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Yue K, Webster J, Grabowski T, Shojaie A, Jahanian H. Iterative Data-adaptive Autoregressive (IDAR) whitening procedure for long and short TR fMRI. Front Neurosci 2024; 18:1381722. [PMID: 39156630 PMCID: PMC11327036 DOI: 10.3389/fnins.2024.1381722] [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: 02/04/2024] [Accepted: 06/17/2024] [Indexed: 08/20/2024] Open
Abstract
Introduction Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies. Methods In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets. Results Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively. Discussion This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.
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Affiliation(s)
- Kun Yue
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Jason Webster
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Thomas Grabowski
- Department of Radiology, University of Washington, Seattle, WA, United States
- Department of Neurology, University of Washington, Seattle, WA, United States
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Hesamoddin Jahanian
- Department of Radiology, University of Washington, Seattle, WA, United States
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Kampa M, Stark R, Klucken T. The impact of past childhood adversity and recent life events on neural responses during fear conditioning. J Neuroimaging 2024; 34:217-223. [PMID: 38009652 DOI: 10.1111/jon.13174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/15/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND AND PURPOSE Many studies have shown that exposure to life events can have a negative impact on mental health. Life events like the death of a spouse or the birth of a child pose a challenge and require temporal or permanent adjustments. Meta-analyses on brain stress responses found bilateral anterior insula activation in response to acute stress. Fear conditioning is assumed a crucial mechanism for the development of anxiety disorders associated with increased activation in the bilateral amygdala. Empirical evidence is lacking regarding the relationship of exposure to recent life events and past childhood adversity with neural processing during fear conditioning. METHODS In the present study, we analyzed data from 103 young, healthy participants. Multiple linear regressions were performed on functional magnetic resonance imaging activation during fear conditioning with the Life Events Scale for Students and the Childhood Trauma questionnaire included as covariates in two separate models. RESULTS We found a positive relationship between the number of life events in the last year and left amygdala activation to the conditioned stimulus. A second finding was a positive relationship between childhood adversity and right anterior insula response to the unconditioned stimulus. CONCLUSIONS Many studies have shown increased amygdala activity after stressful life events. In addition, the anterior insula is activated during acute stress. The present study points to stressor-induced increased salience processing during fear conditioning. We suggest that this could be a potential mechanism for resilience versus mental illness.
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Affiliation(s)
- Miriam Kampa
- Department of Clinical Psychology and Psychotherapy, University of Siegen, Siegen, Germany
- Bender Institute of Neuroimaging, Justus Liebig University, Giessen, Germany
| | - Rudolf Stark
- Bender Institute of Neuroimaging, Justus Liebig University, Giessen, Germany
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University Giessen, Giessen, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Tim Klucken
- Department of Clinical Psychology and Psychotherapy, University of Siegen, Siegen, Germany
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic fMRI-derived functional connectomes represent largely similar information. Netw Neurosci 2023; 7:1266-1301. [PMID: 38144686 PMCID: PMC10631791 DOI: 10.1162/netn_a_00325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/06/2023] [Indexed: 12/26/2023] Open
Abstract
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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Hearne LJ, Breakspear M, Harrison BJ, Hall CV, Savage HS, Robinson C, Sonkusare S, Savage E, Nott Z, Marcus L, Naze S, Burgher B, Zalesky A, Cocchi L. Revisiting deficits in threat and safety appraisal in obsessive-compulsive disorder. Hum Brain Mapp 2023; 44:6418-6428. [PMID: 37853935 PMCID: PMC10681637 DOI: 10.1002/hbm.26518] [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: 04/19/2023] [Revised: 08/22/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Current behavioural treatment of obsessive-compulsive disorder (OCD) is informed by fear conditioning and involves iteratively re-evaluating previously threatening stimuli as safe. However, there is limited research investigating the neurobiological response to conditioning and reversal of threatening stimuli in individuals with OCD. A clinical sample of individuals with OCD (N = 45) and matched healthy controls (N = 45) underwent functional magnetic resonance imaging. While in the scanner, participants completed a well-validated fear reversal task and a resting-state scan. We found no evidence for group differences in task-evoked brain activation or functional connectivity in OCD. Multivariate analyses encompassing all participants in the clinical and control groups suggested that subjective appraisal of threatening and safe stimuli were associated with a larger difference in brain activity than the contribution of OCD symptoms. In particular, we observed a brain-behaviour continuum whereby heightened affective appraisal was related to increased bilateral insula activation during the task (r = 0.39, pFWE = .001). These findings suggest that changes in conditioned threat-related processes may not be a core neurobiological feature of OCD and encourage further research on the role of subjective experience in fear conditioning.
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Affiliation(s)
- Luke J. Hearne
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Michael Breakspear
- College of Engineering Science and Environment, College of Health and MedicineUniversity of NewcastleCallaghanNew South WalesAustralia
| | - Ben J. Harrison
- Melbourne Neuropsychiatry Centre, Department of PsychiatryThe University of Melbourne & Melbourne HealthMelbourneVictoriaAustralia
| | - Caitlin V. Hall
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Hannah S. Savage
- College of Engineering Science and Environment, College of Health and MedicineUniversity of NewcastleCallaghanNew South WalesAustralia
| | - Conor Robinson
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | | | - Emma Savage
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Zoie Nott
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Leo Marcus
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Sebastien Naze
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Bjorn Burgher
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of PsychiatryThe University of Melbourne & Melbourne HealthMelbourneVictoriaAustralia
| | - Luca Cocchi
- QIMR Berghofer Medical Research InstituteBrisbaneQLDAustralia
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Sugawara SK, Yamamoto T, Nakayama Y, Hamano YH, Fukunaga M, Sadato N, Nishimura Y. Premovement activity in the mesocortical system links peak force but not initiation of force generation under incentive motivation. Cereb Cortex 2023; 33:11408-11419. [PMID: 37814358 PMCID: PMC10690858 DOI: 10.1093/cercor/bhad376] [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: 04/01/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/11/2023] Open
Abstract
Motivation facilitates motor performance; however, the neural substrates of the psychological effects on motor performance remain unclear. We conducted a functional magnetic resonance imaging experiment while human subjects performed a ready-set-go task with monetary incentives. Although subjects were only motivated to respond quickly, increasing the incentives improved not only reaction time but also peak grip force. However, the trial-by-trial correlation between reaction time and peak grip force was weak. Extensive areas in the mesocortical system, including the ventral midbrain (VM) and cortical motor-related areas, exhibited motivation-dependent activity in the premovement "Ready" period when the anticipated monetary reward was displayed. This premovement activity in the mesocortical system correlated only with subsequent peak grip force, whereas the activity in motor-related areas alone was associated with subsequent reaction time and peak grip force. These findings suggest that the mesocortical system linking the VM and motor-related regions plays a role in controlling the peak of force generation indirectly associated with incentives but not the initiation of force generation.
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Affiliation(s)
- Sho K Sugawara
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Hayama, Kanagawa 340-0193, Japan
| | - Tetsuya Yamamoto
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Yoshihisa Nakayama
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan
| | - Yuki H Hamano
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Masaki Fukunaga
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Hayama, Kanagawa 340-0193, Japan
| | - Norihiro Sadato
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Hayama, Kanagawa 340-0193, Japan
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | - Yukio Nishimura
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan
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Fernandes FF, Olesen JL, Jespersen SN, Shemesh N. MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading". Neuroimage 2023; 273:120118. [PMID: 37062372 DOI: 10.1016/j.neuroimage.2023.120118] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 04/10/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023] Open
Abstract
MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective threshold to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation "spreading" caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms temporal resolution, respectively), during visual stimulation. MP-PCA denoising produced SNR gains of 64% and 39% and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation "spreading" with increased false positive rate and smoother functional maps due to local "bleeding" of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This "spreading" effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.
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Affiliation(s)
| | - Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
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Coughlan G, Bouffard NR, Golestani A, Thakral PP, Schacter DL, Grady C, Moscovitch M. Transcranial magnetic stimulation to the angular gyrus modulates the temporal dynamics of the hippocampus and entorhinal cortex. Cereb Cortex 2023; 33:3255-3264. [PMID: 36573400 PMCID: PMC10016030 DOI: 10.1093/cercor/bhac273] [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: 11/02/2021] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 12/28/2022] Open
Abstract
Transcranial magnetic stimulation (TMS) delivered to the angular gyrus (AG) affects hippocampal function and associated behaviors (Thakral PP, Madore KP, Kalinowski SE, Schacter DL. Modulation of hippocampal brain networks produces changes in episodic simulation and divergent thinking. 2020a. Proc Natl Acad Sci U S A. 117:12729-12740). Here, we examine if functional magnetic resonance imaging (fMRI)-guided TMS disrupts the gradient organization of temporal signal properties, known as the temporal organization, in the hippocampus (HPC) and entorhinal cortex (ERC). For each of 2 TMS sessions, TMS was applied to either a control site (vertex) or to a left AG target region (N = 18; 14 females). Behavioral measures were then administered, and resting-state scans were acquired. Temporal dynamics were measured by tracking change in the fMRI signal (i) "within" single voxels over time, termed single-voxel autocorrelation and (ii) "between" different voxels over time, termed intervoxel similarity. TMS reduced AG connectivity with the hippocampal target and induced more rapid shifting of activity in single voxels between successive time points, lowering the single-voxel autocorrelation, within the left anteromedial HPC and posteromedial ERC. Intervoxel similarity was only marginally affected by TMS. Our findings suggest that hippocampal-targeted TMS disrupts the functional properties of the target site along the anterior/posterior axis. Further studies should examine the consequences of altering the temporal dynamics of these medial temporal areas to the successful processing of episodic information under task demand.
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Affiliation(s)
- Gillian Coughlan
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, North York, Ontario M6A 2E1, Canada
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 15 Parkman St, Boston, MA 02114, United States
| | - Nichole R Bouffard
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, North York, Ontario M6A 2E1, Canada
- Department of Psychology, University of Toronto, 27 King's College Cir, Toronto, Ontario M5S 3G3, Canada
| | - Ali Golestani
- Department of Psychology, University of Toronto, 27 King's College Cir, Toronto, Ontario M5S 3G3, Canada
| | - Preston P Thakral
- Department of Psychology, Harvard University, 33 Kirkland St, Cambridge, MA 02138, United States
- Department of Psychology and Neuroscience, Boston College, 140 Commonwealth Ave, Chestnut Hill, MA 02467, United States
| | - Daniel L Schacter
- Department of Psychology, Harvard University, 33 Kirkland St, Cambridge, MA 02138, United States
| | - Cheryl Grady
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, North York, Ontario M6A 2E1, Canada
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, Ontario M5T 1R8, Canada
| | - Morris Moscovitch
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, North York, Ontario M6A 2E1, Canada
- Department of Psychology, University of Toronto, 27 King's College Cir, Toronto, Ontario M5S 3G3, Canada
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Bouffard NR, Golestani A, Brunec IK, Bellana B, Park JY, Barense MD, Moscovitch M. Single voxel autocorrelation uncovers gradients of temporal dynamics in the hippocampus and entorhinal cortex during rest and navigation. Cereb Cortex 2023; 33:3265-3283. [PMID: 36573396 PMCID: PMC10388386 DOI: 10.1093/cercor/bhac480] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 12/28/2022] Open
Abstract
During navigation, information at multiple scales needs to be integrated. Single-unit recordings in rodents suggest that gradients of temporal dynamics in the hippocampus and entorhinal cortex support this integration. In humans, gradients of representation are observed, such that granularity of information represented increases along the long axis of the hippocampus. The neural underpinnings of this gradient in humans, however, are still unknown. Current research is limited by coarse fMRI analysis techniques that obscure the activity of individual voxels, preventing investigation of how moment-to-moment changes in brain signal are organized and how they are related to behavior. Here, we measured the signal stability of single voxels over time to uncover previously unappreciated gradients of temporal dynamics in the hippocampus and entorhinal cortex. Using our novel, single voxel autocorrelation technique, we show a medial-lateral hippocampal gradient, as well as a continuous autocorrelation gradient along the anterolateral-posteromedial entorhinal extent. Importantly, we show that autocorrelation in the anterior-medial hippocampus was modulated by navigational difficulty, providing the first evidence that changes in signal stability in single voxels are relevant for behavior. This work opens the door for future research on how temporal gradients within these structures support the integration of information for goal-directed behavior.
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Affiliation(s)
- Nichole R Bouffard
- Department of Psychology, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada
- Rotman Research Institute, Baycrest Health Sciences, 3650 Baycrest Street, Toronto, ON M6A 2E1, Canada
| | - Ali Golestani
- Department of Psychology, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada
| | - Iva K Brunec
- Department of Psychology, Temple University, 1701 North 13th Street, Philadelphia, PA 19122, USA
- Department of Psychology, University of Pennsylvania, 3720 Walnut Street, Philadelphia, PA 19104, USA
| | - Buddhika Bellana
- Department of Psychology, Glendon College—York University, 2275 Bayview Ave, North York, ON M4N 3M6, Canada
| | - Jun Young Park
- Department of Psychology, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada
- Department of Statistical Sciences, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada
| | - Morgan D Barense
- Department of Psychology, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada
- Rotman Research Institute, Baycrest Health Sciences, 3650 Baycrest Street, Toronto, ON M6A 2E1, Canada
| | - Morris Moscovitch
- Department of Psychology, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada
- Rotman Research Institute, Baycrest Health Sciences, 3650 Baycrest Street, Toronto, ON M6A 2E1, Canada
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Parlak F, Pham DD, Spencer DA, Welsh RC, Mejia AF. Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation. Front Neurosci 2023; 16:1051424. [PMID: 36685218 PMCID: PMC9847678 DOI: 10.3389/fnins.2022.1051424] [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: 11/22/2022] [Accepted: 12/09/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction Analysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform "prewhitening" to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates. Methods In this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI. Results We find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives. Conclusion Our analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.
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Affiliation(s)
- Fatma Parlak
- Department of Statistics, Indiana University, Bloomington, IN, United States
| | - Damon D. Pham
- Department of Statistics, Indiana University, Bloomington, IN, United States
| | - Daniel A. Spencer
- Department of Statistics, Indiana University, Bloomington, IN, United States
| | - Robert C. Welsh
- Department of Psychiatry and Bio-behavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Amanda F. Mejia
- Department of Statistics, Indiana University, Bloomington, IN, United States,*Correspondence: Amanda F. Mejia ✉
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Fazal Z, Gomez DEP, Llera A, Marques JPRF, Beck T, Poser BA, Norris DG. A comparison of multiband and multiband multiecho gradient-echo EPI for task fMRI at 3 T. Hum Brain Mapp 2022; 44:82-93. [PMID: 36196782 PMCID: PMC9783458 DOI: 10.1002/hbm.26081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 08/05/2022] [Accepted: 08/16/2022] [Indexed: 02/05/2023] Open
Abstract
A multiband (MB) echo-planar imaging (EPI) sequence is compared to a multiband multiecho (MBME) EPI protocol to investigate differences in sensitivity for task functional magnetic resonance imaging (fMRI) at 3 T. Multiecho sampling improves sensitivity in areas where single-echo-EPI suffers from dropouts. However, It requires in-plane acceleration to reduce the echo train length, limiting the slice acceleration factor and the temporal and spatial resolution Data were acquired for both protocols in two sessions 24 h apart using an adapted color-word interference Stroop task. Besides protocol comparison statistically, we performed test-retest reliability across sessions for different protocols and denoising methods. We evaluated the sensitivity of two different echo-combination strategies for MBME-EPI. We examined the performance of three different data denoising approaches: "Standard," "AROMA," and "FIX" for MB and MBME, and assessed whether a specific method is preferable. We consider using an appropriate autoregressive model order within the general linear model framework to correct TR differences between the protocols. The comparison between protocols and denoising methods showed at group level significantly higher mean z-scores and the number of active voxels for MBME in the motor, subcortical and medial frontal cortices. When comparing different echo combinations, our results suggest that a contrast-to-noise ratio weighted echo combination improves sensitivity in MBME compared to simple echo-summation. This study indicates that MBME can be a preferred protocol in task fMRI at spatial resolution (≥2 mm), primarily in medial prefrontal and subcortical areas.
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Affiliation(s)
- Zahra Fazal
- Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Cognitive NeuroimagingRadboud University NijmegenNijmegenThe Netherlands
| | - Daniel E. P. Gomez
- Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Cognitive NeuroimagingRadboud University NijmegenNijmegenThe Netherlands
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMassachusettsUSA
- Present address:
Department of Biomedical EngineeringBoston UniversityBostonMassachusettsUSA
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Cognitive NeuroimagingRadboud University NijmegenNijmegenThe Netherlands
| | - José P. R. F. Marques
- Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Cognitive NeuroimagingRadboud University NijmegenNijmegenThe Netherlands
| | | | - Benedikt A. Poser
- Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtNetherlands
| | - David G. Norris
- Donders Institute for Brain, Cognition and Behaviour, Donders Centre for Cognitive NeuroimagingRadboud University NijmegenNijmegenThe Netherlands
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, UNESCO‐Weltkulturerbe Zollverein, Leitstand Kokerei ZollvereinEssenGermany
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11
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Lanka P, Bortfeld H, Huppert TJ. Correction of global physiology in resting-state functional near-infrared spectroscopy. NEUROPHOTONICS 2022; 9:035003. [PMID: 35990173 PMCID: PMC9386281 DOI: 10.1117/1.nph.9.3.035003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 07/08/2022] [Indexed: 05/30/2023]
Abstract
Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.
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Affiliation(s)
- Pradyumna Lanka
- University of California, Merced, Department of Psychological Sciences, Merced, California, United States
| | - Heather Bortfeld
- University of California, Merced, Department of Psychological Sciences, Merced, California, United States
- University of California, Merced, Department of Cognitive and Information Sciences, Merced, California, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
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12
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Nakayama Y, Sugawara SK, Fukunaga M, Hamano YH, Sadato N, Nishimura Y. The dorsal premotor cortex encodes the step-by-step planning processes for goal-directed motor behavior in humans. Neuroimage 2022; 256:119221. [PMID: 35447355 DOI: 10.1016/j.neuroimage.2022.119221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 10/18/2022] Open
Abstract
The dorsal premotor cortex (PMd) plays an essential role in visually guided goal-directed motor behavior. Although there are several planning processes for achieving goal-directed behavior, the separate neural processes are largely unknown. Here, we created a new visuo-goal task to investigate the step-by-step planning processes for visuomotor and visuo-goal behavior in humans. Using functional magnetic resonance imaging, we found activation in different portions of the bilateral PMd during each processing step. In particular, the activated area for rule-based visuomotor and visuo-goal mapping was located at the ventrorostral portion of the bilateral PMd, that for action plan specification was at the dorsocaudal portion of the left PMd, that for transformation was at the rostral portion of the left PMd, and that for action preparation was at the caudal portion of the bilateral PMd. Thus, the left PMd was involved throughout all of the processes, but the right PMd was involved only in rule-based visuomotor and visuo-goal mapping and action preparation. The locations related to each process were generally spatially separated from each other, but they overlapped partially. These findings revealed that there are functional subregions in the bilateral PMd in humans and these subregions form a functional gradient to achieve goal-directed behavior.
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Affiliation(s)
- Yoshihisa Nakayama
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Kamikitazawa 2-1-6, Setagaya, Tokyo 156-8506, Japan; Frontal Lobe Function Project, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo 156-8506, Japan.
| | - Sho K Sugawara
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Kamikitazawa 2-1-6, Setagaya, Tokyo 156-8506, Japan; Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan; Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan
| | - Yuki H Hamano
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan; Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan
| | - Yukio Nishimura
- Neural Prosthetics Project, Tokyo Metropolitan Institute of Medical Science, Kamikitazawa 2-1-6, Setagaya, Tokyo 156-8506, Japan
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13
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Guran CNA, Deuker L, Göttlich M, Axmacher N, Bunzeck N. Benefit from retrieval practice is linked to temporal and frontal activity in healthy young and older humans. Cereb Cortex Commun 2022; 3:tgac009. [PMID: 35372838 PMCID: PMC8966694 DOI: 10.1093/texcom/tgac009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 11/29/2022] Open
Abstract
Retrieval practice improves retention of information in long-term memory more than restudy, but the underlying neural mechanisms of this "retrieval practice effect" (RPE) remain poorly understood. Therefore, we investigated the behavioral and neural differences between previously retrieved versus restudied items at final retrieval. Thirty younger (20-30 years old) and twenty-five older (50+ years old) adults learned familiar and new picture stimuli either through retrieval or restudy. At final recognition, hemodynamic activity was measured using functional magnetic resonance imaging (fMRI). Behaviorally, younger and older adults showed similar benefits of retrieval practice, with higher recollection, but unchanged familiarity rates. In a univariate analysis of the fMRI data, activation in medial prefrontal cortex and left temporal regions correlated with an individual's amount of behavioral benefit from retrieval practice, irrespective of age. Compatible with this observation, in a multivariate representational similarity analysis (RSA), retrieval practice led to an increase in pattern similarity for retested items in a priori defined regions of interest, including the medial temporal lobe, as well as prefrontal and parietal cortex. Our findings demonstrate that retrieval practice leads to enhanced long-term memories in younger and older adults alike, and this effect may be driven by fast consolidation processes.
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Affiliation(s)
- Catherine-Noémie Alexandrina Guran
- Department of Psychology I, University of Lübeck, Maria-Goeppert-Straße 9a, Lübeck 23562, Germany
- Vienna Cognitive Science Hub, University of Vienna, Kolingasse 14-16, Vienna 1010, Austria
| | - Lorena Deuker
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany
| | - Martin Göttlich
- Department of Neurology, University Hospital Schleswig-Holstein, University of Lübeck, Ratzeburger Allee 160, Lübeck 23538, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, Lübeck 23562, Germany
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany
| | - Nico Bunzeck
- Department of Psychology I, University of Lübeck, Maria-Goeppert-Straße 9a, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, Lübeck 23562, Germany
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14
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Polimeni JR, Lewis LD. Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response. Prog Neurobiol 2021; 207:102174. [PMID: 34525404 PMCID: PMC8688322 DOI: 10.1016/j.pneurobio.2021.102174] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 07/30/2021] [Accepted: 09/08/2021] [Indexed: 12/20/2022]
Abstract
Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Laura D Lewis
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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15
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Vizioli L, Yacoub E, Lewis LD. How pushing the spatiotemporal resolution of fMRI can advance neuroscience. Prog Neurobiol 2021; 207:102184. [PMID: 34767874 DOI: 10.1016/j.pneurobio.2021.102184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States.
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA United States; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA United States
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16
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Jamil R, Mauconduit F, Le Ster C, Ehses P, Poser BA, Vignaud A, Boulant N. Temporal SNR optimization through RF coil combination in fMRI: The more, the better? PLoS One 2021; 16:e0259592. [PMID: 34748584 PMCID: PMC8575292 DOI: 10.1371/journal.pone.0259592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/21/2021] [Indexed: 11/19/2022] Open
Abstract
For functional MRI with a multi-channel receiver RF coil, images are often reconstructed channel by channel, resulting into multiple images per time frame. The final image to analyze usually is the result of the covariance Sum-of-Squares (covSoS) combination across these channels. Although this reconstruction is quasi-optimal in SNR, it is not necessarily the case in terms of temporal SNR (tSNR) of the time series, which is yet a more relevant metric for fMRI data quality. In this work, we investigated tSNR optimality through voxel-wise RF coil combination and its effects on BOLD sensitivity. An analytical solution for an optimal RF coil combination is described, which is somewhat tied to the extended Krueger-Glover model involving both thermal and physiological noise covariance matrices. Compared experimentally to covSOS on four volunteers at 7T, the method yielded great improvement of tSNR but, surprisingly, did not result into higher BOLD sensitivity. Solutions to improve the method such as for example the t-score for the mean recently proposed are also explored, but result into similar observations once the statistics are corrected properly. Overall, the work shows that data-driven RF coil combinations based on tSNR considerations alone should be avoided unless additional and unbiased assumptions can be made.
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Affiliation(s)
- Redouane Jamil
- CEA, CNRS, BAOBAB, NeuroSpin, Paris-Saclay University, Gif-sur-Yvette, France
| | - Franck Mauconduit
- CEA, CNRS, BAOBAB, NeuroSpin, Paris-Saclay University, Gif-sur-Yvette, France
| | - Caroline Le Ster
- CEA, CNRS, BAOBAB, NeuroSpin, Paris-Saclay University, Gif-sur-Yvette, France
| | - Philipp Ehses
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alexandre Vignaud
- CEA, CNRS, BAOBAB, NeuroSpin, Paris-Saclay University, Gif-sur-Yvette, France
| | - Nicolas Boulant
- CEA, CNRS, BAOBAB, NeuroSpin, Paris-Saclay University, Gif-sur-Yvette, France
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17
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Abstract
The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.
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Affiliation(s)
- Zinong Yang
- Graduate Program in Neuroscience, Boston University, Boston MA, United States
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston MA, United States.,Center for Systems Neuroscience, Boston University, Boston MA, United States
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18
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Raimondo L, Oliveira ĹAF, Heij J, Priovoulos N, Kundu P, Leoni RF, van der Zwaag W. Advances in resting state fMRI acquisitions for functional connectomics. Neuroimage 2021; 243:118503. [PMID: 34479041 DOI: 10.1016/j.neuroimage.2021.118503] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 01/21/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.
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Affiliation(s)
- Luisa Raimondo
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Ĺcaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Jurjen Heij
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | | | - Prantik Kundu
- Hyperfine Research Inc, Guilford, CT, United States; Icahn School of Medicine at Mt. Sinai, New York, United States
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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19
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Dowdle LT, Ghose G, Chen CCC, Ugurbil K, Yacoub E, Vizioli L. Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI. Prog Neurobiol 2021; 207:102171. [PMID: 34492308 DOI: 10.1016/j.pneurobio.2021.102171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 01/25/2023]
Abstract
Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States.
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States
| | - Clark C C Chen
- Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States.
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20
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Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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21
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Deifelt Streese C, Tranel D. Combined lesion-deficit and fMRI approaches in single-case studies: Unique contributions to cognitive neuroscience. Curr Opin Behav Sci 2021; 40:58-63. [PMID: 33709012 PMCID: PMC7943030 DOI: 10.1016/j.cobeha.2021.01.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Although lesion-deficit case studies are foundational in cognitive neuroscience, published papers presenting single lesion cases are declining. In this review, we argue that there is a valuable place for single-case lesion-deficit research, especially when combined with functional neuroimaging methods, such as functional magnetic resonance imaging (fMRI). To support this, we present a summary of notable findings from single-case combined lesion-deficit and fMRI studies published in recent years (2017-2020). These studies show the unique value that this combined approach brings to the understanding of complex functions, brain-level connectivity, and plasticity and recovery. We encourage researchers to consider combining lesion-deficit and functional imaging methods in the analysis of single cases, as this approach affords unique opportunities to address challenging unanswered questions about brain-behavior relationships.
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Affiliation(s)
- Carolina Deifelt Streese
- Department of Neurology; Carver College of Medicine; 200 Hawkins Drive, Iowa City, IA, 52242; United States
| | - Daniel Tranel
- Department of Neurology; Carver College of Medicine; 200 Hawkins Drive, Iowa City, IA, 52242; United States
- Department of Psychological and Brain Sciences; University of Iowa; 340 Iowa Avenue, Iowa City, IA, 52242; United States
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22
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Risk BB, Murden RJ, Wu J, Nebel MB, Venkataraman A, Zhang Z, Qiu D. Which multiband factor should you choose for your resting-state fMRI study? Neuroimage 2021; 234:117965. [PMID: 33744454 PMCID: PMC8159874 DOI: 10.1016/j.neuroimage.2021.117965] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/05/2021] [Accepted: 03/06/2021] [Indexed: 12/30/2022] Open
Abstract
Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.
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Affiliation(s)
- Benjamin B Risk
- Department of Biostatistics and Bioinformatics, Atlanta, GA, United States.
| | - Raphiel J Murden
- Department of Biostatistics and Bioinformatics, Atlanta, GA, United States
| | - Junjie Wu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Arun Venkataraman
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States
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23
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Hamano YH, Sugawara SK, Fukunaga M, Sadato N. The integrative role of the M1 in motor sequence learning. Neurosci Lett 2021; 760:136081. [PMID: 34171404 DOI: 10.1016/j.neulet.2021.136081] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/18/2021] [Accepted: 06/20/2021] [Indexed: 11/29/2022]
Abstract
The primary motor cortex (M1) is crucial in motor learning. Whether the M1 encodes the motor engram for sequential finger tapping formed by an emphasis on speed is still inconclusive. The active states of engrams are hard to discriminate from the motor execution per se. As preparatory activity reflects the upcoming movement parameters, we hypothesized that the retrieval of motor engrams generated by different learning modes is reflected as a learning-related increase in the preparatory activity of the M1. To test this hypothesis, we evaluated the preparatory activity during the learning of sequential finger-tapping with the non-dominant left hand using a 7T functional MRI. Participants alternated between performing a tapping sequence as quickly as possible (maximum mode) or at a constant speed of 2 Hz paced by a sequence-specifying visual cue (constant mode). We found a training-related increase in preparatory activity in the network covering the bilateral anterior intraparietal sulcus and inferior parietal lobule extending to the right M1 during the maximum mode and the right M1 during the constant mode. These findings indicate that the M1, as the last effector of the motor output, integrates the motor engram distributed through the networks despite training mode differences.
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Affiliation(s)
- Yuki H Hamano
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi 444-8585, Japan; Department of Physiological Sciences, School of Life Sciences, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa 240-0193, Japan
| | - Sho K Sugawara
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi 444-8585, Japan; Department of Physiological Sciences, School of Life Sciences, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa 240-0193, Japan; Neural Prosthesis Project, Department of Dementia and Higher Brain Function, Tokyo Metropolitan Institute of Medical Science, 2-1-6, Kamikitazawa, Setagaya, Tokyo 158-8506, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi 444-8585, Japan; Department of Physiological Sciences, School of Life Sciences, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa 240-0193, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi 444-8585, Japan; Department of Physiological Sciences, School of Life Sciences, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa 240-0193, Japan.
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Barghoorn A, Riemenschneider B, Hennig J, LeVan P. Improving the sensitivity of spin-echo fMRI at 3T by highly accelerated acquisitions. Magn Reson Med 2021; 86:245-257. [PMID: 33624352 DOI: 10.1002/mrm.28715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 01/14/2021] [Accepted: 01/14/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE Spin-echo (SE) functional MRI (fMRI) can be highly advantageous compared to gradient-echo (GE) fMRI with respect to magnetic field-inhomogeneity artifacts. However, at 3T, the majority of blood oxygenation level-dependent (BOLD) fMRI experiments are performed using T 2 ∗ -weighted GE sequences because of their superior sensitivity compared to SE-fMRI. The presented SE implementation of a highly accelerated GE pulse sequence therefore aims to improve the sensitivity of SE-fMRI while profiting from a reduction of susceptibility-induced signal dropout. METHODS Spin-echo MR encephalography (SE-MREG) is compared with the more conventionally used spin-echo echo-planar imaging (SE-EPI) and spin-echo simultaneous multislice (SE-SMS) at 3T in terms of capability to detect neuronal activations and resting-state functional connectivity. For activation analysis, healthy subjects underwent consecutive SE-MREG (pulse repetition time [TR] = 0.25 seconds), SE-SMS (TR = 1.3 seconds), and SE-EPI (TR = 4.4 seconds) scans in pseudorandomized order applied to a visual block design paradigm for generation of t-statistics maps. For the investigation of functional connectivity, additional resting-state data were acquired for 5 minutes and a seed-based correlation analysis using Stanford's FIND (Functional Imaging in Neuropsychiatric Disorders) atlas was performed. RESULTS The increased sampling rate of SE-MREG relative to SE-SMS and SE-EPI improves the sensitivity to detect BOLD activation by 33% and 54%, respectively, and increases the capability to extract resting-state networks. Compared with a brain region that is not affected by magnetic field inhomogeneities, SE-MREG shows 2.5 times higher relative signal strength than GE-MREG in mesial temporal structures. CONCLUSION SE-MREG offers a viable possibility for whole-brain fMRI with consideration of brain regions that are affected by strong susceptibility-induced magnetic field gradients.
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Affiliation(s)
- Antonia Barghoorn
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bruno Riemenschneider
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Departments of Radiology and Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada
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Naseri P, Alavi Majd H, Tabatabaei SM, Khadembashi N, Najibi SM, Nazari A. Functional Brain Response to Emotional Musical Stimuli in Depression, Using INLA Approach for Approximate Bayesian Inference. Basic Clin Neurosci 2021; 12:95-104. [PMID: 33995932 PMCID: PMC8114858 DOI: 10.32598/bcn.9.10.480] [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: 06/24/2018] [Revised: 07/10/2018] [Accepted: 03/10/2019] [Indexed: 11/28/2022] Open
Abstract
Introduction: One of the vital skills which has an impact on emotional health and well-being is the regulation of emotions. In recent years, the neural basis of this process has been considered widely. One of the powerful tools for eliciting and regulating emotion is music. The Anterior Cingulate Cortex (ACC) is part of the emotional neural circuitry involved in Major Depressive Disorder (MDD). The current study uses functional Magnetic Resonance Imaging (fMRI) to examine how neural processing of emotional musical auditory stimuli is changed within the ACC in depression. Statistical inference is conducted using a Bayesian Generalized Linear Model (GLM) approach with an Integrated Nested Laplace Approximation (INLA) algorithm. Methods: A new proposed Bayesian approach was applied for assessing functional response to emotional musical auditory stimuli in a block design fMRI data with 105 scans of two healthy and depressed women. In this Bayesian approach, Unweighted Graph-Laplacian (UGL) prior was chosen for spatial dependency, and autoregressive (AR) (1) process was used for temporal correlation via pre-weighting residuals. Finally, the inference was conducted using the Integrated Nested Laplace Approximation (INLA) algorithm in the R-INLA package. Results: The results revealed that positive music, as compared to negative music, elicits stronger activation within the ACC area in both healthy and depressed subjects. In comparing MDD and Never-Depressed (ND) individuals, a significant difference was found between MDD and ND groups in response to positive music vs negative music stimuli. The activations increase from baseline to positive stimuli and decrease from baseline to negative stimuli in ND subjects. Also, a significant decrease from baseline to positive stimuli was observed in MDD subjects, but there was no significant difference between baseline and negative stimuli. Conclusion: Assessing the pattern of activations within ACC in a depressed individual may be useful in retraining the ACC and improving its function, and lead to more effective therapeutic interventions.
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Affiliation(s)
- Parisa Naseri
- Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Alavi Majd
- Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyyed Mohammad Tabatabaei
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Naghmeh Khadembashi
- Department of English Language, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Atiye Nazari
- Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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26
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Kundu S, Risk BB. Scalable Bayesian matrix normal graphical models for brain functional networks. Biometrics 2020; 77:439-450. [PMID: 32569385 DOI: 10.1111/biom.13319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 06/04/2020] [Indexed: 01/23/2023]
Abstract
Recently, there has been an explosive growth in graphical modeling approaches for estimating brain functional networks. In a detailed study, we show that surprisingly, standard graphical modeling approaches for fMRI data may not yield accurate estimates of the brain network due to the inability to suitably account for temporal correlations. We propose a novel Bayesian matrix normal graphical model that jointly models the temporal covariance and the brain network under a separable structure for the covariance to obtain improved estimates. The approach is implemented via an efficient optimization algorithm that computes the maximum-a-posteriori network estimates having desirable theoretical properties and which is scalable to high dimensions. The proposed method leads to substantial gains in network estimation accuracy compared to standard brain network modeling approaches as illustrated via extensive simulations. We apply the method to resting state fMRI data from the Human Connectome Project involving a large number of time scans and brain regions, to study the relationships between fluid intelligence and functional connectivity, where it is not computationally feasible to apply existing matrix normal graphical models. Our proposed approach led to the detection of differences in connectivity between high and low fluid intelligence groups, whereas these differences were less pronounced or absent using the graphical lasso.
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Affiliation(s)
- Suprateek Kundu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia
| | - Benjamin B Risk
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia
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Bhandari R, Kirilina E, Caan M, Suttrup J, De Sanctis T, De Angelis L, Keysers C, Gazzola V. Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T. Neuroimage 2020; 213:116731. [PMID: 32173409 PMCID: PMC7181191 DOI: 10.1016/j.neuroimage.2020.116731] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/27/2020] [Accepted: 03/09/2020] [Indexed: 02/06/2023] Open
Abstract
Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.
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Affiliation(s)
- Ritu Bhandari
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands.
| | - Evgeniya Kirilina
- Center for Cognitive Neuroscience, Free University, Berlin, Germany; Max Plank Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Matthan Caan
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Biomedical Engineering & Physics, Amsterdam, the Netherlands
| | - Judith Suttrup
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Teresa De Sanctis
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Lorenzo De Angelis
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands
| | - Christian Keysers
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands
| | - Valeria Gazzola
- Netherlands Institute for Neuroscience, KNAW, Amsterdam, the Netherlands; Department of Psychology, University of Amsterdam, the Netherlands.
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28
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Luo Q, Misaki M, Mulyana B, Wong CK, Bodurka J. Improved autoregressive model for correction of noise serial correlation in fast fMRI. Magn Reson Med 2020; 84:1293-1305. [PMID: 32060948 PMCID: PMC7263980 DOI: 10.1002/mrm.28203] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/31/2019] [Accepted: 01/17/2020] [Indexed: 11/07/2022]
Abstract
PURPOSE In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. METHODS Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). RESULTS The measured false positive characteristics agreed well with the theoretical curve when using the ARAICc , and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method. CONCLUSION The ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.
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Affiliation(s)
- Qingfei Luo
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Ben Mulyana
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Chung-Ki Wong
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,Stephenson School for Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
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29
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Kampa M, Schick A, Sebastian A, Wessa M, Tüscher O, Kalisch R, Yuen K. Replication of fMRI group activations in the neuroimaging battery for the Mainz Resilience Project (MARP). Neuroimage 2020; 204:116223. [DOI: 10.1016/j.neuroimage.2019.116223] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 09/16/2019] [Accepted: 09/23/2019] [Indexed: 01/25/2023] Open
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30
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Olszowy W, Aston J, Rua C, Williams GB. Accurate autocorrelation modeling substantially improves fMRI reliability. Nat Commun 2019; 10:1220. [PMID: 30899012 PMCID: PMC6428826 DOI: 10.1038/s41467-019-09230-w] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 02/25/2019] [Indexed: 11/23/2022] Open
Abstract
Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorrelation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. SPM’s alternative pre-whitening method, FAST, performed better than SPM’s default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems. There has been recent controversy over the validity of commonly-used software packages for functional MRI (fMRI) data analysis. Here, the authors compare the performance of three leading packages (AFNI, FSL, SPM) in terms of temporal autocorrelation modeling, a key statistical step in fMRI analysis.
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Affiliation(s)
- Wiktor Olszowy
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK. .,Laboratory of Research in Neuroimaging (LREN), Department of Clinical Neurosciences, CHUV, University of Lausanne, 1011, Lausanne, Switzerland.
| | - John Aston
- Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, CB3 0WB, UK
| | - Catarina Rua
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Guy B Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
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31
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Gui S, Gui R. Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions. Int J Neurosci 2019; 130:583-594. [PMID: 31778088 DOI: 10.1080/00207454.2019.1698568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Purpose: We propose a convolutional neural network (CNN) based on wavelet for verifying the activation regions decided with statistical analysis. Because the functional magnetic resonance imaging (fMRI) data contains lots of noises, it is difficult to get the data of blood-oxygen-level dependent (BOLD) signal directly for intervention testing like animal studies. So it is difficult to effectively verify these activation regions. Based on the rapid development of deep learning technology. Materials and methods: We select the task fMRI data of presenting food and nonfood pictures to volunteer subjects from open public data, whose website is https://www.openfmri.org/dataset/ds000157/. Firstly, the brain activation regions are obtained by utilizing the method of statistical analysis. Then the spatial coordinates are acquired from the activation regions by checking the atlas table. The P-value of the activation regions are less 0.05. The activation regions are the most responsive to perceive the differences of BOLD in the brain between the two states, presenting food and nonfood pictures. We select the part task fMRI data of from the activation regions, for preparing the training and validation samples. Then we design a deep leaning network based on wavelet to classify the task fMRI data between food and nonfood.Results and conclusions: The classification accuracy is 80.23%. However, when we select the spatial coordinates of other inactivation regions, the classification accuracy is only 60%. The differences of classification accuracy between the activation regions and the inactivation regions prove that the activation regions selected with statistical analysis method are accurate and effective. The two methods of deep learning and statistical analysis can be cross-validated for the study of human being brain.
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Affiliation(s)
- Shanquan Gui
- Department of Physics, Cuiying Honors College, Lanzhou University, Lanzhou, China
| | - Renzhou Gui
- Department of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Shanghai, China
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32
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Comparison of SMS-EPI and 3D-EPI at 7T in an fMRI localizer study with matched spatiotemporal resolution and homogenized excitation profiles. PLoS One 2019; 14:e0225286. [PMID: 31751410 PMCID: PMC6872176 DOI: 10.1371/journal.pone.0225286] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/31/2019] [Indexed: 02/06/2023] Open
Abstract
The simultaneous multi-slice EPI (SMS-EPI, a.k.a. MB-EPI) sequence has met immense popularity recently in functional neuroimaging. A still less common alternative is the use of 3D-EPI, which offers similar acceleration capabilities. The aim of this work was to compare the SMS-EPI and the 3D-EPI sequences in terms of sampling strategies for the detection of task-evoked activations at 7T using detection theory. To this end, the spatial and temporal resolutions of the sequences were matched (1.6 mm isotropic resolution, TR = 1200 ms) and their excitation profiles were homogenized by means of calibration-free parallel-transmission (Universal Pulses). We used a fast-event “localizer” paradigm of 5:20 min in order to probe sensorimotor functions (visual, auditory and motor tasks) as well as higher level functions (language comprehension, mental calculation), where results from a previous large-scale study at 3T (N = 81) served as ground-truth reference for the brain areas implicated in each cognitive function. In the current study, ten subjects were scanned while their activation maps were generated for each cognitive function with the GLM analysis. The SMS-EPI and 3D-EPI sequences were compared in terms of raw tSNR, t-score testing for the mean signal, activation strength and accuracy of the robust sensorimotor functions. To this end, the sensitivity and specificity of these contrasts were computed by comparing their activation maps to the reference brain areas obtained in the 3T study. Estimated flip angle distributions in the brain reported a normalized root mean square deviation from the target value below 10% for both sequences. The analysis of the t-score testing for the mean signal revealed temporal noise correlations, suggesting the use of this metric instead of the traditional tSNR for testing fMRI sequences. The SMS-EPI and 3D-EPI thereby yielded similar performance from a detection theory perspective.
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33
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Vakamudi K, Posse S, Jung R, Cushnyr B, Chohan MO. Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping. Hum Brain Mapp 2019; 41:797-814. [PMID: 31692177 PMCID: PMC7268088 DOI: 10.1002/hbm.24840] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/11/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.
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Affiliation(s)
- Kishore Vakamudi
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Stefan Posse
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico.,Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico
| | - Rex Jung
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
| | - Brad Cushnyr
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico
| | - Muhammad O Chohan
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
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Agrawal U, Brown EN, Lewis LD. Model-based physiological noise removal in fast fMRI. Neuroimage 2019; 205:116231. [PMID: 31589991 DOI: 10.1016/j.neuroimage.2019.116231] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 09/23/2019] [Accepted: 09/26/2019] [Indexed: 11/26/2022] Open
Abstract
Recent improvements in the speed and sensitivity of fMRI acquisition techniques suggest that fast fMRI can be used to detect and precisely localize sub-second neural dynamics. This enhanced temporal resolution has enormous potential for neuroscientists. However, physiological noise poses a major challenge for the analysis of fast fMRI data. Physiological noise scales with sensitivity, and its autocorrelation structure is altered in rapidly sampled data, suggesting that new approaches are needed for physiological noise removal in fast fMRI. Existing strategies either rely on external physiological recordings, which can be noisy or difficult to collect, or employ data-driven approaches which make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements. We then created a joint model of neural hemodynamics, and physiological and autocorrelated noise to more accurately remove noise. We first verified that HRAN accurately estimates cardiac and respiratory dynamics and that our model demonstrates goodness-of-fit in fast fMRI data. In task-driven data, we then demonstrated that HRAN is able to remove physiological noise while leaving the neural signal intact, thereby increasing detection of task-driven voxels. Finally, we established that in both simulations and fast fMRI data HRAN is able to improve statistical inferences as compared with gold-standard physiological noise removal techniques. In conclusion, we created a tool that harnesses the novel information in fast fMRI to remove physiological noise, enabling broader use of the technology to study human brain function.
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Affiliation(s)
- Uday Agrawal
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Emery N Brown
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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35
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Afyouni S, Smith SM, Nichols TE. Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation. Neuroimage 2019; 199:609-625. [PMID: 31158478 PMCID: PMC6693558 DOI: 10.1016/j.neuroimage.2019.05.011] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 05/02/2019] [Accepted: 05/06/2019] [Indexed: 12/13/2022] Open
Abstract
The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.
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Affiliation(s)
- Soroosh Afyouni
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK.
| | - Stephen M Smith
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK; The Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK.
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK; The Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK; Department of Statistics, University of Warwick, UK.
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36
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Chiew M, Miller KL. Improved statistical efficiency of simultaneous multi-slice fMRI by reconstruction with spatially adaptive temporal smoothing. Neuroimage 2019; 203:116165. [PMID: 31494247 PMCID: PMC6854456 DOI: 10.1016/j.neuroimage.2019.116165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 08/29/2019] [Accepted: 09/04/2019] [Indexed: 11/27/2022] Open
Abstract
We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves statistical efficiency. The method incorporates regularization to adjust temporal smoothness in a spatially varying, encoding-dependent manner, reducing the g-factor noise amplification per temporal degree of freedom. This results in a net improvement in tSNR and GLM efficiency, where the efficiency gain can be derived analytically as a function of the encoding and reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Analytical predictions, simulated and experimental results demonstrate a marked improvement in statistical efficiency in the temporally regularized reconstructions compared to conventional slice-GRAPPA reconstructions, particularly in central brain regions. Furthermore, experimental results confirm that residual slice leakage and aliasing errors are not noticeably increased compared to slice-GRAPPA reconstruction. This approach to temporally regularized image reconstruction in SMS-fMRI improves statistical power, and allows for explicit choice of reconstruction parameters by directly assessing their impact on noise variance per degree of freedom.
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Affiliation(s)
- Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
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Language beyond the language system: Dorsal visuospatial pathways support processing of demonstratives and spatial language during naturalistic fast fMRI. Neuroimage 2019; 216:116128. [PMID: 31473349 DOI: 10.1016/j.neuroimage.2019.116128] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/08/2019] [Accepted: 08/23/2019] [Indexed: 11/21/2022] Open
Abstract
Spatial demonstratives are powerful linguistic tools used to establish joint attention. Identifying the meaning of semantically underspecified expressions like "this one" hinges on the integration of linguistic and visual cues, attentional orienting and pragmatic inference. This synergy between language and extralinguistic cognition is pivotal to language comprehension in general, but especially prominent in demonstratives. In this study, we aimed to elucidate which neural architectures enable this intertwining between language and extralinguistic cognition using a naturalistic fMRI paradigm. In our experiment, 28 participants listened to a specially crafted dialogical narrative with a controlled number of spatial demonstratives. A fast multiband-EPI acquisition sequence (TR = 388 m s) combined with finite impulse response (FIR) modelling of the hemodynamic response was used to capture signal changes at word-level resolution. We found that spatial demonstratives bilaterally engage a network of parietal areas, including the supramarginal gyrus, the angular gyrus, and precuneus, implicated in information integration and visuospatial processing. Moreover, demonstratives recruit frontal regions, including the right FEF, implicated in attentional orienting and reference frames shifts. Finally, using multivariate similarity analyses, we provide evidence for a general involvement of the dorsal ("where") stream in the processing of spatial expressions, as opposed to ventral pathways encoding object semantics. Overall, our results suggest that language processing relies on a distributed architecture, recruiting neural resources for perception, attention, and extra-linguistic aspects of cognition in a dynamic and context-dependent fashion.
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38
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James O, Park H, Kim S. Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis. Hum Brain Mapp 2019; 40:3321-3337. [PMID: 31004386 PMCID: PMC6618018 DOI: 10.1002/hbm.24600] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 03/12/2019] [Accepted: 04/04/2019] [Indexed: 01/18/2023] Open
Abstract
A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross-covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.
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Affiliation(s)
- Oliver James
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonSouth Korea
- Department of Biomedical EngineeringSungkyunkwan UniversitySuwonSouth Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonSouth Korea
- School of Electronic and Electrical EngineeringSungkyunkwan UniversitySuwonSouth Korea
| | - Seong‐Gi Kim
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonSouth Korea
- Department of Biomedical EngineeringSungkyunkwan UniversitySuwonSouth Korea
- Samsung Advanced Institute for Health Sciences and TechnologySungkyunkwan UniversitySuwonSouth Korea
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39
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The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution. Neuroimage 2019; 194:228-243. [PMID: 30910728 DOI: 10.1016/j.neuroimage.2019.03.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/14/2019] [Accepted: 03/19/2019] [Indexed: 11/20/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) enables the update of various brain-activity measures during an ongoing experiment as soon as a new brain volume is acquired. However, the recorded Blood-oxygen-level dependent (BOLD) signal also contains physiological artifacts such as breathing and heartbeat, which potentially cause misleading false positive effects especially problematic in brain-computer interface (BCI) and neurofeedback (NF) setups. The low temporal resolution of echo planar imaging (EPI) sequences (which is in the range of seconds) prevents a proper separation of these artifacts from the BOLD signal. MR-Encephalography (MREG) has been shown to provide the high temporal resolution required to unalias and correct for physiological fluctuations and leads to increased specificity and sensitivity for mapping task-based activation and functional connectivity as well as for detecting dynamic changes in connectivity over time. By comparing a simultaneous multislice echo planar imaging (SMS-EPI) sequence and an MREG sequence using the same nominal spatial resolution in an offline analysis for three different experimental fMRI paradigms (perception of house and face stimuli, motor imagery, Stroop task), the potential of this novel technique for future BCI and NF applications was investigated. First, adapted general linear model pre-whitening which accounts for the high temporal resolution in MREG was implemented to calculate proper statistical results and be able to compare these with the SMS-EPI sequence. Furthermore, the respiration- and cardiac pulsation-related signals were successfully separated from the MREG signal using independent component analysis which were then included as regressors for a GLM analysis. Only the MREG sequence allowed to clearly separate cardiac pulsation and respiration components from the signal time course. It could be shown that these components highly correlate with the recorded respiration and cardiac pulsation signals using a respiratory belt and fingertip pulse plethysmograph. Temporal signal-to-noise ratios of SMS-EPI and MREG were comparable. Functional connectivity analysis using partial correlation showed a reduced standard error in MREG compared to SMS-EPI. Also, direct time course comparisons by down-sampling the MREG signal to the SMS-EPI temporal resolution showed lower variance in MREG. In general, we show that the higher temporal resolution is beneficial for fMRI time course modeling and this aspect can be exploited in offline application but also, is especially attractive, for real-time BCI and NF applications.
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40
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Eklund A, Knutsson H, Nichols TE. Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates. Hum Brain Mapp 2019; 40:2017-2032. [PMID: 30318709 PMCID: PMC6445744 DOI: 10.1002/hbm.24350] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/30/2018] [Accepted: 08/01/2018] [Indexed: 01/16/2023] Open
Abstract
Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.
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Affiliation(s)
- Anders Eklund
- Division of Medical Informatics, Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
- Division of Statistics & Machine Learning, Department of Computer and Information ScienceLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
| | - Hans Knutsson
- Division of Medical Informatics, Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
| | - Thomas E. Nichols
- Big Data InstituteUniversity of OxfordOxfordUnited Kingdom
- Wellcome Trust Centre for Integrative Neuroimaging (WIN‐FMRIB)University of OxfordOxfordUnited Kingdom
- Department of StatisticsUniversity of WarwickCoventryUnited Kingdom
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41
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Lee HL, Li Z, Coulson EJ, Chuang KH. Ultrafast fMRI of the rodent brain using simultaneous multi-slice EPI. Neuroimage 2019; 195:48-58. [PMID: 30910726 DOI: 10.1016/j.neuroimage.2019.03.045] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 03/05/2019] [Accepted: 03/19/2019] [Indexed: 12/25/2022] Open
Abstract
Increasing spatial and temporal resolutions of functional MRI (fMRI) measurement has been shown to benefit the study of neural dynamics and functional interaction. However, acceleration of rodent brain fMRI using parallel and simultaneous multi-slice imaging techniques is hampered by the lack of high-density phased-array coils for the small brain. To overcome this limitation, we adapted phase-offset multiplanar and blipped-controlled aliasing echo planar imaging (EPI) to enable simultaneous multi-slice fMRI of the mouse brain using a single loop coil on a 9.4T scanner. Four slice bands of 0.3 × 0.3 × 0.5 mm3 resolution can be simultaneously acquired to cover the whole brain at a temporal resolution of 300 ms or the whole cerebrum in 150 ms. Instead of losing signal-to-noise ratio (SNR), both spatial and temporal SNR can be increased due to the increased k-space sampling compared to a standard single-band EPI. Task fMRI using a visual stimulation shows close to 80% increase of z-score and 4 times increase of activated area in the visual cortex using the multiband EPI due to the highly increased temporal samples. Resting-state fMRI shows reliable detection of bilateral connectivity by both single-band and multiband EPI, but no significant difference was found. Without the need of a dedicated hardware, we have demonstrated a practical method that can enable unparallelly fast whole-brain fMRI for preclinical studies. This technique can be used to increase sensitivity, distinguish transient response or acquire high spatiotemporal resolution fMRI.
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Affiliation(s)
- Hsu-Lei Lee
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre of Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Zengmin Li
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Elizabeth J Coulson
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
| | - Kai-Hsiang Chuang
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Centre of Advanced Imaging, The University of Queensland, Brisbane, Australia.
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42
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Ramot M, Gonzalez-Castillo J. A framework for offline evaluation and optimization of real-time algorithms for use in neurofeedback, demonstrated on an instantaneous proxy for correlations. Neuroimage 2019; 188:322-334. [PMID: 30553044 PMCID: PMC11103676 DOI: 10.1016/j.neuroimage.2018.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 11/14/2018] [Accepted: 12/03/2018] [Indexed: 01/09/2023] Open
Abstract
Interest in real-time fMRI neurofeedback has grown exponentially over the past few years, both for use as a basic science research tool, and as part of the search for novel clinical interventions for neurological and psychiatric illnesses. In order to expand the range of questions which can be addressed with this tool however, new neurofeedback methods must be developed, going beyond feedback of activations in a single region. These new methods, several of which have already been proposed, are by their nature complex, involving many possible parameters. Here we suggest a framework for evaluating and optimizing algorithms for use in a real-time setting, before beginning the neurofeedback experiment, by offline simulations of algorithm output using a previously collected dataset. We demonstrate the application of this framework on the instantaneous proxy for correlations which we developed for training connectivity between different network nodes, identify the optimal parameters for use with this algorithm, and compare it to more traditional correlation methods. We also examine the effects of advanced imaging techniques, such as multi-echo acquisition, and the integration of these into the real-time processing stream.
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Affiliation(s)
- Michal Ramot
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
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43
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Chen JE, Polimeni JR, Bollmann S, Glover GH. On the analysis of rapidly sampled fMRI data. Neuroimage 2019; 188:807-820. [PMID: 30735828 PMCID: PMC6984348 DOI: 10.1016/j.neuroimage.2019.02.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 01/11/2019] [Accepted: 02/04/2019] [Indexed: 02/08/2023] Open
Abstract
Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Stanford University, Stanford, CA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Gary H Glover
- Department of Radiology, Stanford University, Stanford, CA, USA
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44
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Hearne LJ, Dean RJ, Robinson GA, Richards LJ, Mattingley JB, Cocchi L. Increased cognitive complexity reveals abnormal brain network activity in individuals with corpus callosum dysgenesis. NEUROIMAGE-CLINICAL 2018; 21:101595. [PMID: 30473430 PMCID: PMC6411589 DOI: 10.1016/j.nicl.2018.11.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 10/20/2018] [Accepted: 11/12/2018] [Indexed: 11/29/2022]
Abstract
Cognitive reasoning is thought to require functional interactions between whole-brain networks. Such networks rely on both cerebral hemispheres, with the corpus callosum providing cross-hemispheric communication. Here we used high-field functional magnetic resonance imaging (7 T fMRI), a well validated cognitive task, and brain network analyses to investigate the functional networks underlying cognitive reasoning in individuals with corpus callosum dysgenesis (CCD), an anatomical abnormality that affects the corpus callosum. Participants with CCD were asked to solve cognitive reasoning problems while their brain activity was measured using fMRI. The complexity of these problems was parametrically varied by changing the complexity of relations that needed to be established between shapes within each problem matrix. Behaviorally, participants showed a typical reduction in task performance as problem complexity increased. Task-evoked neural activity was observed in brain regions known to constitute two key cognitive control systems: the fronto-parietal and cingulo-opercular networks. Under low complexity demands, network topology and the patterns of local neural activity in the CCD group closely resembled those observed in neurotypical controls. By contrast, when asked to solve more complex problems, participants with CCD showed a reduction in neural activity and connectivity within the fronto-parietal network. These complexity-induced, as opposed to resting-state, differences in functional network activity help resolve the apparent paradox between preserved network architecture found at rest in CCD individuals, and the heterogeneous deficits they display in response to cognitive task demands [preprint: https://doi.org/10.1101/312629]. Individuals with corpus callosum dysgenesis fail to develop a normal corpus callosum. Resting-state functional brain networks in callosal dysgenesis appear relatively normal. Cognitive complexity revealed a deficit in fronto-parietal network activity. Differences in brain activity might only be revealed when under cognitive load.
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Affiliation(s)
- Luke J Hearne
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Ryan J Dean
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Gail A Robinson
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; School of Psychology, The University of Queensland, Brisbane, Australia
| | - Linda J Richards
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
| | - Jason B Mattingley
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia; School of Psychology, The University of Queensland, Brisbane, Australia
| | - Luca Cocchi
- Clincal Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
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45
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Rettenmeier C, Maziero D, Qian Y, Stenger VA. A circular echo planar sequence for fast volumetric fMRI. Magn Reson Med 2018; 81:1685-1698. [PMID: 30273963 DOI: 10.1002/mrm.27522] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 08/03/2018] [Accepted: 08/15/2018] [Indexed: 11/07/2022]
Abstract
PURPOSE To demonstrate a circular EPI (CEPI) sequence as well as a generalized EPI reconstruction for fast fMRI with parallel imaging acceleration. METHODS The CEPI acquisition was constructed using variable readout lengths and maximum ramp sampling as well as blipped-CAIPI z-gradient encoding for simultaneous multislice (SMS) and 3D volumetric imaging. A signal equation model with constant and linear phase terms was used to iteratively reconstruct images with low ghosting. Simulation, phantom, and human imaging experiments including audio/visual fMRI were performed at 3T using a 52-channel coil. RESULTS Application of CEPI gradients with duration of 27 ms covering a 22-cm FOV at a 64 × 64 pixel resolution in SMS and 3D acquisitions resulted in images with comparable quality to those of standard Cartesian EPI. With parallel imaging techniques robust detection of BOLD fMRI activation with temporal sampling down to 275 ms was possible. The high temporal resolution enabled higher activation statistics at a penalty in increased noise and residual aliasing. The un-accelerated 3D acquisition showed large temporal instability compared with a standard 2D acquisition. CONCLUSION Nonuniform sampling and generalized image reconstructions can be applied to EPI acquisitions including those with blipped-CAIPI z gradients. The same gradients can be used for either SMS or 3D acquisitions providing identical coverage.
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Affiliation(s)
- Christoph Rettenmeier
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
| | - Danilo Maziero
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
| | - Yongxian Qian
- Department of Radiology, New York University School of Medicine, New York, New York
| | - V Andrew Stenger
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
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46
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Corbin N, Todd N, Friston KJ, Callaghan MF. Accurate modeling of temporal correlations in rapidly sampled fMRI time series. Hum Brain Mapp 2018; 39:3884-3897. [PMID: 29885101 PMCID: PMC6175228 DOI: 10.1002/hbm.24218] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 05/03/2018] [Accepted: 05/07/2018] [Indexed: 11/08/2022] Open
Abstract
Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false‐positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first‐order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the “FAST” model implemented in SPM is used with a well‐controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal‐to‐noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t‐score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t‐score measure is directly related to the t‐score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.
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Affiliation(s)
- Nadège Corbin
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Nick Todd
- Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
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