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Karl J Friston - One of the best experts on this subject based on the ideXlab platform.

  • dynamic Causal Modeling of the relationship between cognition and theta alpha oscillations in adults with down syndrome
    Cerebral Cortex, 2019
    Co-Authors: Sarah Hamburg, Richard E Rosch, Carla M Startin, Karl J Friston, Andre Strydom
    Abstract:

    Individuals with Down syndrome (DS) show high inter-subject variability in cognitive ability and have an ultra-high risk of developing dementia (90% lifetime prevalence). Elucidating factors underlying variability in cognitive function can inform us about intellectual disability (ID) and may improve our understanding of factors associated with later cognitive decline. Increased neuronal inhibition has been posited to contribute to ID in DS. Combining electroencephalography (EEG) with dynamic Causal Modeling (DCM) provides a non-invasive method for investigating excitatory/inhibitory mechanisms. Resting-state EEG recordings were obtained from 36 adults with DS with no evidence of cognitive decline. Theta–alpha activity (4–13 Hz) was characterized in relation to general cognitive ability (raw Kaufmann’s Brief Intelligence Test second Edition (KBIT-2) score). Higher KBIT-2 was associated with higher frontal alpha peak amplitude and higher theta–alpha band power across distributed regions. Modeling this association with DCM revealed intrinsic self-inhibition was the key network parameter underlying observed differences in 4–13 Hz power in relation to KBIT-2 and age. In particular, intrinsic self-inhibition in right V1 was negatively correlated with KBIT-2. Results suggest intrinsic self-inhibition within the alpha network is associated with individual differences in cognitive ability in adults with DS, and may provide a potential therapeutic target for cognitive enhancement.

  • The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults.
    Cerebral cortex (New York N.Y. : 1991), 2017
    Co-Authors: Yuan Zhou, Karl J Friston, Peter Zeidman, Jie Chen, Adeel Razi
    Abstract:

    An important characteristic of spontaneous brain activity is the anticorrelation between the core default network (cDN) and the dorsal attention network (DAN) and the salience network (SN). This anticorrelation may constitute a key aspect of functional anatomy and is implicated in several brain disorders. We used dynamic Causal Modeling to assess the hypothesis that a Causal hierarchy underlies this anticorrelation structure, using resting-state fMRI of healthy adolescent and young adults (N = 404). Our analysis revealed an asymmetric effective connectivity, such that the regions in the SN and DAN exerted an inhibitory influence on the cDN regions; whereas the cDN exerted an excitatory influence on the SN and DAN regions. The relative strength of efferent versus afferent connections places the SN at the apex of the hierarchy, suggesting that the SN modulates anticorrelated networks with descending hierarchical connections. In short, this study of directed neuronal coupling reveals a Causal hierarchical architecture that generates or orchestrates anticorrelation of brain activity. These new findings shed light on functional integration of intrinsic brain networks at rest and speak to future dynamic Causal Modeling studies of large-scale networks.

  • dynamic Causal Modeling in ptsd and its dissociative subtype bottom up versus top down processing within fear and emotion regulation circuitry
    Human Brain Mapping, 2017
    Co-Authors: Karl J Friston, Andrew A Nicholson, Peter Zeidman, Sherain Harricharan, Margaret C Mckinnon, Maria Densmore
    Abstract:

    Objective Posttraumatic stress disorder (PTSD) is associated with decreased top-down emotion modulation from medial prefrontal cortex (mPFC) regions, a pathophysiology accompanied by hyperarousal and hyperactivation of the amygdala. By contrast, PTSD patients with the dissociative subtype (PTSD + DS) often exhibit increased mPFC top-down modulation and decreased amygdala activation associated with emotional detachment and hypoarousal. Crucially, PTSD and PTSD + DS display distinct functional connectivity within the PFC, amygdala complexes, and the periaqueductal gray (PAG), a region related to defensive responses/emotional coping. However, differences in directed connectivity between these regions have not been established in PTSD, PTSD + DS, or controls. Methods To examine directed (effective) connectivity among these nodes, as well as group differences, we conducted resting-state stochastic dynamic Causal Modeling (sDCM) pairwise analyses of coupling between the ventromedial (vm)PFC, the bilateral basolateral and centromedial (CMA) amygdala complexes, and the PAG, in 155 participants (PTSD [n = 62]; PTSD + DS [n = 41]; age-matched healthy trauma-unexposed controls [n = 52]). Results PTSD was characterized by a pattern of predominant bottom-up connectivity from the amygdala to the vmPFC and from the PAG to the vmPFC and amygdala. Conversely, PTSD + DS exhibited predominant top-down connectivity between all node pairs (from the vmPFC to the amygdala and PAG, and from the amygdala to the PAG). Interestingly, the PTSD + DS group displayed the strongest intrinsic inhibitory connections within the vmPFC. Conclusions These results suggest the contrasting symptom profiles of PTSD and its dissociative subtype (hyper- vs. hypo-emotionality, respectively) may be driven by complementary changes in directed connectivity corresponding to bottom-up defensive fear processing versus enhanced top-down regulation. Hum Brain Mapp 38:5551-5561, 2017. © 2017 Wiley Periodicals, Inc.

  • Causal Modeling methods and their application to speech and language
    2017
    Co-Authors: Karl J Friston, Baojuan Li, Seppo P Ahlfors, Dimitris A Pinotsis, Maria Mody
    Abstract:

    With the advent of non-invasive functional neuroimaging methods in the late 1970s, localization theories of language—based on brain lesion studies—have long given way to distributed models of language, implicating a network of sequential and parallel functional connections. This renders the processes within the speech and language network well suited to effective connectivity analysis using Causal Modeling approaches. Despite the large number of studies examining various components of the language system, the relationship between these processes and the directionality of Causal influences between the brain regions mediating these processes remain far less understood. The chapter presents select studies that have used Causal Modeling to investigate the neural basis of speech and language networks in healthy controls and clinical populations drawing on measures of directed functional connectivity and effective connectivity like Granger Causality and DCM. Applications to novel data from magnetoencephalography illustrate the usefulness of this approach to disorders of higher level cognition.

  • bridging the gap dynamic Causal Modeling and granger Causality analysis of resting state functional magnetic resonance imaging
    Brain, 2016
    Co-Authors: Sahil Bajaj, Karl J Friston, Bhim M Adhikari, Mukesh Dhamala
    Abstract:

    Granger Causality (GC) and dynamic Causal Modeling (DCM) are the two key approaches used to determine the directed interactions among brain areas. Recent discussions have provided a constructive account of the merits and demerits. GC, on one side, considers dependencies among measured responses, whereas DCM, on the other, models how neuronal activity in one brain area causes dynamics in another. In this study, our objective was to establish construct validity between GC and DCM in the context of resting state functional magnetic resonance imaging (fMRI). We first established the face validity of both approaches using simulated fMRI time series, with endogenous fluctuations in two nodes. Crucially, we tested both unidirectional and bidirectional connections between the two nodes to ensure that both approaches give veridical and consistent results, in terms of model comparison. We then applied both techniques to empirical data and examined their consistency in terms of the (quantitative) in-degree of key nodes of the default mode. Our simulation results suggested a (qualitative) consistency between GC and DCM. Furthermore, by applying nonparametric GC and stochastic DCM to resting-state fMRI data, we confirmed that both GC and DCM infer similar (quantitative) directionality between the posterior cingulate cortex (PCC), the medial prefrontal cortex, the left middle temporal cortex, and the left angular gyrus. These findings suggest that GC and DCM can be used to estimate directed functional and effective connectivity from fMRI measurements in a consistent manner.

Norihiro Sadato - One of the best experts on this subject based on the ideXlab platform.

  • neural networks for action representation a functional magnetic resonance imaging and dynamic Causal Modeling study
    Frontiers in Human Neuroscience, 2012
    Co-Authors: Akihiro T Sasaki, Takanori Kochiyama, Motoaki Sugiura, Hiroki C Tanabe, Norihiro Sadato
    Abstract:

    Automatic mimicry is based on the tight linkage between motor and perception action representations in which internal models play a key role. Based on the anatomical connection, we hypothesized that the direct effective connectivity from the posterior superior temporal sulcus (pSTS) to the ventral premotor area (PMv) formed an inverse internal model, converting visual representation into a motor plan, and that reverse connectivity formed a forward internal model, converting the motor plan into a sensory outcome of action. To test this hypothesis, we employed dynamic Causal-Modeling analysis with functional magnetic-resonance imaging. Twenty-four normal participants underwent a change-detection task involving two visually-presented balls that were either manually rotated by the investigator’s right hand (‘Hand’) or automatically rotated. The effective connectivity from the pSTS to the PMv was enhanced by hand observation and suppressed by execution, corresponding to the inverse model. Opposite effects were observed from the PMv to the pSTS, suggesting the forward model. Additionally, both execution and hand observation commonly enhanced the effective connectivity from the pSTS to the inferior parietal lobule (IPL), the IPL to the primary sensorimotor cortex (S/M1), the PMv to the IPL, and the PMv to the S/M1. Representation of the hand action therefore was implemented in the motor system including the S/M1. During hand observation, effective connectivity toward the pSTS was suppressed whereas that toward the PMv and S/M1 was enhanced. Thus the action-representation network acted as a dynamic feedback-control system during action observation.

Ewald Moser - One of the best experts on this subject based on the ideXlab platform.

  • disrupted effective connectivity between the amygdala and orbitofrontal cortex in social anxiety disorder during emotion discrimination revealed by dynamic Causal Modeling for fmri
    Cerebral Cortex, 2015
    Co-Authors: Ronald Sladky, Rupert Lanzenberger, Ewald Moser, Anna Hoflich, Martin Kublbock, Christoph Kraus, P Baldinger, Christian Windischberger
    Abstract:

    Social anxiety disorder (SAD) is characterized by over-reactivity of fear-related circuits in social or performance situations and associated with marked social impairment. We used dynamic Causal Modeling (DCM), a method to evaluate effective connectivity, to test our hypothesis that SAD patients would exhibit dysfunctions in the amygdala–prefrontal emotion regulation network. Thirteen unmedicated SAD patients and 13 matched healthy controls performed a series of facial emotion and object discrimination tasks while undergoing fMRI. The emotion-processing network was identified by a task-related contrast and motivated the selection of the right amygdala, OFC, and DLPFC for DCM analysis. Bayesian model averaging for DCM revealed abnormal connectivity between the OFC and the amygdala in SAD patients. In healthy controls, this network represents a negative feedback loop. In patients, however, positive connectivity from OFC to amygdala was observed, indicating an excitatory connection. As we did not observe a group difference of the modulatory influence of the FACE condition on the OFC to amygdala connection, we assume a context-independent reduction of prefrontal control over amygdalar activation in SAD patients. Using DCM, it was possible to highlight not only the neuronal dysfunction of isolated brain regions, but also the dysbalance of a distributed functional network.

  • the suppressive influence of sma on m1 in motor imagery revealed by fmri and dynamic Causal Modeling
    NeuroImage, 2008
    Co-Authors: C H Kasess, Christian Windischberger, Ross Cunnington, Rupert Lanzenberger, L Pezawas, Ewald Moser
    Abstract:

    Although motor imagery is widely used for motor learning in rehabilitation and sports training, the underlying mechanisms are still poorly understood. Based on fMRI data sets acquired with very high temporal resolution (300 ms) under motor execution and imagery conditions, we utilized Dynamic Causal Modeling (DCM) to determine effective connectivity measures between supplementary motor area (SMA) and primary motor cortex (M1). A set of 28 models was tested in a Bayesian framework and the by-far best-performing model revealed a strong suppressive influence of the motor imagery condition on the forward connection between SMA and M1. Our results clearly indicate that the lack of activation in M1 during motor imagery is caused by suppression from the SMA. These results highlight the importance of the SMA not only for the preparation and execution of intended movements, but also for suppressing movements that are represented in the motor system but not to be performed.

Godfrey D Pearlson - One of the best experts on this subject based on the ideXlab platform.

  • regression dynamic Causal Modeling for resting state fmri
    Human Brain Mapping, 2021
    Co-Authors: Stefan Frassle, Samuel J Harrison, Jakob Heinzle, Brett A Clementz, Carol A Tamminga, John A Sweeney, Elliot S Gershon, Matcheri S Keshavan, Godfrey D Pearlson
    Abstract:

    "Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI-regression dynamic Causal Modeling (rDCM)-extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

Joel Schwartz - One of the best experts on this subject based on the ideXlab platform.