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Li Q, Xing Y, Zhu Z, Fei X, Tang Y, Lu J. Effects of computerized cognitive training on functional brain networks in patients with vascular cognitive impairment and no dementia. CNS Neurosci Ther 2024; 30:e14779. [PMID: 38828650 PMCID: PMC11145123 DOI: 10.1111/cns.14779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 04/21/2024] [Accepted: 05/08/2024] [Indexed: 06/05/2024] Open
Abstract
AIMS Previous neuroimaging studies of vascular cognitive impairment, no dementia (VCIND), have reported functional alterations, but far less is known about the effects of cognitive training on functional connectivity (FC) of intrinsic connectivity networks (ICNs) and how they relate to intervention-related cognitive improvement. This study provides comprehensive research on the changes in intra- and inter-brain functional networks in patients with VCIND who received computerized cognitive training, with a focus on the underlying mechanisms and potential therapeutic strategies. METHODS We prospectively collected 60 patients with VCIND who were randomly divided into the training group (N = 30) receiving computerized cognitive training and the control group (N = 30) receiving fixed cognitive training. Functional MRI scans and cognitive assessments were performed at baseline, at the 7-week training, and at the 6-month follow-up. Utilizing templates for ICNs, the study employed a linear mixed model to compare intra- and inter-network FC changes between the two groups. Pearson correlation was applied to calculate the relationship between FC and cognitive function. RESULTS We found significantly decreased intra-network FC within the default mode network (DMN) following computerized cognitive training at Month 6 (p = 0.034), suggesting a potential loss of functional specialization. Computerized training led to increased functional coupling between the DMN and sensorimotor network (SMN) (p = 0.01) and between the language network (LN) and executive control network (ECN) at Month 6 (p < 0.001), indicating compensatory network adaptations in patients with VCIND. Notably, the intra-LN exhibited enhanced functional specialization after computerized cognitive training (p = 0.049), with significant FC increases among LN regions, which correlated with improvements in neuropsychological measures (p < 0.05), emphasizing the targeted impact of computerized cognitive training on language abilities. CONCLUSIONS This study provides insights into neuroplasticity and adaptive changes resulting from cognitive training in patients with VCIND, with implications for potential therapeutic strategies.
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Affiliation(s)
- Qiong‐Ge Li
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Yi Xing
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Zu‐De Zhu
- Collaborative Innovation Center for Language AbilityJiangsu Normal UniversityXuzhouChina
| | - Xiao‐Lu Fei
- Department of Information, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Yi Tang
- Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
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Sanda P, Hlinka J, van den Berg M, Skoch A, Bazhenov M, Keliris GA, Krishnan GP. Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression. PLoS Comput Biol 2024; 20:e1012099. [PMID: 38843298 PMCID: PMC11185486 DOI: 10.1371/journal.pcbi.1012099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 06/18/2024] [Accepted: 04/23/2024] [Indexed: 06/19/2024] Open
Abstract
Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.
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Affiliation(s)
- Pavel Sanda
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Georgios A. Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | - Giri P. Krishnan
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Lokossou HA, Rabuffo G, Bernard M, Bernard C, Viola A, Perles-Barbacaru TA. Impact of the day/night cycle on functional connectome in ageing male and female mice. Neuroimage 2024; 290:120576. [PMID: 38490583 DOI: 10.1016/j.neuroimage.2024.120576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024] Open
Abstract
To elucidate how time of day, sex, and age affect functional connectivity (FC) in mice, we aimed to examine whether the mouse functional connectome varied with the day/night cycle and whether it depended on sex and age. We explored C57Bl6/J mice (6♀ and 6♂) at mature age (5 ± 1 months) and middle-age (14 ± 1 months). Each mouse underwent Blood Oxygen-Level-Dependent (BOLD) resting-state functional MRI (rs-fMRI) on a 7T scanner at four different times of the day, two under the light condition and two under the dark condition. Data processing consisted of group independent component analysis (ICA) and region-level analysis using resting-state networks (RSNs) derived from literature. Linear mixed-effect models (LMEM) were used to assess the effects of sex, lighting condition and their interactions for each RSN obtained with group-ICA (RSNs-GICA) and six bilateral RSNs adapted from literature (RSNs-LIT). Our study highlighted new RSNs in mice related to day/night alternation in addition to other networks already reported in the literature. In mature mice, we found sex-related differences in brain activation only in one RSNs-GICA comprising the cortical, hippocampal, midbrain and cerebellar regions of the right hemisphere. In males, brain activity was significantly higher in the left hippocampus, the retrosplenial cortex, the superior colliculus, and the cerebellum regardless of lighting condition; consistent with the role of these structures in memory formation and integration, sleep, and sex-differences in memory processing. Experimental constraints limited the analysis to the impact of light/dark cycle on the RSNs for middle-aged females. We detected significant activation in the pineal gland during the dark condition, a finding in line with the nocturnal activity of this gland. For the analysis of RSNs-LIT, new variables "sexage" (sex and age combined) and "edges" (pairs of RSNs) were introduced. FC was calculated as the Pearson correlation between two RSNs. LMEM revealed no effect of sexage or lighting condition. The FC depended on the edges, but there were no interaction effects between sexage, lighting condition and edges. Interaction effects were detected between i) sex and lighting condition, with higher FC in males under the dark condition, ii) sexage and edges with higher FC in male brain regions related to vision, memory, and motor action. We conclude that time of day and sex should be taken into account when designing, analyzing, and interpreting functional imaging studies in rodents.
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Affiliation(s)
- Houéfa Armelle Lokossou
- Centre for Magnetic Resonance in Biology and Medicine, CRMBM UMR 7339, Aix-Marseille University-CNRS, Marseille, France; Institute of Systems Neuroscience, INS UMR 1106, Aix-Marseille University-INSERM, Marseille, France.
| | - Giovanni Rabuffo
- Institute of Systems Neuroscience, INS UMR 1106, Aix-Marseille University-INSERM, Marseille, France
| | - Monique Bernard
- Centre for Magnetic Resonance in Biology and Medicine, CRMBM UMR 7339, Aix-Marseille University-CNRS, Marseille, France
| | - Christophe Bernard
- Institute of Systems Neuroscience, INS UMR 1106, Aix-Marseille University-INSERM, Marseille, France.
| | - Angèle Viola
- Centre for Magnetic Resonance in Biology and Medicine, CRMBM UMR 7339, Aix-Marseille University-CNRS, Marseille, France
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Bein O, Davachi L. Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning. J Neurosci 2024; 44:e0627232023. [PMID: 38129134 PMCID: PMC10919070 DOI: 10.1523/jneurosci.0627-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.
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Affiliation(s)
- Oded Bein
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, New York 10027
- Center for Clinical Research, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
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Golestani AM, Chen JJ. Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging. Front Neurosci 2024; 18:1223230. [PMID: 38379761 PMCID: PMC10876882 DOI: 10.3389/fnins.2024.1223230] [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: 05/15/2023] [Accepted: 01/17/2024] [Indexed: 02/22/2024] Open
Abstract
Introduction Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. Methods In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI). Results Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group. Discussion While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.
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Affiliation(s)
- Ali M. Golestani
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - J. Jean Chen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Freedberg MV. The balance of hippocampal and caudate network functional connectivity is associated with episodic memory performance and its decline across adulthood. Neuropsychologia 2023; 191:108723. [PMID: 37923122 DOI: 10.1016/j.neuropsychologia.2023.108723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
The hippocampal and caudate networks interact to support episodic memory, but the relationship between hippocampal and caudate connectivity strength and episodic memory is unclear. In general, cognition is optimally supported when connectivity within a functional network dominates connectivity from other networks. For example, episodic memory may be optimally supported when the hippocampal and caudate networks express this pattern of connectivity, consistent with research showing that the two networks are organized competitively. Alternatively, episodic memory may be optimally supported when connectivity in both networks is more balanced, consistent with fMRI reports showing cooperation between networks. Using cross-sectional behavioral and resting state fMRI data from a diverse sample (N = 347; Ages 18-85), I tested the hypothesis that reduced hippocampal and caudate network dominance would be associated with reduced episodic memory across individuals and age. Consistent with this hypothesis, lower caudate network dominance in bilateral thalamic regions was associated with worse episodic memory regardless of age. Age-related differences in caudate network dominance in the pallidum and putamen were also associated with worse episodic memory performance, but through their shared variance with age. I found no evidence that network dominance was related to processing speed or executive function, or that hippocampal network dominance was relate to episodic memory performance. These results show that ongoing biological dynamics between the hippocampal and caudate networks throughout adulthood are related to episodic memory performance and support a growing literature specifying the role of the caudate network in episodic memory.
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Affiliation(s)
- Michael V Freedberg
- The University of Texas, Department of Kinesiology and Health Education, Austin, TX, 78712, USA; The University of Texas, Institute for Neuroscience, Austin, TX, 78712, USA.
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Silchenko AN, Hoffstaedter F, Eickhoff SB. Impact of sample size and regression of tissue-specific signals on effective connectivity within the core default mode network. Hum Brain Mapp 2023; 44:5858-5870. [PMID: 37713540 PMCID: PMC10619387 DOI: 10.1002/hbm.26481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023] Open
Abstract
Interactions within brain networks are inherently directional, which are inaccessible to classical functional connectivity estimates from resting-state functional magnetic resonance imaging (fMRI) but can be detected using spectral dynamic causal modeling (DCM). The sample size and unavoidable presence of nuisance signals during fMRI measurement are the two important factors influencing the stability of group estimates of connectivity parameters. However, most recent studies exploring effective connectivity (EC) have been conducted with small sample sizes and minimally pre-processed datasets. We explore the impact of these two factors by analyzing clean resting-state fMRI data from 330 unrelated subjects from the Human Connectome Project database. We demonstrate that both the stability of the model selection procedures and the inference of connectivity parameters are highly dependent on the sample size. The minimum sample size required for stable DCM is approximately 50, which may explain the variability of the DCM results reported so far. We reveal a stable pattern of EC within the core default mode network computed for large sample sizes and demonstrate that the use of subject-specific thresholded whole-brain masks for tissue-specific signals regression enhances the detection of weak connections.
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Affiliation(s)
- Alexander N. Silchenko
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
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Bartoň M, Rapcsak SZ, Zvončák V, Mareček R, Cvrček V, Rektorová I. Functional neuroanatomy of reading in Czech: Evidence of a dual-route processing architecture in a shallow orthography. Front Psychol 2023; 13:1037365. [PMID: 36726504 PMCID: PMC9885179 DOI: 10.3389/fpsyg.2022.1037365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/21/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction According to the strong version of the orthographic depth hypothesis, in languages with transparent letter-sound mappings (shallow orthographies) the reading of both familiar words and unfamiliar nonwords may be accomplished by a sublexical pathway that relies on serial grapheme-to-phoneme conversion. However, in languages such as English characterized by inconsistent letter-sound relationships (deep orthographies), word reading is mediated by a lexical-semantic pathway that relies on mappings between word-specific orthographic, semantic, and phonological representations, whereas the sublexical pathway is used primarily to read nonwords. Methods In this study, we used functional magnetic resonance imaging to elucidate neural substrates of reading in Czech, a language characterized by a shallo worthography. Specifically, we contrasted patterns of brain activation and connectivity during word and nonword reading to determine whether similar or different neural mechanisms are involved. Neural correlates were measured as differences in simple whole-brain voxel-wise activation, and differences in visual word form area (VWFA) task-related connectivity were computed on the group level from data of 24 young subject. Trial-to-trial reading reaction times were used as a measure of task difficulty, and these effects were subtracted from the activation and connectivity effects in order to eliminate difference in cognitive effort which is naturally higher for nonwords and may mask the true lexicality effects. Results We observed pattern of activity well described in the literature mostly derived from data of English speakers - nonword reading (as compared to word reading) activated the sublexical pathway to a greater extent whereas word reading was associated with greater activation of semantic networks. VWFA connectivity analysis also revealed stronger connectivity to a component of the sublexical pathway - left inferior frontal gyrus (IFG), for nonword compared to word reading. Discussion These converging results suggest that the brain mechanism of skilled reading in shallow orthography languages are similar to those engaged when reading in languages with a deep orthography and are supported by a universal dual-pathway neural architecture.
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Affiliation(s)
- Marek Bartoň
- Applied Neuroscience Research Group, Central European Institute of Technology – CEITEC, Masaryk University, Brno, Czechia
| | - Steven Z. Rapcsak
- Department of Neurology, University of Arizona, Tucson, AZ, United States
| | - Vojtěch Zvončák
- Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Radek Mareček
- Applied Neuroscience Research Group, Central European Institute of Technology – CEITEC, Masaryk University, Brno, Czechia
| | - Václav Cvrček
- Institute of the Czech National Corpus, Charles University, Prague, Czechia
| | - Irena Rektorová
- Applied Neuroscience Research Group, Central European Institute of Technology – CEITEC, Masaryk University, Brno, Czechia,International Clinical Research Center, ICRC, St. Anne’s University Hospital and Faculty of Medicine, Masaryk University, Brno, Czechia,*Correspondence: Irena Rektorová, ✉ ; ✉
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Li Q, Hu S, Mo Y, Chen H, Meng C, Zhan L, Li M, Quan X, Gao Y, Cheng L, Hao Z, Jia X, Liang Z. Regional homogeneity alterations in multifrequency bands in patients with basal ganglia stroke: A resting-state functional magnetic resonance imaging study. Front Aging Neurosci 2022; 14:938646. [PMID: 36034147 PMCID: PMC9403766 DOI: 10.3389/fnagi.2022.938646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/22/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveThe aim of this study was to investigate the spontaneous regional neural activity abnormalities in patients with acute basal ganglia ischemic stroke (BGIS) using a multifrequency bands regional homogeneity (ReHo) method and to explore whether the alteration of ReHo values was associated with clinical characteristics.MethodsIn this study, 34 patients with acute BGIS and 44 healthy controls (HCs) were recruited. All participants were examined by resting-state functional magnetic resonance imaging (rs-fMRI). The ReHo method was used to detect the alterations of spontaneous neural activities in patients with acute BGIS. A two-sample t-test comparison was performed to compare the ReHo value between the two groups, and a Pearson correlation analysis was conducted to assess the relationship between the regional neural activity abnormalities and clinical characteristics.ResultsCompared with the HCs, the patients with acute BGIS showed increased ReHo in the left caudate and subregions such as the right caudate and left putamen in conventional frequency bands. In the slow-5 frequency band, patients with BGIS showed decreased ReHo in the left medial cingulum of BGIS compared to the HCs and other subregions such as bilateral caudate and left putamen. No brain regions with ReHo alterations were found in the slow-4 frequency band. Moreover, we found that the ReHo value of left caudate was positively correlated with the NIHSS score.ConclusionOur findings revealed the alterations of ReHo in patients with acute BGIS in a specific frequency band and provided a new insight into the pathogenesis mechanism of BGIS. This study demonstrated the frequency-specific characteristics of ReHo in patients with acute BGIS, which may have a positive effect on the future neuroimaging studies.
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Affiliation(s)
- Qianqian Li
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Su Hu
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Yingmin Mo
- The Cadre Ward in Department of Neurology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Hao Chen
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chaoguo Meng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Xuemei Quan
- Department of Neurology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Lulu Cheng
- School of Foreign Studies, China University of Petroleum (East China), Qingdao, China
- Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Zeqi Hao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Zhijian Liang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Aabedi AA, Young JS, Chang EF, Berger MS, Hervey-Jumper SL. Involvement of White Matter Language Tracts in Glioma: Clinical Implications, Operative Management, and Functional Recovery After Injury. Front Neurosci 2022; 16:932478. [PMID: 35898410 PMCID: PMC9309688 DOI: 10.3389/fnins.2022.932478] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
To achieve optimal survival and quality of life outcomes in patients with glioma, the extent of tumor resection must be maximized without causing injury to eloquent structures. Preservation of language function is of particular importance to patients and requires careful mapping to reveal the locations of cortical language hubs and their structural and functional connections. Within this language network, accurate mapping of eloquent white matter tracts is critical, given the high risk of permanent neurological impairment if they are injured during surgery. In this review, we start by describing the clinical implications of gliomas involving white matter language tracts. Next, we highlight the advantages and limitations of methods commonly used to identify these tracts during surgery including structural imaging techniques, functional imaging, non-invasive stimulation, and finally, awake craniotomy. We provide a rationale for combining these complementary techniques as part of a multimodal mapping paradigm to optimize postoperative language outcomes. Next, we review local and long-range adaptations that take place as the language network undergoes remodeling after tumor growth and surgical resection. We discuss the probable cellular mechanisms underlying this plasticity with emphasis on the white matter, which until recently was thought to have a limited role in adults. Finally, we provide an overview of emerging developments in targeting the glioma-neuronal network interface to achieve better disease control and promote recovery after injury.
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Tonic pain alters functional connectivity of the descending pain modulatory network involving amygdala, periaqueductal gray, parabrachial nucleus and anterior cingulate cortex. Neuroimage 2022; 256:119278. [PMID: 35523367 PMCID: PMC9250649 DOI: 10.1016/j.neuroimage.2022.119278] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 04/07/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
INTRODUCTION Resting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC accompanying tonic pain in pain-free persons are rare. A network we term the Descending Pain Modulatory Network (DPMN) is implicated in healthy and pathologic pain modulation. Here, we evaluate the effect of tonic pain on FC of specific nodes of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN). METHODS In 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. functional MRI measured resting BOLD signal during pain-free rest with a 32°C thermode and then tonic pain where participants experienced a previously warm temperature combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity during both states. We hypothesized tonic pain would diminish FC dyads within the DPMN. RESULTS Of all hypothesized FC dyads, only PAG and subgenual ACC was weakly altered during pain (F=3.34; p=0.074; pain-free>pain d=0.25). After pain induction sACC-PAG FC became positively correlated with pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During pain, this connection's FC was diminished (paired t=-3.17; p=0.0026). In whole-brain analyses, during pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During pain, FC between left AMYG and right inferior temporal gyrus negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC with right claustrum; right primary visual cortex and right temporo-occipitoparietal junction Conclusion: We demonstrate sACC-PAG tonic pain FC positively correlates with experienced pain and resting right PBN-PAG FC correlates with subsequent pain and is diminished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which correlate with subsequently experienced pain intensity. Our findings suggest specific connectivity patterns within the DPMN at rest are associated with subsequently experienced pain and modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation or biomarkers to guide interventions.
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12
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Martins D, Lockwood P, Cutler J, Moran R, Paloyelis Y. Oxytocin modulates neurocomputational mechanisms underlying prosocial reinforcement learning. Prog Neurobiol 2022; 213:102253. [DOI: 10.1016/j.pneurobio.2022.102253] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/19/2022] [Accepted: 02/28/2022] [Indexed: 12/18/2022]
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Kovářová A, Gajdoš M, Rektor I, Mikl M. Contribution of the multi-echo approach in accelerated functional magnetic resonance imaging multiband acquisition. Hum Brain Mapp 2021; 43:955-973. [PMID: 34716738 PMCID: PMC8764472 DOI: 10.1002/hbm.25698] [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: 03/17/2021] [Revised: 07/16/2021] [Accepted: 10/18/2021] [Indexed: 11/11/2022] Open
Abstract
We wanted to verify the effect of combining multi‐echo (ME) functional magnetic resonance imaging (fMRI) with slice acceleration in simultaneous multi‐slice acquisition. The aim was to shed light on the benefits of multiple echoes for various acquisition settings, especially for levels of slice acceleration and flip angle. Whole‐brain ME fMRI data were obtained from 26 healthy volunteers (using three echoes; seven runs with slice acceleration 1, 4, 6, and 8; and two different flip angles for each of the first three acceleration factors) and processed as single‐echo (SE) data and ME data based on optimal combinations weighted by the contrast‐to‐noise ratio. Global metrics (temporal signal‐to‐noise ratio, signal‐to‐noise separation, number of active voxels, etc.) and local characteristics in regions of interest were used to evaluate SE and ME data. ME results outperformed SE results in all runs; the differences became more apparent for higher acceleration, where a significant decrease in data quality is observed. ME fMRI can improve the observed data quality metrics over SE fMRI for a wide range of accelerated fMRI acquisitions.
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Affiliation(s)
- Anežka Kovářová
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,First Department of Neurology, Faculty of Medicine of the Masaryk University, Brno, Czech Republic
| | - Martin Gajdoš
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Ivan Rektor
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,First Department of Neurology, Faculty of Medicine of the Masaryk University, Brno, Czech Republic
| | - Michal Mikl
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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Labounek R, Wu Z, Bridwell DA, Brázdil M, Jan J, Nestrašil I. Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? Front Neurol 2021; 12:644874. [PMID: 33981283 PMCID: PMC8107237 DOI: 10.3389/fneur.2021.644874] [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: 12/22/2020] [Accepted: 03/22/2021] [Indexed: 02/01/2023] Open
Abstract
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low β 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, β 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
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Affiliation(s)
- René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
| | - Zhuolin Wu
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milan Brázdil
- Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Jiří Jan
- Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Igor Nestrašil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
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Li YT, Chang CY, Hsu YC, Fuh JL, Kuo WJ, Yeh JNT, Lin FH. Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer's disease. J Cereb Blood Flow Metab 2021; 41:166-181. [PMID: 32070180 PMCID: PMC7747160 DOI: 10.1177/0271678x19897442] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD.
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Affiliation(s)
- Yi-Tien Li
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.,Department of Medical Imaging, Taipei Medical University - Shuang-Ho Hospital, New Taipei, Taiwan
| | - Chun-Yuan Chang
- Department of Neurology, Min-Sheng General Hospital, Taoyuan, Taiwan
| | - Yi-Cheng Hsu
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,Faculty of Medicine, National Yang-Ming University Schools of Medicine, Taipei, Taiwan
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Jhy-Neng Tasso Yeh
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
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Kopal J, Pidnebesna A, Tomeček D, Tintěra J, Hlinka J. Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines. Hum Brain Mapp 2020; 41:5325-5340. [PMID: 32881215 PMCID: PMC7670643 DOI: 10.1002/hbm.25195] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/07/2020] [Accepted: 08/09/2020] [Indexed: 12/25/2022] Open
Abstract
Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.
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Affiliation(s)
- Jakub Kopal
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic.,Centre de Recherche Cerveau et Cognition, Universite Paul Sabatier, Toulouse, France
| | - Anna Pidnebesna
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - David Tomeček
- National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic
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The role of the striatum in visuomotor integration during handwriting: an fMRI study. J Neural Transm (Vienna) 2020; 127:331-337. [PMID: 31901984 DOI: 10.1007/s00702-019-02131-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/21/2019] [Indexed: 10/25/2022]
Abstract
This study investigates the role of the dorsal/sensorimotor striatum in visuomotor integration (i.e., the transformation of internal visual information about letter shapes into motor output) during handwriting. Twenty healthy participants underwent fMRI scanning with tasks consisting of self-paced handwriting of alphabetically ordered single letters and simple dots, with both tasks performed without visual feedback. Functional connectivity (FC) from these two tasks was compared to demonstrate the difference between coordinated activity arising during handwriting and the activity during a simpler motor condition. Our study focused upon the writing-specific cortico-striatal network of preselected regions of interest consisting of the visual word form area (VWFA), anterior intraparietal sulcus/superior parietal lobule, striatum, premotor cortex/Exner's area, and primary and supplementary motor regions. We observed systematically increased task-induced cortico-striatal and cortico-cortical FC. This increased synchronization of neural activity between the VWFA, i.e., the visual cortical area containing information about letter shapes, and the frontoparietal motor regions is mediated by the striatum. These findings suggest the involvement of the striatum in integrating stored letter-shape information with motor planning and execution during handwriting.
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18
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Grajauskas LA, Frizzell T, Song X, D'Arcy RCN. White Matter fMRI Activation Cannot Be Treated as a Nuisance Regressor: Overcoming a Historical Blind Spot. Front Neurosci 2019; 13:1024. [PMID: 31636527 PMCID: PMC6787144 DOI: 10.3389/fnins.2019.01024] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 09/09/2019] [Indexed: 12/19/2022] Open
Abstract
Despite past controversies, increasing evidence has led to acceptance that white matter activity is detectable using functional magnetic resonance imaging (fMRI). In spite of this, advanced analytic methods continue to be published that reinforce a historic bias against white matter activation by using it as a nuisance regressor. It is important that contemporary analyses overcome this blind spot in whole brain functional imaging, both to ensure that newly developed noise regression techniques are accurate, and to ensure that white matter, a vital and understudied part of the brain, is not ignored in functional neuroimaging studies.
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Affiliation(s)
- Lukas A Grajauskas
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,ImageTech Lab, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada
| | - Tory Frizzell
- ImageTech Lab, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada.,Faculty of Applied Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Xiaowei Song
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.,ImageTech Lab, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada
| | - Ryan C N D'Arcy
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.,ImageTech Lab, Surrey Memorial Hospital, Fraser Health, Surrey, BC, Canada.,Faculty of Applied Sciences, Simon Fraser University, Burnaby, BC, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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Bartoň M, Mareček R, Krajčovičová L, Slavíček T, Kašpárek T, Zemánková P, Říha P, Mikl M. Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation. Hum Brain Mapp 2018; 40:1114-1138. [PMID: 30403309 DOI: 10.1002/hbm.24433] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 08/17/2018] [Accepted: 10/08/2018] [Indexed: 12/16/2022] Open
Abstract
This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.
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Affiliation(s)
- Marek Bartoň
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,First Department of Neurology, Faculty of Medicine of the Masaryk University and St. Anne's University Hospital, Brno, Czech Republic
| | - Radek Mareček
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Lenka Krajčovičová
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Tomáš Slavíček
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Tomáš Kašpárek
- Department of Psychiatry, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petra Zemánková
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,First Department of Neurology, Faculty of Medicine of the Masaryk University and St. Anne's University Hospital, Brno, Czech Republic.,Department of Psychiatry, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Pavel Říha
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Michal Mikl
- CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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