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Ma L, Braun SE, Steinberg JL, Bjork JM, Martin CE, Keen Ii LD, Moeller FG. Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis. Neuroimage 2024; 292:120604. [PMID: 38604537 DOI: 10.1016/j.neuroimage.2024.120604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/31/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024] Open
Abstract
Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA.
| | | | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - Caitlin E Martin
- Institute for Drug and Alcohol Studies, USA; Department of Obstetrics and Gynecology, USA
| | - Larry D Keen Ii
- Department of Psychology, Virginia State University, Petersburg, VA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA; Department of Neurology, USA; Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA
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Bernal-Casas D, Lee HJ, Weitz AJ, Lee JH. Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI. Neuron 2017; 93:522-532.e5. [PMID: 28132829 DOI: 10.1016/j.neuron.2016.12.035] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 10/30/2016] [Accepted: 12/20/2016] [Indexed: 12/12/2022]
Abstract
Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell-type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific, brain-wide functional measurements-to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell-type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies.
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Affiliation(s)
- David Bernal-Casas
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Hyun Joo Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Andrew J Weitz
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Jin Hyung Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
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Phillips HN, Blenkmann A, Hughes LE, Kochen S, Bekinschtein TA, Cam-Can, Rowe JB. Convergent evidence for hierarchical prediction networks from human electrocorticography and magnetoencephalography. Cortex 2016; 82:192-205. [PMID: 27389803 DOI: 10.1016/j.cortex.2016.05.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 03/08/2016] [Accepted: 05/02/2016] [Indexed: 11/20/2022]
Abstract
We propose that sensory inputs are processed in terms of optimised predictions and prediction error signals within hierarchical neurocognitive models. The combination of non-invasive brain imaging and generative network models has provided support for hierarchical frontotemporal interactions in oddball tasks, including recent identification of a temporal expectancy signal acting on prefrontal cortex. However, these studies are limited by the need to invert magnetoencephalographic or electroencephalographic sensor signals to localise activity from cortical 'nodes' in the network, or to infer neural responses from indirect measures such as the fMRI BOLD signal. To overcome this limitation, we examined frontotemporal interactions estimated from direct cortical recordings from two human participants with cortical electrode grids (electrocorticography - ECoG). Their frontotemporal network dynamics were compared to those identified by magnetoencephalography (MEG) in forty healthy adults. All participants performed the same auditory oddball task with standard tones interspersed with five deviant tone types. We normalised post-operative electrode locations to standardised anatomic space, to compare across modalities, and inverted the MEG to cortical sources using the estimated lead field from subject-specific head models. A mismatch negativity signal in frontal and temporal cortex was identified in all subjects. Generative models of the electrocorticographic and magnetoencephalographic data were separately compared using the free-energy estimate of the model evidence. Model comparison confirmed the same critical features of hierarchical frontotemporal networks in each patient as in the group-wise MEG analysis. These features included bilateral, feedforward and feedback frontotemporal modulated connectivity, in addition to an asymmetric expectancy driving input on left frontal cortex. The invasive ECoG provides an important step in construct validation of the use of neural generative models of MEG, which in turn enables generalisation to larger populations. Together, they give convergent evidence for the hierarchical interactions in frontotemporal networks for expectation and processing of sensory inputs.
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Abstract
We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus’ onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad hoc concepts such as “executive control” is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty) and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching.
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Affiliation(s)
- Roberto Limongi
- UDP-INECO Foundation Core on Neuroscience, Diego Portales University , Santiago, Chile ; Instituto Venezolano de Investigaciones Lingüísticas y Literarias "Andres Bello" , Caracas, Venezuela
| | - Angélica M Silva
- Instituto Venezolano de Investigaciones Lingüísticas y Literarias "Andres Bello" , Caracas, Venezuela ; Escuela Lingüística de Valparaíso de la Pontificia Universidad Católica de Valparaíso , Chile
| | - Begoña Góngora-Costa
- Escuela de Fonoaudiología, Facultad de Medicina, Universidad de Valparaíso , Chile
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Affiliation(s)
- Petra Ritter
- Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Deparment of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany ; Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University Berlin, Germany
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine Marseille, France
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Centre, University of Toronto Toronto, ON, Canada
| | - Michael Breakspear
- Systems Neuroscience Group, QIMR Berghofer Brisbane, QLD, Australia ; The Royal Brisbane and Woman's Hospital Brisbane, QLD, Australia
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Ma L, Steinberg JL, Cunningham KA, Lane SD, Kramer LA, Narayana PA, Kosten TR, Bechara A, Moeller FG. Inhibitory behavioral control: a stochastic dynamic causal modeling study using network discovery analysis. Brain Connect 2015; 5:177-86. [PMID: 25336321 PMCID: PMC4394161 DOI: 10.1089/brain.2014.0275] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
This study employed functional magnetic resonance imaging (fMRI)-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity underlying inhibitory behavioral control. fMRI data were acquired from 15 healthy subjects while they performed a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard NoGo conditions) in distinguishing spatial patterns of lines. Based on the previous inhibitory control literature and the present fMRI activation results, 10 brain regions were postulated as nodes in the effective connectivity model. Due to the large number of potential interconnections among these nodes, the number of models for final analysis was reduced to a manageable level for the whole group by conducting DCM Network Discovery, which is a recently developed option within the Statistical Parametric Mapping software package. Given the optimum network model, the DCM Network Discovery analysis found that the locations of the driving input into the model from all the experimental stimuli in the Go/NoGo task were the amygdala and the hippocampus. The strengths of several cortico-subcortical connections were modulated (influenced) by the two NoGo conditions. Specifically, connectivity from the middle frontal gyrus (MFG) to hippocampus was enhanced by the Easy condition and further enhanced by the Hard NoGo condition, possibly suggesting that compared with the Easy NoGo condition, stronger control from MFG was needed for the hippocampus to discriminate/learn the spatial pattern in order to respond correctly (inhibit), during the Hard NoGo condition.
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Affiliation(s)
- Liangsuo Ma
- Department of Radiology, Virginia Commonwealth University, Richmond, Virginia
| | - Joel L. Steinberg
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Kathryn A. Cunningham
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, Texas
| | - Scott D. Lane
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center, Houston, Texas
| | - Larry A. Kramer
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center, Houston, Texas
| | - Ponnada A. Narayana
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center, Houston, Texas
| | - Thomas R. Kosten
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas
| | - Antoine Bechara
- Department of Psychology, Institute for the Neurological Study of Emotion and Creativity, University of Southern California, Los Angeles, California
| | - F. Gerard Moeller
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
- Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, Virginia
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Ma L, Steinberg JL, Cunningham KA, Lane SD, Bjork JM, Neelakantan H, Price AE, Narayana PA, Kosten TR, Bechara A, Moeller FG. Inhibitory behavioral control: A stochastic dynamic causal modeling study comparing cocaine dependent subjects and controls. Neuroimage Clin 2015; 7:837-47. [PMID: 26082893 PMCID: PMC4459041 DOI: 10.1016/j.nicl.2015.03.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/02/2015] [Accepted: 03/19/2015] [Indexed: 01/08/2023]
Abstract
Cocaine dependence is associated with increased impulsivity in humans. Both cocaine dependence and impulsive behavior are under the regulatory control of cortico-striatal networks. One behavioral laboratory measure of impulsivity is response inhibition (ability to withhold a prepotent response) in which altered patterns of regional brain activation during executive tasks in service of normal performance are frequently found in cocaine dependent (CD) subjects studied with functional magnetic resonance imaging (fMRI). However, little is known about aberrations in specific directional neuronal connectivity in CD subjects. The present study employed fMRI-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity associated with response inhibition in CD subjects, elicited under performance of a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard). The performance on the Go/NoGo task was not significantly different between CD subjects and controls. The DCM analysis revealed that prefrontal–striatal connectivity was modulated (influenced) during the NoGo conditions for both groups. The effective connectivity from left (L) anterior cingulate cortex (ACC) to L caudate was similarly modulated during the Easy NoGo condition for both groups. During the Hard NoGo condition in controls, the effective connectivity from right (R) dorsolateral prefrontal cortex (DLPFC) to L caudate became more positive, and the effective connectivity from R ventrolateral prefrontal cortex (VLPFC) to L caudate became more negative. In CD subjects, the effective connectivity from L ACC to L caudate became more negative during the Hard NoGo conditions. These results indicate that during Hard NoGo trials in CD subjects, the ACC rather than DLPFC or VLPFC influenced caudate during response inhibition. Dynamic causal modeling was used to study response inhibition in cocaine dependence. A Go/NoGo task with two levels of NoGo difficulty (Easy and Hard) was used. Patients and controls used anterior cingulate cortex to control caudate during Easy NoGo. Controls used dorsolateral/ventrolateral prefrontal cortex to control caudate during Hard NoGo. Patients continued using anterior cingulate cortex to control caudate during Hard NoGo.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), Richmond, VA, USA ; Department of Radiology, VCU, Richmond, VA, USA
| | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), Richmond, VA, USA ; Department of Psychiatry, VCU, Richmond, VA, USA
| | - Kathryn A Cunningham
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, USA
| | - Scott D Lane
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center, Houston (UTHSC-H), USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), Richmond, VA, USA ; Department of Psychiatry, VCU, Richmond, VA, USA
| | - Harshini Neelakantan
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, USA
| | - Amanda E Price
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, USA
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, UTHSC-H, Houston, TX, USA
| | - Thomas R Kosten
- Department of Psychiatry and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Antoine Bechara
- Brain and Creativity Institute and Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University (VCU), Richmond, VA, USA ; Department of Psychiatry, VCU, Richmond, VA, USA ; Department of Pharmacology and Toxicology, Richmond, VCU, VA 23219, USA
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Limongi R, Tomio A, Ibanez A. Dynamical predictions of insular hubs for social cognition and their application to stroke. Front Behav Neurosci 2014; 8:380. [PMID: 25408640 PMCID: PMC4219475 DOI: 10.3389/fnbeh.2014.00380] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 10/16/2014] [Indexed: 12/20/2022] Open
Abstract
The insular cortex (IC) is considered a rich hub for context-sensitive emotions/social cognition. Patients with focal IC stroke provide unique opportunities to study socio-emotional processes. Nevertheless, Couto et al. (2013b) have recently reported controversial results regarding IC involvement in emotion and social cognition. Similarly, patients with similar lesions show high functional variability, ranging from almost totally preserved to strongly impaired behavior. Critical evidence suggests that the variability of these patients in the above domains can be explained by enhanced neuroplasticity, compensatory processes, and functional remapping after stroke. Therefore, socio-emotional processes would depend on long-distance connections between the IC and frontotemporal regions. We propose that predictive coding and effective connectivity represent a novel approach to explore functional connectivity and assess compensatory, contralateral, and subsidiary network differences among focal stroke patients. This approach would help explain why socio-emotional performance is so variable within this population.
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Affiliation(s)
- Roberto Limongi
- UDP-INECO Foundation Core on Neuroscience (UIFCoN), Diego Portales University Santiago, Chile
| | - Ailin Tomio
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive Neurology (INECO), Favaloro University Buenos Aires, Argentina
| | - Agustin Ibanez
- UDP-INECO Foundation Core on Neuroscience (UIFCoN), Diego Portales University Santiago, Chile ; Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive Neurology (INECO), Favaloro University Buenos Aires, Argentina ; Universidad Autónoma del Caribe Barranquilla, Colombia ; National Scientific and Technical Research Council (CONICET) Buenos Aires, Argentina ; Centre of Excellence in Cognition and its Disorders, Australian Research Council (ACR) Sydney, Australia
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Abstract
There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG–fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake–sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed.
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Affiliation(s)
- Maria Centeno
- Imaging and Biophysics Unit, Institute of Child Health, University College London , London , UK ; Epilepsy Unit, Great Ormond Street Hospital , London , UK
| | - David W Carmichael
- Imaging and Biophysics Unit, Institute of Child Health, University College London , London , UK ; Epilepsy Unit, Great Ormond Street Hospital , London , UK
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Kahan J, Urner M, Moran R, Flandin G, Marreiros A, Mancini L, White M, Thornton J, Yousry T, Zrinzo L, Hariz M, Limousin P, Friston K, Foltynie T. Resting state functional MRI in Parkinson's disease: the impact of deep brain stimulation on 'effective' connectivity. ACTA ACUST UNITED AC 2014; 137:1130-44. [PMID: 24566670 PMCID: PMC3959559 DOI: 10.1093/brain/awu027] [Citation(s) in RCA: 174] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Deep brain stimulation is an established therapy for Parkinson’s disease, although its mechanism of action remains unclear. Kahan et al. use resting state fMRI and dynamic causal modelling to study changes in ‘effective’ connectivity within the basal ganglia. Analyses implicate subthalamic afferents and the direct pathway in the clinical response. Depleted of dopamine, the dynamics of the parkinsonian brain impact on both ‘action’ and ‘resting’ motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the ‘effective’ connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network—disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses.
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Affiliation(s)
- Joshua Kahan
- 1 Sobell Department for Motor Neurosciences and Movement Disorders, UCL Institute of Neurology, London, UK
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Vaudano AE, Avanzini P, Tassi L, Ruggieri A, Cantalupo G, Benuzzi F, Nichelli P, Lemieux L, Meletti S. Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG. Front Neurol 2013; 4:185. [PMID: 24294210 PMCID: PMC3827676 DOI: 10.3389/fneur.2013.00185] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Accepted: 10/30/2013] [Indexed: 11/13/2022] Open
Abstract
Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.
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Affiliation(s)
- Anna Elisabetta Vaudano
- Department of Biomedical Sciences, Metabolism, and Neuroscience, NOCSE Hospital, University of Modena and Reggio Emilia , Modena , Italy ; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery , London , UK
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Bernal-Casas D, Balaguer-Ballester E, Gerchen MF, Iglesias S, Walter H, Heinz A, Meyer-Lindenberg A, Stephan KE, Kirsch P. Multi-site reproducibility of prefrontal-hippocampal connectivity estimates by stochastic DCM. Neuroimage 2013; 82:555-63. [PMID: 23747286 DOI: 10.1016/j.neuroimage.2013.05.120] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Revised: 05/10/2013] [Accepted: 05/29/2013] [Indexed: 11/24/2022] Open
Abstract
This study examined the reproducibility of prefrontal-hippocampal connectivity estimates obtained by stochastic dynamic causal modeling (sDCM). 180 healthy subjects were measured by functional magnetic resonance imaging (fMRI) during a standard working memory N-Back task at three different sites (Mannheim, Bonn, Berlin; each with 60 participants). The reproducibility of regional activations in key regions for working memory (dorsolateral prefrontal cortex, DLPFC; hippocampal formation, HF) was evaluated using conjunction analyses across locations. These analyses showed consistent activation of right DLPFC and deactivation of left HF across all three different sites. The effective connectivity between DLPFC and HF was analyzed using a simple two-region sDCM. For each subject, we evaluated sixty-seven alternative sDCMs and compared their relative plausibility using Bayesian model selection (BMS). Across all locations, BMS consistently revealed the same winning model, with the 2-Back working memory condition as driving input to both DLPFC and HF and with a connection from DLPFC to HF. Statistical tests on the sDCM parameter estimates did not show any significant differences across the three sites. The consistency of both the BMS results and model parameter estimates indicates the reliability of sDCM in our paradigm. This provides a basis for future genetic and clinical studies using this approach.
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Affiliation(s)
- D Bernal-Casas
- Bernstein-Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany.
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