salud Jazmín, Sermas, Madrid, Spain
| | | | | | - Katya Rubia
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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17
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Jolly AE, Scott GT, Sharp DJ, Hampshire AH. Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury. Brain 2020; 143:1158-1176. [PMID: 32243506 PMCID: PMC7174032 DOI: 10.1093/brain/awaa067] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 12/10/2019] [Accepted: 01/25/2020] [Indexed: 12/21/2022] Open
Abstract
It is well established that chronic cognitive problems after traumatic brain injury relate to diffuse axonal injury and the consequent widespread disruption of brain connectivity. However, the pattern of diffuse axonal injury varies between patients and they have a correspondingly heterogeneous profile of cognitive deficits. This heterogeneity is poorly understood, presenting a non-trivial challenge for prognostication and treatment. Prominent amongst cognitive problems are deficits in working memory and reasoning. Previous functional MRI in controls has associated these aspects of cognition with distinct, but partially overlapping, networks of brain regions. Based on this, a logical prediction is that differences in the integrity of the white matter tracts that connect these networks should predict variability in the type and severity of cognitive deficits after traumatic brain injury. We use diffusion-weighted imaging, cognitive testing and network analyses to test this prediction. We define functionally distinct subnetworks of the structural connectome by intersecting previously published functional MRI maps of the brain regions that are activated during our working memory and reasoning tasks, with a library of the white matter tracts that connect them. We examine how graph theoretic measures within these subnetworks relate to the performance of the same tasks in a cohort of 92 moderate-severe traumatic brain injury patients. Finally, we use machine learning to determine whether cognitive performance in patients can be predicted using graph theoretic measures from each subnetwork. Principal component analysis of behavioural scores confirm that reasoning and working memory form distinct components of cognitive ability, both of which are vulnerable to traumatic brain injury. Critically, impairments in these abilities after traumatic brain injury correlate in a dissociable manner with the information-processing architecture of the subnetworks that they are associated with. This dissociation is confirmed when examining degree centrality measures of the subnetworks using a canonical correlation analysis. Notably, the dissociation is prevalent across a number of node-centric measures and is asymmetrical: disruption to the working memory subnetwork relates to both working memory and reasoning performance whereas disruption to the reasoning subnetwork relates to reasoning performance selectively. Machine learning analysis further supports this finding by demonstrating that network measures predict cognitive performance in patients in the same asymmetrical manner. These results accord with hierarchical models of working memory, where reasoning is dependent on the ability to first hold task-relevant information in working memory. We propose that this finer grained information may be useful for future applications that attempt to predict long-term outcomes or develop tailored therapies.
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Affiliation(s)
- Amy E Jolly
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Burlington Danes Building, Hammersmith Campus, Imperial College London, Du Cane Road, London W12 ONN, UK
| | - Gregory T Scott
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Burlington Danes Building, Hammersmith Campus, Imperial College London, Du Cane Road, London W12 ONN, UK
| | - David J Sharp
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Burlington Danes Building, Hammersmith Campus, Imperial College London, Du Cane Road, London W12 ONN, UK
| | - Adam H Hampshire
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Burlington Danes Building, Hammersmith Campus, Imperial College London, Du Cane Road, London W12 ONN, UK
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18
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Monti RP, Gibberd A, Roy S, Nunes M, Lorenz R, Leech R, Ogawa T, Kawanabe M, Hyvärinen A. Interpretable brain age prediction using linear latent variable models of functional connectivity. PLoS One 2020; 15:e0232296. [PMID: 32520931 PMCID: PMC7286502 DOI: 10.1371/journal.pone.0232296] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 04/11/2020] [Indexed: 01/02/2023] Open
Abstract
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.
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Affiliation(s)
- Ricardo Pio Monti
- Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
- RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan
- * E-mail:
| | - Alex Gibberd
- Department of Mathematics & Statistics, Lancaster University, Bailrigg, United Kingdom
| | - Sandipan Roy
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Matthew Nunes
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, Stanford University, Stanford, CA, United States of America
| | - Robert Leech
- Centre for Neuroimaging Science, Kings College London, London, United Kingdom
| | - Takeshi Ogawa
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
| | - Motoaki Kawanabe
- RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
| | - Aapo Hyvärinen
- Université Paris-Saclay, Inria, 91190 Palaiseau, France
- Department of Computer Science and HIIT, University of Helsinki, Helsinki, Finland
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19
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Reproducibility of amygdala activation in facial emotion processing at 7T. Neuroimage 2020; 211:116585. [DOI: 10.1016/j.neuroimage.2020.116585] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/24/2019] [Accepted: 01/23/2020] [Indexed: 01/10/2023] Open
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20
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Assem M, Glasser MF, Van Essen DC, Duncan J. A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex. Cereb Cortex 2020; 30:4361-4380. [PMID: 32244253 PMCID: PMC7325801 DOI: 10.1093/cercor/bhaa023] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Numerous brain imaging studies identified a domain-general or "multiple-demand" (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation, and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with the cortex of each individual parcellated using neurobiologically grounded multimodal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting-state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.
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Affiliation(s)
- Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK
| | - Matthew F Glasser
- Department of Neuroscience, Washington University in St. Louis, Saint Louis, MO 63110, USA.,Department of Radiology, Washington University in St. Louis, Saint Louis, MO 63110, USA.,St. Luke's Hospital, Saint Louis, MO 63017, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University in St. Louis, Saint Louis, MO 63110, USA
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK.,Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK
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21
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Cole JH, Lorenz R, Geranmayeh F, Wood T, Hellyer P, Williams S, Turkheimer F, Leech R. Active Acquisition for multimodal neuroimaging. Wellcome Open Res 2019; 3:145. [PMID: 31667357 PMCID: PMC6807153 DOI: 10.12688/wellcomeopenres.14918.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2019] [Indexed: 02/02/2023] Open
Abstract
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field -of -view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.
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Affiliation(s)
- James H. Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Romy Lorenz
- MRC Centre for Cognition and Brain Sciences, University of Cambridge, Cambridge, UK
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Fatemeh Geranmayeh
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Tobias Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Peter Hellyer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Steven Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rob Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
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22
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Lorenz R, Simmons LE, Monti RP, Arthur JL, Limal S, Laakso I, Leech R, Violante IR. Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization. Brain Stimul 2019; 12:1484-1489. [PMID: 31289013 PMCID: PMC6879005 DOI: 10.1016/j.brs.2019.07.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 06/26/2019] [Accepted: 07/01/2019] [Indexed: 11/25/2022] Open
Abstract
Background Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation. Objective We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time. Methods To validate the method, Bayesian optimization was employed using participants’ binary judgements about the intensity of phosphenes elicited through tACS. Results We demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations. Conclusion Alongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals.
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Affiliation(s)
- Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK; Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany.
| | - Laura E Simmons
- Computational, Cognitive and Clinical Neuroscience Laboratory, Department of Medicine, Imperial College London, London, W12 0NN, UK
| | - Ricardo P Monti
- Gatsby Computational Neuroscience Unit, University College London, London, W1T 4JG, UK
| | - Joy L Arthur
- Computational, Cognitive and Clinical Neuroscience Laboratory, Department of Medicine, Imperial College London, London, W12 0NN, UK
| | - Severin Limal
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - Ilkka Laakso
- Department of Electrical Engineering and Automation, Aalto University, Espoo, 02150, Finland
| | - Robert Leech
- Centre for Neuroimaging Science, King's College London, London, SE5 8AF, UK
| | - Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
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23
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Zboňáková L, Monti RP, Härdle WK. Towards the interpretation of time-varying regularization parameters in streaming penalized regression models. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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24
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deBettencourt MT, Turk-Browne NB, Norman KA. Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval. Neuroimage 2019; 200:292-301. [PMID: 31201985 DOI: 10.1016/j.neuroimage.2019.06.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 04/17/2019] [Accepted: 06/02/2019] [Indexed: 11/29/2022] Open
Abstract
Theories of mental context and memory posit that successful mental context reinstatement enables better retrieval of memories from the same context, at the expense of memories from other contexts. To test this hypothesis, we had participants study lists of words, interleaved with task-irrelevant images from one category (e.g., scenes). Following encoding, participants were cued to mentally reinstate the context associated with a particular list, by thinking about the images that had appeared between the words. We measured context reinstatement by applying multivariate pattern classifiers to fMRI, and related this to performance on a free recall test that followed immediately afterwards. To increase sensitivity, we used a closed-loop neurofeedback procedure, whereby higher classifier evidence for the cued category elicited increased visibility of the images from the studied context onscreen. Our goal was to create a positive feedback loop that amplified small fluctuations in mental context reinstatement, making it easier to experimentally detect a relationship between context reinstatement and recall. As predicted, we found that greater amounts of classifier evidence were associated with better recall of words from the reinstated context, and worse recall of words from a different context. In a second experiment, we assessed the role of neurofeedback in identifying this brain-behavior relationship by presenting context images again and manipulating whether their visibility depended on classifier evidence. When neurofeedback was removed, the relationship between classifier evidence and memory retrieval disappeared. Together, these findings demonstrate a clear effect of context reinstatement on memory recall and suggest that neurofeedback can be a useful tool for characterizing brain-behavior relationships.
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Affiliation(s)
- Megan T deBettencourt
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL, 60637, USA; Department of Psychology, University of Chicago, Chicago, IL, 60637, USA.
| | - Nicholas B Turk-Browne
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA; Department of Psychology, Princeton University, Princeton, NJ, 08540, USA; Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA; Department of Psychology, Princeton University, Princeton, NJ, 08540, USA
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25
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Soreq E, Leech R, Hampshire A. Dynamic network coding of working-memory domains and working-memory processes. Nat Commun 2019; 10:936. [PMID: 30804436 PMCID: PMC6389921 DOI: 10.1038/s41467-019-08840-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 01/18/2019] [Indexed: 01/09/2023] Open
Abstract
The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes (encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity that they evoke. This is the case even when focusing on 'multiple demands' brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
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Affiliation(s)
- Eyal Soreq
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK.
| | - Robert Leech
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Kings College London, London, SE5 8AF, UK
| | - Adam Hampshire
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK
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26
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Jackson RL, Cloutman LL, Lambon Ralph MA. Exploring distinct default mode and semantic networks using a systematic ICA approach. Cortex 2019; 113:279-297. [PMID: 30716610 PMCID: PMC6459395 DOI: 10.1016/j.cortex.2018.12.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 11/26/2018] [Accepted: 12/22/2018] [Indexed: 11/20/2022]
Abstract
Resting-state networks (RSNs; groups of regions consistently co-activated without an explicit task) are hugely influential in modern brain research. Despite this popularity, the link between specific RSNs and their functions remains elusive, limiting the impact on cognitive neuroscience (where the goal is to link cognition to neural systems). Here we present a series of logical steps to formally test the relationship between a coherent RSN with a cognitive domain. This approach is applied to a challenging and significant test-case; extracting a recently-proposed semantic RSN, determining its relation with a well-known RSN, the default mode network (DMN), and assessing their roles in semantic cognition. Results showed the DMN and semantic network are two distinct coherent RSNs. Assessing the cognitive signature of these spatiotemporally coherent networks directly (and therefore accounting for overlapping networks) showed involvement of the proposed semantic network, but not the DMN, in task-based semantic cognition. Following the steps presented here, researchers could formally test specific hypotheses regarding the function of RSNs, including other possible functions of the DMN.
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Affiliation(s)
- Rebecca L Jackson
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Lauren L Cloutman
- Neuroscience and Aphasia Research Unit (NARU), Division of Neuroscience & Experimental Psychology (Zochonis Building), University of Manchester, Manchester, UK
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27
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Cole JH, Lorenz R, Geranmayeh F, Wood T, Hellyer P, Williams S, Turkheimer F, Leech R. Active Acquisition for multimodal neuroimaging. Wellcome Open Res 2018; 3:145. [PMID: 31667357 PMCID: PMC6807153 DOI: 10.12688/wellcomeopenres.14918.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2018] [Indexed: 02/02/2023] Open
Abstract
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field -of -view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.
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Affiliation(s)
- James H. Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Romy Lorenz
- MRC Centre for Cognition and Brain Sciences, University of Cambridge, Cambridge, UK
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Fatemeh Geranmayeh
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Tobias Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Peter Hellyer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Steven Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rob Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
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Monti RP, Anagnostopoulos C, Montana G. Adaptive regularization for Lasso models in the context of nonstationary data streams. Stat Anal Data Min 2018. [DOI: 10.1002/sam.11390] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ricardo P. Monti
- Department of Mathematics; Imperial College London; London UK
- Gatsby Computational Neuroscience Unit; UCL; London UK
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Gandelman JA, Albert K, Boyd BD, Park JW, Riddle M, Woodward ND, Kang H, Landman BA, Taylor WD. Intrinsic Functional Network Connectivity Is Associated With Clinical Symptoms and Cognition in Late-Life Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 4:160-170. [PMID: 30392844 DOI: 10.1016/j.bpsc.2018.09.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/13/2018] [Accepted: 09/01/2018] [Indexed: 01/15/2023]
Abstract
BACKGROUND Late-life depression (LLD) has been associated with alterations in intrinsic functional networks, best characterized in the default mode network (DMN), cognitive control network (CCN), and salience network. However, these findings often derive from small samples, and it is not well understood how network findings relate to clinical and cognitive symptomatology. METHODS We studied 100 older adults (n = 79 with LLD, n = 21 nondepressed) and collected resting-state functional magnetic resonance imaging, clinical measures of depression, and performance on cognitive tests. We selected canonical network regions for each intrinsic functional network (DMN, CCN, and salience network) as seeds in seed-to-voxel analysis. We compared connectivity between the depressed and nondepressed groups and correlated connectivity with depression severity among depressed subjects. We then investigated whether the observed connectivity findings were associated with greater severity of common neuropsychiatric symptoms or poorer cognitive performance. RESULTS LLD was characterized by decreased DMN connectivity to the frontal pole, a CCN region (Wald χ21 = 22.33, p < .001). No significant group differences in connectivity were found for the CCN or salience network. However, in the LLD group, increased CCN connectivity was associated with increased depression severity (Wald χ21 > 20.14, p < .001), greater anhedonia (Wald χ21 = 7.02, p = .008) and fatigue (Wald χ21 = 6.31, p = .012), and poorer performance on tests of episodic memory (Wald χ21 > 4.65, p < .031), executive function (Wald χ21 = 7.18, p = .007), and working memory (Wald χ21 > 4.29, p < .038). CONCLUSIONS LLD is characterized by differences in DMN connectivity, while CCN connectivity is associated with LLD symptomology, including poorer performance in several cognitive domains.
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Affiliation(s)
| | - Kimberly Albert
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brian D Boyd
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jung Woo Park
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Meghan Riddle
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neil D Woodward
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Warren D Taylor
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee; Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee.
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