1
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Morawetz C, Basten U. Neural underpinnings of individual differences in emotion regulation: A systematic review. Neurosci Biobehav Rev 2024; 162:105727. [PMID: 38759742 DOI: 10.1016/j.neubiorev.2024.105727] [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: 02/19/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/19/2024]
Abstract
This review synthesises individual differences in neural processes related to emotion regulation (ER). It comprises individual differences in self-reported and physiological regulation success, self-reported ER-related traits, and demographic variables, to assess their correlation with brain activation during ER tasks. Considering region-of-interest (ROI) and whole-brain analyses, the review incorporated data from 52 functional magnetic resonance imaging studies. Results can be summarized as follows: (1) Self-reported regulation success (assessed by emotional state ratings after regulation) and self-reported ER-related traits (assessed by questionnaires) correlated with brain activity in the lateral prefrontal cortex. (2) Amygdala activation correlated with ER-related traits only in ROI analyses, while it was associated with regulation success in whole-brain analyses. (3) For demographic and physiological measures, there was no systematic overlap in effects reported across studies. In showing that individual differences in regulation success and ER-related traits can be traced back to differences in the neural activity of brain regions associated with emotional reactivity (amygdala) and cognitive control (lateral prefrontal cortex), our findings can inform prospective personalised intervention models.
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Affiliation(s)
| | - Ulrike Basten
- Department of Psychology, RPTU Kaiserslautern-Landau, Germany
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2
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McAvoy MM, Liu L, Zhou R, Philip BA. Reducing individual differences in task fMRI with OGRE (One-step General Registration and Extraction) preprocessing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.19.558290. [PMID: 37781580 PMCID: PMC10541115 DOI: 10.1101/2023.09.19.558290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Volumetric analysis methods continue to enjoy great popularity in the analysis of task-related functional MRI (fMRI) data. Among these methods, the software package FSL (FMRIB, Oxford, UK) is omnipresent throughout the field. However, it remains unknown what advantages might be gained by integrating FSL with alternative preprocessing tools. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to apply FreeSurfer brain extraction for simultaneous registration and motion correction ("one-step resampling"), for FSL volumetric analysis of fMRI data. We compared three preprocessing approaches (OGRE, FSL, and fMRIPrep) in a dataset wherein adult human volunteers (N=39) performed a precision drawing task during fMRI scanning. The three approaches produced grossly similar results, but OGRE's preprocessing led to lower inter-individual variability across the brain and greater detected activation in primary motor cortex contralateral to movement. This demonstrates that FreeSurfer tools and one-step resampling can improve FSL's volumetric analysis of fMRI data. The OGRE pipeline provides an off-the-shelf method to apply FreeSurfer-based brain extraction and one-step resampling of motion correction and registration for FSL analysis of task fMRI data.
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3
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Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
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4
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Mandino F, Horien C, Shen X, Desrosiers-Gregoire G, Luo W, Markicevic M, Constable RX, Papademetris X, Chakravarty MM, Betzel RF, Lake EMR. Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.594620. [PMID: 38826324 PMCID: PMC11142213 DOI: 10.1101/2024.05.24.594620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.
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5
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Lee K, Ji JL, Fonteneau C, Berkovitch L, Rahmati M, Pan L, Repovš G, Krystal JH, Murray JD, Anticevic A. Human brain state dynamics reflect individual neuro-phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.557763. [PMID: 37790400 PMCID: PMC10542143 DOI: 10.1101/2023.09.18.557763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, en-compassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of statetrait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.
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6
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Bishop DVM, Woodhead ZVJ, Watkins KE. Approaches to Measuring Language Lateralisation: An Exploratory Study Comparing Two fMRI Methods and Functional Transcranial Doppler Ultrasound. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:409-431. [PMID: 38911461 PMCID: PMC11192441 DOI: 10.1162/nol_a_00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/08/2024] [Indexed: 06/25/2024]
Abstract
In this exploratory study we compare and contrast two methods for deriving a laterality index (LI) from functional magnetic resonance imaging (fMRI) data: the weighted bootstrapped mean from the LI Toolbox (toolbox method), and a novel method that uses subtraction of activations from homologous regions in left and right hemispheres to give an array of difference scores (mirror method). Data came from 31 individuals who had been selected to include a high proportion of people with atypical laterality when tested with functional transcranial Doppler ultrasound (fTCD). On two tasks, word generation and semantic matching, the mirror method generally gave better agreement with fTCD laterality than the toolbox method, both for individual regions of interest, and for a large region corresponding to the middle cerebral artery. LI estimates from this method had much smaller confidence intervals (CIs) than those from the toolbox method; with the mirror method, most participants were reliably lateralised to left or right, whereas with the toolbox method, a higher proportion were categorised as bilateral (i.e., the CI for the LI spanned zero). Reasons for discrepancies between fMRI methods are discussed: one issue is that the toolbox method averages the LI across a wide range of thresholds. Furthermore, examination of task-related t-statistic maps from the two hemispheres showed that language lateralisation is evident in regions characterised by deactivation, and so key information may be lost by ignoring voxel activations below zero, as is done with conventional estimates of the LI.
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Affiliation(s)
- Dorothy V. M. Bishop
- Wellcome Centre for Integrative Neuroimaging, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Zoe V. J. Woodhead
- Wellcome Centre for Integrative Neuroimaging, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Kate E. Watkins
- Wellcome Centre for Integrative Neuroimaging, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
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7
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Paige KJ, Colder CR, Cope LM, Hardee JE, Heitzeg MM, Soules ME, Weigard AS. Clarifying the longitudinal factor structure, temporal stability, and construct validity of Go/No-Go task-related neural activation across adolescence and young adulthood. Dev Cogn Neurosci 2024; 67:101390. [PMID: 38759528 PMCID: PMC11127199 DOI: 10.1016/j.dcn.2024.101390] [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: 01/31/2024] [Revised: 04/22/2024] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
This study aimed to clarify the psychometric properties and development of Go/No-Go (GNG) task-related neural activation across critical periods of neurobiological maturation by examining its longitudinal stability, factor structure, developmental change, and associations with a computational index of task-general cognitive control. A longitudinal sample (N=289) of adolescents from the Michigan Longitudinal Study was assessed at four time-points (mean number of timepoints per participant=2.05; standard deviation=0.89) spanning early adolescence (ages 10-13) to young adulthood (22-25). Results suggested that regional neural activations from the "successful inhibition" (SI>GO) and "failed inhibition" (FI>GO; error-monitoring) contrasts are each described well by a single general factor. Neural activity across both contrasts showed developmental increases throughout adolescence that plateau in young adulthood. Neural activity metrics evidenced low temporal stability across this period of marked developmental change, and the SI>GO factor showed no relations with a behavioral index of cognitive control. The FI>GO factor displayed stronger criterion validity in the form of significant, positive associations with behaviorally measured cognitive control. Findings emphasize the utility of well-validated psychometric methods and longitudinal data for clarifying the measurement properties of functional neuroimaging metrics and improving measurement practices in developmental cognitive neuroscience.
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Affiliation(s)
- K J Paige
- Department of Psychology, The State University of New York at Buffalo, USA.
| | - C R Colder
- Department of Psychology, The State University of New York at Buffalo, USA
| | - L M Cope
- Department of Psychiatry, University of Michigan, USA
| | - J E Hardee
- Department of Psychiatry, University of Michigan, USA
| | - M M Heitzeg
- Department of Psychiatry, University of Michigan, USA
| | - M E Soules
- Department of Psychiatry, University of Michigan, USA
| | - A S Weigard
- Department of Psychiatry, University of Michigan, USA
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8
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Bernstein-Eliav M, Tavor I. The Prediction of Brain Activity from Connectivity: Advances and Applications. Neuroscientist 2024; 30:367-377. [PMID: 36250457 PMCID: PMC11107130 DOI: 10.1177/10738584221130974] [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] [Indexed: 11/16/2022]
Abstract
The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.
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Affiliation(s)
| | - Ido Tavor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel
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9
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Meng Z, Huang Y, Wang W, Zhou L, Zhou K. Orienting role of the putative human posterior infero-temporal area in visual attention. Cortex 2024; 175:54-65. [PMID: 38704919 DOI: 10.1016/j.cortex.2024.04.006] [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: 09/27/2023] [Revised: 02/27/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024]
Abstract
The dorsal attention network (DAN) is a network of brain regions essential for attentional orienting, which includes the lateral intraparietal area (LIP) and frontal eye field (FEF). Recently, the putative human dorsal posterior infero-temporal area (phPITd) has been identified as a new node of the DAN. However, its functional relationship with other areas of the DAN and its specific role in visual attention remained unclear. In this study, we analyzed a large publicly available neuroimaging dataset to investigate the intrinsic functional connectivities (FCs) of the phPITd with other brain areas. The results showed that the intrinsic FCs of the phPITd with the areas of the visual network and the DAN were significantly stronger than those with the ventral attention network (VAN) areas and areas of other networks. We further conducted individual difference analyses with a sample size of 295 participants and a series of attentional tasks to investigate which attentional components each phPITd-based DAN edge predicts. Our findings revealed that the intrinsic FC of the left phPITd with the LIPv could predict individual ability in attentional orienting, but not in alerting, executive control, and distractor suppression. Our results not only provide direct evidence of the phPITd's functional relationship with the LIPv, but also offer a comprehensive understanding of its specific role in visual attention.
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Affiliation(s)
- Zong Meng
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Yingjie Huang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Wenbo Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
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10
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Huang S, Faul L, Parikh N, LaBar KS, De Brigard F. Counterfactual thinking induces different neural patterns of memory modification in anxious individuals. Sci Rep 2024; 14:10630. [PMID: 38724623 PMCID: PMC11082200 DOI: 10.1038/s41598-024-61545-x] [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: 12/16/2023] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
Episodic counterfactual thinking (eCFT) is the process of mentally simulating alternate versions of experiences, which confers new phenomenological properties to the original memory and may be a useful therapeutic target for trait anxiety. However, it remains unclear how the neural representations of a memory change during eCFT. We hypothesized that eCFT-induced memory modification is associated with changes to the neural pattern of a memory primarily within the default mode network, moderated by dispositional anxiety levels. We tested this proposal by examining the representational dynamics of eCFT for 39 participants varying in trait anxiety. During eCFT, lateral parietal regions showed progressively more distinct activity patterns, whereas medial frontal neural activity patterns became more similar to those of the original memory. Neural pattern similarity in many default mode network regions was moderated by trait anxiety, where highly anxious individuals exhibited more generalized representations for upward eCFT (better counterfactual outcomes), but more distinct representations for downward eCFT (worse counterfactual outcomes). Our findings illustrate the efficacy of examining eCFT-based memory modification via neural pattern similarity, as well as the intricate interplay between trait anxiety and eCFT generation.
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Affiliation(s)
- Shenyang Huang
- Department of Psychology and Neuroscience, Duke University, Durham, NC, 27708, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA.
| | - Leonard Faul
- Department of Psychology and Neuroscience, Boston College, Chestnut Hill, MA, 02467, USA
| | - Natasha Parikh
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kevin S LaBar
- Department of Psychology and Neuroscience, Duke University, Durham, NC, 27708, USA
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA
| | - Felipe De Brigard
- Department of Psychology and Neuroscience, Duke University, Durham, NC, 27708, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA.
- Department of Philosophy, Duke University, Durham, NC, 27708, USA.
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11
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Demidenko MI, Mumford JA, Poldrack RA. Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585755. [PMID: 38562804 PMCID: PMC10983911 DOI: 10.1101/2024.03.19.585755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples ( N s: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho ) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization, however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.
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12
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Kim N, Kim MJ, Strauman TJ, Hariri AR. Intrinsic functional connectivity of motor and heteromodal association cortex predicts individual differences in regulatory focus. PNAS NEXUS 2024; 3:pgae167. [PMID: 38711811 PMCID: PMC11071117 DOI: 10.1093/pnasnexus/pgae167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024]
Abstract
Regulatory focus theory (RFT) describes two cognitive-motivational systems for goal pursuit-the promotion and prevention systems-important for self-regulation and previously implicated in vulnerability to psychopathology. According to RFT, the promotion system is engaged in attaining ideal goals (e.g. hopes and dreams), whereas the prevention system is associated with accomplishing ought goals (e.g. duties and obligations). Prior task-based functional magnetic resonance imaging (fMRI) studies have mostly explored the mapping of these two systems onto the activity of a priori brain regions supporting motivation and executive control in both healthy and depressed adults. However, complex behavioral processes such as those guided by individual differences in regulatory focus are likely supported by widely distributed patterns of intrinsic functional connectivity. We used data-driven connectome-based predictive modeling to identify patterns of distributed whole-brain intrinsic network connectivity associated with individual differences in promotion and prevention system orientation in 1,307 young university volunteers. Our analyses produced a network model predictive of prevention but not promotion orientation, specifically the subjective experience of successful goal pursuit using prevention strategies. The predictive model of prevention success was highlighted by decreased intrinsic functional connectivity of both heteromodal association cortices in the parietal and limbic networks and the primary motor cortex. We discuss these findings in the context of strategic inaction, which drives individuals with a strong dispositional prevention orientation to inhibit their behavioral tendencies in order to shield the self from potential losses, thus maintaining the safety of the status quo but also leading to trade-offs in goal pursuit success.
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Affiliation(s)
- Nayoung Kim
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| | - M Justin Kim
- Department of Psychology, Sungkyunkwan University, Seoul 03063, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, South Korea
| | - Timothy J Strauman
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
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13
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Cao C, Li Y, Hu F, Gao X. Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations. Cereb Cortex 2024; 34:bhae146. [PMID: 38602743 DOI: 10.1093/cercor/bhae146] [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: 12/19/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024] Open
Abstract
The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Fang Hu
- The Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, 423043 Chenzhou, China
| | - Xieping Gao
- The Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081 Changsha, China
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14
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Nakuci J, Yeon J, Kim JH, Kim SP, Rahnev D. Behavior can be decoded across the cortex when individual differences are considered. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.12.584674. [PMID: 38559114 PMCID: PMC10979965 DOI: 10.1101/2024.03.12.584674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Group-level analyses have typically associated behavioral signatures with a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the level of the individual, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. These results were further replicated in a second dataset. Lastly, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. These results show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and the individual level.
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Affiliation(s)
- Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
| | - Jiwon Yeon
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
- Department of Psychology, Stanford University, Stanford, California, 94305, USA
| | - Ji-Hyun Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
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15
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Ikeda S, Jeong H, Sasaki Y, Sakaki K, Yamazaki S, Nozawa T, Kawashima R. Predicting conversational satisfaction of face-to-face conversation through interpersonal similarity in resting-state functional connectivity. Sci Rep 2024; 14:6015. [PMID: 38472307 DOI: 10.1038/s41598-024-56718-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 03/10/2024] [Indexed: 03/14/2024] Open
Abstract
When conversing with an unacquainted person, if it goes well, we can obtain much satisfaction (referred to as conversational satisfaction). Can we predict how satisfied dyads will be with face-to-face conversation? To this end, we employed interpersonal similarity in whole-brain resting-state functional connectivity (RSFC), measured using functional magnetic resonance imaging before dyadic conversation. We investigated whether conversational satisfaction could be predicted from interpersonal similarity in RSFC using multivariate pattern analysis. Consequently, prediction was successful, suggesting that interpersonal similarity in RSFC is an effective neural biomarker predicting how much face-to-face conversation goes well. Furthermore, regression coefficients from predictive models suggest that both interpersonal similarity and dissimilarity contribute to good interpersonal relationships in terms of brain activity. The present study provides the potential of an interpersonal similarity approach using RSFC for understanding the foundations of human relationships and new neuroscientific insight into whether success in human interactions is predetermined.
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Affiliation(s)
- Shigeyuki Ikeda
- Department of Ubiquitous Sensing, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
| | - Hyeonjeong Jeong
- Graduate School of International Cultural Studies, Tohoku University, Sendai, Japan
| | - Yukako Sasaki
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Kohei Sakaki
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Shohei Yamazaki
- Department of Human Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Takayuki Nozawa
- Research Institute for the Earth Inclusive Sensing, Tokyo Institute of Technology, Tokyo, Japan
| | - Ryuta Kawashima
- Department of Ubiquitous Sensing, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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16
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Huang Y, Li Y, Yuan Y, Zhang X, Yan W, Li T, Niu Y, Xu M, Yan T, Li X, Li D, Xiang J, Wang B, Yan T. Beta-informativeness-diffusion multilayer graph embedding for brain network analysis. Front Neurosci 2024; 18:1303741. [PMID: 38525375 PMCID: PMC10957763 DOI: 10.3389/fnins.2024.1303741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/07/2024] [Indexed: 03/26/2024] Open
Abstract
Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.
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Affiliation(s)
- Yin Huang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ying Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Yuting Yuan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Xingyu Zhang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Wenjie Yan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yan Niu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Mengzhou Xu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ting Yan
- Translational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Xiaowen Li
- Computer Information Engineering Institute, Shanxi Technology and Business College, Taiyuan, China
| | - Dandan Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
| | - Tianyi Yan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
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17
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Gao C, Wu X, Cheng X, Madsen KH, Chu C, Yang Z, Fan L. Individualized brain mapping for navigated neuromodulation. Chin Med J (Engl) 2024; 137:508-523. [PMID: 38269482 PMCID: PMC10932519 DOI: 10.1097/cm9.0000000000002979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Indexed: 01/26/2024] Open
Abstract
ABSTRACT The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.
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Affiliation(s)
- Chaohong Gao
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xia Wu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xinle Cheng
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Kristoffer Hougaard Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark
| | - Congying Chu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhengyi Yang
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lingzhong Fan
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266000, China
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18
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Li B, Tong L, Zhang C, Chen P, Wang L, Yan B. Prediction of image interpretation cognitive ability under different mental workloads: a task-state fMRI study. Cereb Cortex 2024; 34:bhae100. [PMID: 38494891 DOI: 10.1093/cercor/bhae100] [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: 01/03/2024] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Visual imaging experts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techniques can predict cognitive abilities, which provides a possible method for selecting individuals with excellent image interpretation skills. We recorded behavioral data and neural activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participants were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superior parietal gyrus, inferior parietal gyrus, and insula compared to the low-ability group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. Our study revealed the neural basis related to image interpretation ability when exposed to different mental workloads. Additionally, our results demonstrated the efficacy of machine learning algorithms in extracting neural activation features to predict such ability.
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Affiliation(s)
- Bao Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Chi Zhang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Panpan Chen
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Linyuan Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Science Avenue 62, Zhengzhou, 450001, China
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19
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Snijder JP, Tang R, Bugg JM, Conway ARA, Braver TS. On the psychometric evaluation of cognitive control tasks: An Investigation with the Dual Mechanisms of Cognitive Control (DMCC) battery. Behav Res Methods 2024; 56:1604-1639. [PMID: 37040066 PMCID: PMC10088767 DOI: 10.3758/s13428-023-02111-7] [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] [Accepted: 03/20/2023] [Indexed: 04/12/2023]
Abstract
The domain of cognitive control has been a major focus of experimental, neuroscience, and individual differences research. Currently, however, no theory of cognitive control successfully unifies both experimental and individual differences findings. Some perspectives deny that there even exists a unified psychometric cognitive control construct to be measured at all. These shortcomings of the current literature may reflect the fact that current cognitive control paradigms are optimized for the detection of within-subject experimental effects rather than individual differences. In the current study, we examine the psychometric properties of the Dual Mechanisms of Cognitive Control (DMCC) task battery, which was designed in accordance with a theoretical framework that postulates common sources of within-subject and individual differences variation. We evaluated both internal consistency and test-retest reliability, and for the latter, utilized both classical test theory measures (i.e., split-half methods, intraclass correlation) and newer hierarchical Bayesian estimation of generative models. Although traditional psychometric measures suggested poor reliability, the hierarchical Bayesian models indicated a different pattern, with good to excellent test-retest reliability in almost all tasks and conditions examined. Moreover, within-task, between-condition correlations were generally increased when using the Bayesian model-derived estimates, and these higher correlations appeared to be directly linked to the higher reliability of the measures. In contrast, between-task correlations remained low regardless of theoretical manipulations or estimation approach. Together, these findings highlight the advantages of Bayesian estimation methods, while also pointing to the important role of reliability in the search for a unified theory of cognitive control.
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Affiliation(s)
- Jean-Paul Snijder
- Department of Psychology, Heidelberg University, Heidelberg, Germany.
| | - Rongxiang Tang
- Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - Julie M Bugg
- Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - Andrew R A Conway
- Division of Behavioral & Organizational Sciences, Claremont Graduate University, Claremont, CA, USA
| | - Todd S Braver
- Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
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20
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Cao Z, Xiao X, Xie C, Wei L, Yang Y, Zhu C. Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility. Front Psychiatry 2024; 15:1341908. [PMID: 38419897 PMCID: PMC10899497 DOI: 10.3389/fpsyt.2024.1341908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.
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Affiliation(s)
- Zhengcao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- School of Arts and Communication, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Cong Xie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lijiang Wei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
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21
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Busch EL, Rapuano KM, Anderson KM, Rosenberg MD, Watts R, Casey BJ, Haxby JV, Feilong M. Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development. J Neurosci 2024; 44:e0735232023. [PMID: 38148152 PMCID: PMC10866091 DOI: 10.1523/jneurosci.0735-23.2023] [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: 03/14/2023] [Revised: 10/09/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.
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Affiliation(s)
- Erica L Busch
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Kristina M Rapuano
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, Illinois, 60637
| | - Richard Watts
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - B J Casey
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - James V Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, 03755
| | - Ma Feilong
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, 03755
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22
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Ross D, Wagshul ME, Izzetoglu M, Holtzer R. Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking. J Int Neuropsychol Soc 2024; 30:117-127. [PMID: 37366047 PMCID: PMC10751394 DOI: 10.1017/s1355617723000371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
OBJECTIVE Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance. METHOD Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions. RESULTS Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02). CONCLUSIONS Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.
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Affiliation(s)
- Daliah Ross
- Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA
| | - Mark E. Wagshul
- Department of Radiology, Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Meltem Izzetoglu
- Department of Electrical and Computer Engineering, Villanova University, Villanova, PA, USA
| | - Roee Holtzer
- Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
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23
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Mumford JA, Bissett PG, Jones HM, Shim S, Rios JAH, Poldrack RA. The response time paradox in functional magnetic resonance imaging analyses. Nat Hum Behav 2024; 8:349-360. [PMID: 37996498 DOI: 10.1038/s41562-023-01760-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 10/13/2023] [Indexed: 11/25/2023]
Abstract
Response times (RTs) are often the main signal of interest in cognitive psychology but are often ignored in functional MRI (fMRI) analyses. In fMRI analysis the intensity of the signal serves as a proxy for the intensity of local neuronal activity, but changes in either the intensity or the duration of neuronal activity can yield identical fMRI signals. Therefore, if RTs are ignored and pair with neuronal durations, fMRI results claiming intensity differences may be confounded by RTs. We show how ignoring RTs goes beyond this confound, where longer RTs are paired with larger activation estimates, to lesser-known issues where RTs become confounds in group-level analyses and, surprisingly, how the RT confound can induce other artificial group-level associations with variables that are not related to the condition contrast or RTs. We propose a new time-series model to address these issues and encourage increasing focus on what the widespread RT-based signal represents.
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Affiliation(s)
| | | | - Henry M Jones
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Sunjae Shim
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Jaime Ali H Rios
- Department of Psychology, Stanford University, Stanford, CA, USA
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24
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Sandre A, Weinberg A, Park J. Psychophysiology and affective processing across the lifespan: Pathways to psychopathology. Biol Psychol 2024; 186:108740. [PMID: 38154702 DOI: 10.1016/j.biopsycho.2023.108740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 12/21/2023] [Accepted: 12/21/2023] [Indexed: 12/30/2023]
Affiliation(s)
- Aislinn Sandre
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY 10027, USA
| | - Anna Weinberg
- Department of Ps ychology, McGill University, 2001 McGill College Avenue, Montreal, Quebec H3A 1G1, Canada
| | - Juhyun Park
- Department of Ps ychology, McGill University, 2001 McGill College Avenue, Montreal, Quebec H3A 1G1, Canada.
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25
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Chen DY, Di X, Yu X, Biswal BB. The significance and limited influence of cerebrovascular reactivity on age and sex effects in task- and resting-state brain activity. Cereb Cortex 2024; 34:bhad448. [PMID: 38212284 PMCID: PMC10832986 DOI: 10.1093/cercor/bhad448] [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: 09/01/2023] [Accepted: 10/31/2023] [Indexed: 01/13/2024] Open
Abstract
Functional MRI measures the blood-oxygen-level dependent signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state blood-oxygen-level dependent activity and may confound inter-individual effects, such as those related to aging and biological sex. We examined a large dataset containing breath-holding, checkerboard, and resting-state tasks. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and quantified resting-state activity with amplitude of low-frequency fluctuations and regional homogeneity. We hypothesized that cerebrovascular reactivity would be correlated with blood-oxygen-level dependent measures and that accounting for these correlations would result in better estimates of age and sex effects. We found that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with amplitude of low-frequency fluctuations and regional homogeneity in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in amplitude of low-frequency fluctuations and regional homogeneity. However, correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.
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Affiliation(s)
- Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
- Rutgers Biomedical and Health Sciences, Rutgers School of Graduate Studies, Newark, NJ 08901, United States
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Xin Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02114, United States
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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26
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Zugman A, Ringlein GV, Finn ES, Lewis KM, Berman E, Silverman WK, Lebowitz ER, Pine DS, Winkler AM. Brain Functional Connectivity and Anatomical Features as Predictors of Cognitive Behavioral Therapy Outcome for Anxiety in Youths. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.29.24301959. [PMID: 38352528 PMCID: PMC10862993 DOI: 10.1101/2024.01.29.24301959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Background Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have major impact. However, existing clinical models are weakly predictive. The current study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. Methods Two datasets were studied: (A) one consisted of n=54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n=15 subjects treated for 8 weeks. Connectome Predictive Modeling (CPM) was used to predict treatment response, as assessed with the PARS; additionally we investigated models using anatomical features, instead of functional connectivity. The main analysis included network edges positively correlated with treatment outcome, and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses also are presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r and mean absolute error (MAE). Outcomes The main model showed a mean absolute error of approximately 3.5 (95%CI: [3.1-3.8]) points a R2 of 0.08 [-0.14 - 0.26] and r of 0.38 [0.24 - 0.511]. When testing this model in the left-out sample (B) the results were similar, with a MAE of 3.4 [2.8 - 4.7], R2-0.65 [-2.29 - 0.16] and r of 0.4 [0.24 - 0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2. Interpretation The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, the current study does not support extensive use of CPM to predict outcome in pediatric anxiety.
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Affiliation(s)
- Andre Zugman
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Grace V. Ringlein
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Emily S. Finn
- Psychological and Brain Sciences, Dartmouth College, 3 Maynard St, Hanover, NH, 03755, USA
| | - Krystal M. Lewis
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Erin Berman
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Wendy K. Silverman
- Child Study Center, Yale University, 230 South Frontage Rd., New Haven, CT 06520, USA
| | - Eli R. Lebowitz
- Child Study Center, Yale University, 230 South Frontage Rd., New Haven, CT 06520, USA
| | - Daniel S. Pine
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Anderson M. Winkler
- Division of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, 1 West University Blvd, Brownsville, TX 78520, USA
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27
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Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
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Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
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28
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Xu T, Wu Y, Zhang Y, Zuo XN, Chen F, Zhou C. Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development. Neurosci Bull 2024; 40:50-64. [PMID: 37715923 PMCID: PMC10774512 DOI: 10.1007/s12264-023-01108-8] [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: 09/20/2022] [Accepted: 06/01/2023] [Indexed: 09/18/2023] Open
Abstract
The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.
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Affiliation(s)
- Tianyong Xu
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
| | - Yunying Wu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Yi Zhang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Feiyan Chen
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
| | - Changsong Zhou
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, 999077, China.
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29
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Mallaroni P, Mason NL, Kloft L, Reckweg JT, van Oorsouw K, Toennes SW, Tolle HM, Amico E, Ramaekers JG. Shared functional connectome fingerprints following ritualistic ayahuasca intake. Neuroimage 2024; 285:120480. [PMID: 38061689 DOI: 10.1016/j.neuroimage.2023.120480] [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: 07/17/2023] [Revised: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 01/13/2024] Open
Abstract
The knowledge that brain functional connectomes are unique and reliable has enabled behaviourally relevant inferences at a subject level. However, whether such "fingerprints" persist under altered states of consciousness is unknown. Ayahuasca is a potent serotonergic psychedelic which produces a widespread dysregulation of functional connectivity. Used communally in religious ceremonies, its shared use may highlight relevant novel interactions between mental state and functional connectome (FC) idiosyncrasy. Using 7T fMRI, we assessed resting-state static and dynamic FCs for 21 Santo Daime members after collective ayahuasca intake in an acute, within-subject study. Here, connectome fingerprinting revealed FCs showed reduced idiosyncrasy, accompanied by a spatiotemporal reallocation of keypoint edges. Importantly, we show that interindividual differences in higher-order FC motifs are relevant to experiential phenotypes, given that they can predict perceptual drug effects. Collectively, our findings offer an example of how individualised connectivity markers can be used to trace a subject's FC across altered states of consciousness.
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Affiliation(s)
- Pablo Mallaroni
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands.
| | - Natasha L Mason
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Lilian Kloft
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Johannes T Reckweg
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Kim van Oorsouw
- Department of Forensic Psychology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Stefan W Toennes
- Institute of Legal Medicine, University Hospital, Goethe University, Frankfurt/Main, Germany
| | | | | | - Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
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30
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Dabbagh A, Horn U, Kaptan M, Mildner T, Müller R, Lepsien J, Weiskopf N, Brooks JCW, Finsterbusch J, Eippert F. Reliability of task-based fMRI in the dorsal horn of the human spinal cord. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.572825. [PMID: 38187724 PMCID: PMC10769329 DOI: 10.1101/2023.12.22.572825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with good reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.
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Affiliation(s)
- Alice Dabbagh
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, CA, USA
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Müller
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jöran Lepsien
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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31
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Hu L, Katz ES, Stamoulis C. Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference. Neuroimage 2023; 284:120459. [PMID: 37977408 DOI: 10.1016/j.neuroimage.2023.120459] [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: 06/23/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
Metabolic, hormonal, autonomic and physiological rhythms may have a significant impact on cerebral hemodynamics and intrinsic brain synchronization measured with fMRI (the resting-state connectome). The impact of their characteristic time scales (hourly, circadian, seasonal), and consequently scan timing effects, on brain topology in inherently heterogeneous developing connectomes remains elusive. In a cohort of 4102 early adolescents with resting-state fMRI (median age = 120.0 months; 53.1 % females) from the Adolescent Brain Cognitive Development Study, this study investigated associations between scan time-of-day, time-of-week (school day vs weekend) and time-of-year (school year vs summer vacation) and topological properties of resting-state connectomes at multiple spatial scales. On average, participants were scanned around 2 pm, primarily during school days (60.9 %), and during the school year (74.6 %). Scan time-of-day was negatively correlated with multiple whole-brain, network-specific and regional topological properties (with the exception of a positive correlation with modularity), primarily of visual, dorsal attention, salience, frontoparietal control networks, and the basal ganglia. Being scanned during the weekend (vs a school day) was correlated with topological differences in the hippocampus and temporoparietal networks. Being scanned during the summer vacation (vs the school year) was consistently positively associated with multiple topological properties of bilateral visual, and to a lesser extent somatomotor, dorsal attention and temporoparietal networks. Time parameter interactions suggested that being scanned during the weekend and summer vacation enhanced the positive effects of being scanned in the morning. Time-of-day effects were overall small but spatially extensive, and time-of-week and time-of-year effects varied from small to large (Cohen's f ≤ 0.1, Cohen's d<0.82, p < 0.05). Together, these parameters were also positively correlated with temporal fMRI signal variability but only in the left hemisphere. Finally, confounding effects of scan time parameters on relationships between connectome properties and cognitive task performance were assessed using the ABCD neurocognitive battery. Although most relationships were unaffected by scan time parameters, their combined inclusion eliminated associations between properties of visual and somatomotor networks and performance in the Matrix Reasoning and Pattern Comparison Processing Speed tasks. Thus, scan time of day, week and year may impact measurements of adolescent brain's functional circuits, and should be accounted for in studies on their associations with cognitive performance, in order to reduce the probability of incorrect inference.
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Affiliation(s)
- Linfeng Hu
- Department of Pediatrics, Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Harvard School of Public Health, Department of Biostatistics, Boston, MA 02115, USA
| | - Eliot S Katz
- Johns Hopkins All Children's Hospital, St. Petersburg, FL 33701, USA
| | - Catherine Stamoulis
- Department of Pediatrics, Division of Adolescent and Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA.
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32
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Feng C, Tian X, Luo YJ. Neurocomputational Substrates Underlying the Effect of Identifiability on Third-Party Punishment. J Neurosci 2023; 43:8018-8031. [PMID: 37752000 PMCID: PMC10669760 DOI: 10.1523/jneurosci.0460-23.2023] [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: 03/22/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 09/28/2023] Open
Abstract
The identifiable target effect refers to the preference for helping identified victims and punishing identifiable perpetrators compared with equivalent but unidentifiable counterparts. The identifiable target effect is often attributed to the heightened moral emotions evoked by identified targets. However, the specific neurocognitive processes that mediate and/or modulate this effect remain largely unknown. Here, we combined a third-party punishment game with brain imaging and computational modeling to unravel the neurocomputational underpinnings of the identifiable transgressor effect. Human participants (males and females) acted as bystanders and punished identified or anonymous wrongdoers. Participants were more punitive toward identified wrongdoers than anonymous wrongdoers because they took a vicarious perspective of victims and adopted lower reference points of inequity (i.e., more stringent norms) in the identified context than in the unidentified context. Accordingly, there were larger activity of the ventral anterior insula, more distinct multivariate neural patterns in the dorsal anterior insula and dorsal anterior cingulate cortex, and lower strength between ventral anterior insula and dorsolateral PFC and between dorsal anterior insula and ventral striatum connectivity in response to identified transgressors than anonymous transgressors. These findings implicate the interplay of expectancy violations, emotions, and self-interest in the identifiability effect. Last, individual differences in the identifiability effect were associated with empathic concern/social dominance orientation, activity in the precuneus/cuneus and temporo-parietal junction, and intrinsic functional connectivity of the dorsolateral PFC. Together, our work is the first to uncover the neurocomputational processes mediating identifiable transgressor effect and to characterize psychophysiological profiles modulating the effect.SIGNIFICANCE STATEMENT The identifiable target effect, more help to identified victims or stronger punishment to identifiable perpetrators, is common in daily life. We examined the neurocomputational mechanisms mediating/modulating the identifiability effect on third-party punishment by bridging literature from economics and cognitive neuroscience. Our findings reveal that identifiable transgressor effect is mediated by lower reference points of inequity (i.e., more stringent norms), which might be associated with a stronger involvement of the emotion processes and a weaker engagement of the analytic/deliberate processes. Furthermore, personality traits, altered brain activity, and intrinsic functional connectivity contribute to the individual variance in the identifiability effect. Overall, our study advances the understanding of the identifiability effect by shedding light on its component processes and modulating factors.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Xia Tian
- Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Yue-Jia Luo
- The State Key Lab of Cognitive and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
- Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, 266113, China
- School of Psychology, Chengdu Medical College, Chengdu, 610500, China
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33
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Wu H, Zhou C, Guan X, Bai X, Guo T, Wu J, Chen J, Wen J, Wu C, Cao Z, Liu X, Gao T, Gu L, Huang P, Xu X, Zhang B, Zhang M. Functional connectomes of akinetic-rigid and tremor within drug-naïve Parkinson's disease. CNS Neurosci Ther 2023; 29:3507-3517. [PMID: 37305965 PMCID: PMC10580330 DOI: 10.1111/cns.14284] [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: 08/12/2022] [Revised: 03/26/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023] Open
Abstract
AIMS To detect functional connectomes of akinetic-rigid (AR) and tremor and compare their connection pattern. METHODS Resting-state functional MRI data of 78 drug-naïve PD patients were enrolled to construct connectomes of AR and tremor via connectome-based predictive modeling (CPM). The connectomes were further validated with 17 drug-naïve patients to verify their replication. RESULTS The connectomes related to AR and tremor were identified via CPM method and successfully validated in the independent set. Additional regional-based CPM demonstrated neither AR nor tremor could be simplified to functional changes within a single brain region. Computational lesion version of CPM revealed that parietal lobe and limbic system were the most important regions among AR-related connectome, and motor strip and cerebellum were the most important regions among tremor-related connectome. Comparing two connectomes found that the patterns of connection between them were largely distinct, with only four overlapped connections identified. CONCLUSION AR and tremor were found to be associated with functional changes in multiple brain regions. Distinct connection patterns of AR-related and tremor-related connectomes suggest different neural mechanisms underlying the two symptoms.
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Affiliation(s)
- Haoting Wu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Tao Guo
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Chenqing Wu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Ting Gao
- Department of Neurology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Luyan Gu
- Department of Neurology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
| | - Minming Zhang
- Department of Radiology, The Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina
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34
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Bottenhorn KL, Cardenas-Iniguez C, Mills KL, Laird AR, Herting MM. Profiling intra- and inter-individual differences in brain development across early adolescence. Neuroimage 2023; 279:120287. [PMID: 37536527 PMCID: PMC10833064 DOI: 10.1016/j.neuroimage.2023.120287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/27/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.
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Affiliation(s)
- Katherine L Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA; Department of Psychology, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA.
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, 1227 University St, Eugene, OR 97403, USA
| | - Angela R Laird
- Department of Physics, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, CA 90032, USA.
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35
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Jiang C, He Y, Betzel RF, Wang YS, Xing XX, Zuo XN. Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability. Netw Neurosci 2023; 7:1080-1108. [PMID: 37781147 PMCID: PMC10473278 DOI: 10.1162/netn_a_00315] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 03/22/2023] [Indexed: 10/03/2023] Open
Abstract
A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).
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Affiliation(s)
- Chao Jiang
- School of Psychology, Capital Normal University, Beijing, China
| | - Ye He
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
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36
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Xing XX, Gao X, Jiang C. Individual Variability of Human Cortical Spontaneous Activity by 3T/7T fMRI. Neuroscience 2023; 528:117-128. [PMID: 37544577 DOI: 10.1016/j.neuroscience.2023.07.032] [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: 09/10/2022] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Mapping variability in cortical spontaneous activity (CSA) is an essential goal of understanding various sources of dark brain energy in human neuroscience. CSA was traditionally characterized using resting-state functional MRI (rfMRI) at 1.5T or 3T magnets while recently with 7T-rfMRI. However, the utility and interpretability of 7T-rfMRI must first be established for its variability. By leveraging rfMRI data from the Human Connectome Project (HCP), we derived CSA metrics with 3T-rfMRI and 7T-rfMRI for the same 84 healthy participants (52 females). The 7T-rfMRI produces different CSA metrics at multiple spatial-scales and their variability from the 3T-rfMRI. These differences were spatially dependent and varied according to specific cortical organization. For the amplitude metric, 7T-rfMRI enhanced its spatial contrasts in the anterior cortex but weakened it in the posterior cortex. An opposite pattern was observed for the connectivity metrics. The reliability changes of these metrics were scale dependent, indicating enhanced reliability for connectivity but weakened reliability for amplitude by 7T-rfMRI. These effects were primarily located in the high-order associate cortex, parsing the corresponding changes in individual differences with respect to 7T-rfMRI: (1) higher connectivity variability between participants and the lower connectivity variability within individual participants, and (2) lower amplitude variability between participants and higher amplitude variability within participants. Our work, for the first time, demonstrated the variability of the human CSA across space, rfMRI settings/platforms, and individuals. We discussed the statistical implications of our findings on CSA-based experimental designs and reproducible neuroscience as well as their translational value for personalized applications.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Xiao Gao
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Chao Jiang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
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37
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Geissmann L, Coynel D, Papassotiropoulos A, de Quervain DJF. Neurofunctional underpinnings of individual differences in visual episodic memory performance. Nat Commun 2023; 14:5694. [PMID: 37709747 PMCID: PMC10502056 DOI: 10.1038/s41467-023-41380-w] [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: 09/12/2022] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
Episodic memory, the ability to consciously recollect information and its context, varies substantially among individuals. While prior fMRI studies have identified certain brain regions linked to successful memory encoding at a group level, their role in explaining individual memory differences remains largely unexplored. Here, we analyze fMRI data of 1,498 adults participating in a picture encoding task in a single MRI scanner. We find that individual differences in responsivity of the hippocampus, orbitofrontal cortex, and posterior cingulate cortex account for individual variability in episodic memory performance. While these regions also emerge in our group-level analysis, other regions, predominantly within the lateral occipital cortex, are related to successful memory encoding but not to individual memory variation. Furthermore, our network-based approach reveals a link between the responsivity of nine functional connectivity networks and individual memory variability. Our work provides insights into the neurofunctional correlates of individual differences in visual episodic memory performance.
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Affiliation(s)
- Léonie Geissmann
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, Basel, Switzerland.
- Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland.
| | - David Coynel
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, Basel, Switzerland
- Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland
| | - Andreas Papassotiropoulos
- Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland
- Division of Molecular Neuroscience, Department of Biomedicine, University of Basel, Basel, Switzerland
- University Psychiatric Clinics, University of Basel, Basel, Switzerland
| | - Dominique J F de Quervain
- Division of Cognitive Neuroscience, Department of Biomedicine, University of Basel, Basel, Switzerland.
- Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland.
- University Psychiatric Clinics, University of Basel, Basel, Switzerland.
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38
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Neuser MP, Kühnel A, Kräutlein F, Teckentrup V, Svaldi J, Kroemer NB. Reliability of gamified reinforcement learning in densely sampled longitudinal assessments. PLOS DIGITAL HEALTH 2023; 2:e0000330. [PMID: 37672521 PMCID: PMC10482292 DOI: 10.1371/journal.pdig.0000330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 07/17/2023] [Indexed: 09/08/2023]
Abstract
Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22-0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.
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Affiliation(s)
- Monja P. Neuser
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Anne Kühnel
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- Department of Translational Psychiatry, Max Planck Institute of Psychiatry and International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Section of Medical Psychology, Department of Psychiatry & Psychotherapy, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Franziska Kräutlein
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- School of Psychology & Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Jennifer Svaldi
- Department of Psychology, Clinical Psychology and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Nils B. Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- School of Psychology & Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- German Center for Mental Health, Tübingen, Germany
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39
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Chen DY, Di X, Yu X, Biswal BB. The Significance and Limited Influence of Cerebrovascular Reactivity on Age and Sex Effects in Task- and Resting-State Brain Activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.18.553848. [PMID: 37662201 PMCID: PMC10473673 DOI: 10.1101/2023.08.18.553848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Functional MRI (fMRI) measures the blood-oxygen-level dependent (BOLD) signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state BOLD activity and may confound inter-individual effects observed in BOLD-based measures, such as those related to aging and biological sex. To investigate this, we examined a large open-access fMRI dataset containing a breath-holding task, checkerboard task, and resting-state scans. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and from the resting-state data, we quantified the resting-state amplitude of low-frequency fluctuations (ALFF), and resting-state regional homogeneity (ReHo). We hypothesized that cerebrovascular reactivity would be correlated with BOLD measures and that accounting for these correlations would result in better estimates of age and sex effects. Our analysis showed that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with ALFF and ReHo in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in ALFF and ReHo scores. Finally, we demonstrated that correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.
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Affiliation(s)
- Donna Y. Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
- Rutgers Biomedical and Health Sciences, Rutgers School of Graduate Studies, Newark, NJ, USA
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Xin Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Bharat B. Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
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40
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Dadashkarimi J, Karbasi A, Liang Q, Rosenblatt M, Noble S, Foster M, Rodriguez R, Adkinson B, Ye J, Sun H, Camp C, Farruggia M, Tejavibulya L, Dai W, Jiang R, Pollatou A, Scheinost D. Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Med Image Anal 2023; 88:102864. [PMID: 37352650 PMCID: PMC10526726 DOI: 10.1016/j.media.2023.102864] [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: 09/21/2022] [Revised: 02/10/2023] [Accepted: 05/31/2023] [Indexed: 06/25/2023]
Abstract
Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).
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Affiliation(s)
| | - Amin Karbasi
- Computer Science Department, Yale University, New Haven, CT, USA; Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA
| | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Raimundo Rodriguez
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Brendan Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Michael Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Wei Dai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Angeliki Pollatou
- Developing Brain Institute, Children's National Hospital, Washington DC, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Statistics & Data Science, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale University, New Haven, CT, USA
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41
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Tik N, Gal S, Madar A, Ben-David T, Bernstein-Eliav M, Tavor I. Generalizing prediction of task-evoked brain activity across datasets and populations. Neuroimage 2023; 276:120213. [PMID: 37268097 DOI: 10.1016/j.neuroimage.2023.120213] [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: 04/27/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023] Open
Abstract
Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.
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Affiliation(s)
- Niv Tik
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Shachar Gal
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Asaf Madar
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Tamar Ben-David
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Michal Bernstein-Eliav
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ido Tavor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel.
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42
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Meschke EX, Castello MVDO, la Tour TD, Gallant JL. Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.17.549356. [PMID: 37503232 PMCID: PMC10370105 DOI: 10.1101/2023.07.17.549356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.
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43
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Jacobs EAK, Ryu S. Larval zebrafish as a model for studying individual variability in translational neuroscience research. Front Behav Neurosci 2023; 17:1143391. [PMID: 37424749 PMCID: PMC10328419 DOI: 10.3389/fnbeh.2023.1143391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/22/2023] [Indexed: 07/11/2023] Open
Abstract
The larval zebrafish is a popular model for translational research into neurological and psychiatric disorders due to its conserved vertebrate brain structures, ease of genetic and experimental manipulation and small size and scalability to large numbers. The possibility of obtaining in vivo whole-brain cellular resolution neural data is contributing important advances into our understanding of neural circuit function and their relation to behavior. Here we argue that the larval zebrafish is ideally poised to push our understanding of how neural circuit function relates to behavior to the next level by including considerations of individual differences. Understanding variability across individuals is particularly relevant for tackling the variable presentations that neuropsychiatric conditions frequently show, and it is equally elemental if we are to achieve personalized medicine in the future. We provide a blueprint for investigating variability by covering examples from humans and other model organisms as well as existing examples from larval zebrafish. We highlight recent studies where variability may be hiding in plain sight and suggest how future studies can take advantage of existing paradigms for further exploring individual variability. We conclude with an outlook on how the field can harness the unique strengths of the zebrafish model to advance this important impending translational question.
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Affiliation(s)
- Elina A. K. Jacobs
- Institute of Human Genetics, University Medical Center of Johannes Gutenberg University Mainz, Mainz, Germany
| | - Soojin Ryu
- Institute of Human Genetics, University Medical Center of Johannes Gutenberg University Mainz, Mainz, Germany
- Living Systems Institute, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom
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44
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Xu T, Kiar G, Cho JW, Bridgeford EW, Nikolaidis A, Vogelstein JT, Milham MP. ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences. Nat Methods 2023:10.1038/s41592-023-01901-3. [PMID: 37264147 DOI: 10.1038/s41592-023-01901-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 04/28/2023] [Indexed: 06/03/2023]
Abstract
Characterizing multifaceted individual differences in brain function using neuroimaging is central to biomarker discovery in neuroscience. We provide an integrative toolbox, Reliability eXplorer (ReX), to facilitate the examination of individual variation and reliability as well as the effective direction for optimization of measuring individual differences in biomarker discovery. We also illustrate gradient flows, a two-dimensional field map-based approach to identifying and representing the most effective direction for optimization when measuring individual differences, which is implemented in ReX.
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Affiliation(s)
- Ting Xu
- Department of Brain Development, Child Mind Institute, New York, NY, USA.
| | - Gregory Kiar
- Department of Brain Development, Child Mind Institute, New York, NY, USA
| | - Jae Wook Cho
- Department of Brain Development, Child Mind Institute, New York, NY, USA
| | | | - Aki Nikolaidis
- Department of Brain Development, Child Mind Institute, New York, NY, USA
| | | | - Michael P Milham
- Department of Brain Development, Child Mind Institute, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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45
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Norbury A, Seeley SH, Perez-Rodriguez MM, Feder A. Functional neuroimaging of resilience to trauma: convergent evidence and challenges for future research. Psychol Med 2023; 53:3293-3305. [PMID: 37264949 DOI: 10.1017/s0033291723001162] [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] [Indexed: 06/03/2023]
Abstract
Resilience is broadly defined as the ability to adapt successfully following stressful life events. Here, we review functional MRI studies that investigated key psychological factors that have been consistently linked to resilience to severe adversity and trauma exposure. These domains include emotion regulation (including cognitive reappraisal), reward responsivity, and cognitive control. Further, we briefly review functional imaging evidence related to emerging areas of study that may potentially facilitate resilience: namely social cognition, active coping, and successful fear extinction. Finally, we also touch upon ongoing issues in neuroimaging study design that will need to be addressed to enable us to harness insight from such studies to improve treatments for - or, ideally, guard against the development of - debilitating post-traumatic stress syndromes.
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Affiliation(s)
- Agnes Norbury
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Queen Square Institute of Neurology and Mental Health Neuroscience Department, Applied Computational Psychiatry Lab, Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Saren H Seeley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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46
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Fan Y, Wang R, Yi C, Zhou L, Wu Y. Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification. iScience 2023; 26:106575. [PMID: 37250302 PMCID: PMC10214405 DOI: 10.1016/j.isci.2023.106575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/23/2022] [Accepted: 03/29/2023] [Indexed: 05/31/2023] Open
Abstract
The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests that brain modules overlap. However, little is known about the hierarchical overlapping modular structure in the brain. In this study, we developed a framework to uncover brain hierarchical overlapping modular structures based on a nested-spectral partition algorithm and an edge-centric network model. Overlap degree between brain modules is symmetrical across hemispheres, with highest overlap observed in the control and salience/ventral attention networks. Furthermore, brain edges are clustered into two groups: intrasystem and intersystem edges, to form hierarchical overlapping modules. At different levels, modules are self-similar in the degree of overlap. Additionally, the brain's hierarchical structure contains more individual identifiable information than a single-level structure, particularly in the control and salience/ventral attention networks. Our results offer pathways for future studies aimed at relating the organization of hierarchical overlapping modules to brain cognitive behavior and disorders.
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Affiliation(s)
- Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Rong Wang
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710049, China
| | - Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Lv Zhou
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
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47
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Green MA, Crawford JL, Kuhnen CM, Samanez-Larkin GR, Seaman KL. Multivariate associations between dopamine receptor availability and risky investment decision-making across adulthood. Cereb Cortex Commun 2023; 4:tgad008. [PMID: 37255569 PMCID: PMC10225308 DOI: 10.1093/texcom/tgad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 06/01/2023] Open
Abstract
Enhancing dopamine increases financial risk taking across adulthood but it is unclear whether baseline individual differences in dopamine function are related to risky financial decisions. Here, thirty-five healthy adults completed an incentive-compatible risky investment decision task and a PET scan at rest using [11C]FLB457 to assess dopamine D2-like receptor availability. Participants made choices between a safe asset (bond) and a risky asset (stock) with either an expected value less than the bond ("bad stock") or expected value greater than the bond ("good stock"). Five measures of behavior (choice inflexibility, risk seeking, suboptimal investment) and beliefs (absolute error, optimism) were computed and D2-like binding potential was extracted from four brain regions of interest (midbrain, amygdala, anterior cingulate, insula). We used canonical correlation analysis to evaluate multivariate associations between decision-making and dopamine function controlling for age. Decomposition of the first dimension (r = 0.76) revealed that the strongest associations were between measures of choice inflexibility, incorrect choice, optimism, amygdala binding potential, and age. Follow-up univariate analyses revealed that amygdala binding potential and age were both independently associated with choice inflexibility. The findings suggest that individual differences in dopamine function may be associated with financial risk taking in healthy adults.
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Affiliation(s)
- Mikella A Green
- Department of Psychology & Neuroscience, 417 Chapel Dr, Durham, NC 27708, Center for Cognitive Neuroscience, Duke University, 308 Research Drive, Durham, NC 27708
| | - Jennifer L Crawford
- Department of Psychology, Brandeis University, 415 South Street, Waltham, MA 02453
| | - Camelia M Kuhnen
- UNC Kenan-Flagler Business School, 300 Kenan Center Drive, Chapel Hill, NC 27599, National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138
| | - Gregory R Samanez-Larkin
- Department of Psychology & Neuroscience, 417 Chapel Dr, Durham, NC 27708, Center for Cognitive Neuroscience, Duke University, 308 Research Drive, Durham, NC 27708
| | - Kendra L Seaman
- Department of Psychology, University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080-3021, Center for Vital Longevity, University of Texas at Dallas, 1600 Viceroy Drive, Suite 800, Dallas, TX 75235
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48
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Wu D, Li X. Graph propagation network captures individual specificity of the relationship between functional and structural connectivity. Hum Brain Mapp 2023; 44:3885-3896. [PMID: 37186004 DOI: 10.1002/hbm.26320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Functional connectivity (FC) network characterizes the functional interactions between brain regions and is considered to root in the underlying structural connectivity (SC) network. If this is the case, individual variations in SC should cause corresponding individual variations in FC. However, divergences exist in the correspondence between direct SC and FC and researchers still cannot capture individual differences in FC via direct SC. As brain regions may interact through multi-hop indirect SC pathways, we conceived that one can capture the individual specific SC-FC relationship via incorporating indirect SC pathways appropriately. In this study, we designed graph propagation network (GPN) that models the information propagation between brain regions based on the SC network. Effects of interactions through multi-hop SC pathways naturally emerge from the multilayer information propagation in GPN. We predicted the individual differences in FC network based on SC network via multilayer GPN and results indicate that multilayer GPN incorporating effects of multi-hop indirect SCs greatly enhances the ability to predict individual FC network. Furthermore, the SC-FC relationship evaluated via the prediction accuracy is negatively correlated with the functional gradient, suggesting that the SC-FC relationship gradually uncouples along the functional hierarchy spanning from unimodal to transmodal cortex. We also revealed important intermediate brain regions along multi-hop SC pathways involving in the individual SC-FC relationship. These results suggest that multilayer GPN can serve as a method to establish individual SC-FC relationship at the macroneuroimaging level.
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Affiliation(s)
- Dongya Wu
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Xin Li
- School of Mathematics, Northwest University, Xi'an, China
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49
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Pat N, Wang Y, Bartonicek A, Candia J, Stringaris A. Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cereb Cortex 2023; 33:2682-2703. [PMID: 35697648 PMCID: PMC10016053 DOI: 10.1093/cercor/bhac235] [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: 02/04/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/13/2022] Open
Abstract
Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
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Affiliation(s)
- Narun Pat
- Corresponding author: Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand.
| | - Yue Wang
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Adam Bartonicek
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology National Institute on Aging, National Institute of Health, Branch, 251 Bayview Boulevard, Rm 05B113A, Biomedical Research Center, Baltimore, MD 21224, USA
| | - Argyris Stringaris
- Division of Psychiatry and Department of Clinical, Educational – Health Psychology, University College London, 1-19 Torrington Pl, London WC1E 7HB, United Kingdom
- Department of Psychiatry, National and Kapodistrian University of Athens, Medical School, Mikras Asias 75, Athina 115 27, Greece
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50
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Izakson L, Gal S, Shahar M, Tavor I, Levy DJ. Similar functional networks predict performance in both perceptual and value-based decision tasks. Cereb Cortex 2023; 33:2669-2681. [PMID: 35724432 DOI: 10.1093/cercor/bhac234] [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: 03/21/2022] [Revised: 05/16/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are numerous commonalities between perceptual and preferential decision processes. For instance, previous studies have shown that both of these decision types are influenced by context. Also, the same computational models can explain both. However, the neural processes and functional connections that underlie these similarities between perceptual and value-based decisions are still unclear. Hence, in the current study, we examine whether perceptual and preferential processes can be explained by similar functional networks utilizing data from the Human Connectome Project. We used resting-state functional magnetic resonance imaging data to predict performance of 2 different decision-making tasks: a value-related task (the delay discounting task) and a perceptual task (the flanker task). We then examined the existence of shared predictive-network features across these 2 decision tasks. Interestingly, we found a significant positive correlation between the functional networks, which predicted the value-based and perceptual tasks. In addition, a larger functional connectivity between visual and frontal decision brain areas was a critical feature in the prediction of both tasks. These results demonstrate that functional connections between perceptual and value-related areas in the brain are inherently related to decision-making processes across domains.
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Affiliation(s)
- Liz Izakson
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Coller School of Management, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Shachar Gal
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Moni Shahar
- Center of AI and Data Science, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Ido Tavor
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Strauss Center for Computational Neuroimaging, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Dino J Levy
- Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Coller School of Management, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
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