The dysconnection hypothesis (2016)

Friston, Karl J.; Brown, Harriet; Siemerkus, Jakob; Stephan, Klaas Ε.

doi:10.1016/j.schres.2016.07.014

Cited by 500 publications

(471 citation statements)

References 142 publications

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“…Sparse rDCM may become particularly useful in the context of disorders for which global dysconnectivity has been suggested, such as schizophrenia (Anticevic et al, 2015;Bullmore et al, 1997;Friston et al, 2016;Friston and Frith, 1995;Stephan et al, 2006). In these situations, the computational framework introduced in this paper may deliver global "fingerprints" of aberrant functional integration, bringing computational phenotyping of whole-brain effective connectivity patterns in individual patients within reach (Stephan et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

A generative model of whole-brain effective connectivity

et al. 2018

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A B S T R A C TThe development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters.This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data -in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling -for example, for phenotyping individual patients in terms of wholebrain network structure. IntroductionThe human brain comprises multiple levels of organization, with cognitive functions arising from the interplay of functional specialization and integration (Sporns, 2013;Tononi et al., 1994). With the advent of non-invasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), researchers have begun to systematically study these fundamental properties of human brain organization. While early neuroimaging studies focused on localizing cognitive processes to specific brain regions, contemporary studies are commonly concerned with functional integration of these regions; this requires analyzing brain connectivity (Smith, 2012). In addition to analyses of structural (anatomical) connections, assessments of functional connectivity (statistical dependencies between network nodes) play a major role. Particularly, functional connectivity has been used frequently for studying the functional organization of large (whole-brain) networks both in tasks and in the "resting state" (i.e., unconstrained cognition in the absence of external perturbations). Various methods have been proposed (for a comprehensive review, see Karahanoglu and Van De Ville, 2017), ranging from conventional correlation or coherence analyses which assume stationarity (Fox et al., 2005) to sliding-window correlation analyses that can capture dyna...

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Section: Discussionmentioning

confidence: 99%

A generative model of whole-brain effective connectivity

et al. 2018

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“…Thus, cerebellar organization influences the fidelity of perceptions, detects errors, and rapidly modulates coordination. Schizophrenia may impair the proper modulation and coordination of multiple signals causing a misinterpretation of erroneous sensory associations usually suppressed by the cerebellum, resulting in the frequently noted evidence of abnormal predictive coding in the illness (17). …”

Section: Dysfunction In Regions Of the Core And Extended Motor Netmentioning

confidence: 99%

Motor system dysfunction in the schizophrenia diathesis: Neural systems to neurotransmitters

Abboud

Noronha

Diwadkar³

2017

Eur. psychiatr.

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Motor control is a ubiquitous aspect of human function, and from its earliest origins, abnormal motor control has been proposed as being central to schizophrenia. The neurobiological architecture of the motor system is well understood in primates and involves cortical and sub-cortical components including the primary motor cortex, supplementary motor area, dorsal anterior cingulate cortex, the prefrontal cortex, the basal ganglia, and cerebellum. Notably all of these regions are associated in some manner to the pathophysiology of schizophrenia. At the molecular scale, both dopamine and γ-Aminobutyric acid (GABA) abnormalities have been associated with working memory dysfunction, but particularly relating to the basal ganglia and the prefrontal cortex respectively. As evidence from multiple scales (behavioral, regional and molecular) converges, here we provide a synthesis of the bio-behavioral relevance of motor dysfunction in schizophrenia, and its consistency across scales. We believe that the selective compendium we provide can supplement calls arguing for renewed interest in studying the motor system in schizophrenia. We believe that in addition to being a highly relevant target for the study of schizophrenia related pathways in the brain, such focus provides tractable behavioral probes for in vivo imaging studies in the illness. Our assessment is that the motor system is a highly valuable research domain for the study of schizophrenia.

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“…The reiterative adjustment of motor prediction to the visual inputs may be a mechanism that optimizes neural responses to sensory inputs (Friston, 2009). Impaired prediction error is an emerging framework for understanding dysfunction in illnesses such as schizophrenia (Friston et al, 2016), and it is plausible that clinical obsessions distort neural mechanisms for optimizing prediction error in OCD. Because the uncertainty of sensory onsets in the random condition is certainly greater than those in the periodic condition, it is certainly true that participants are operating under a higher degree of uncertainty (reflected in the response profiles for the periodic and random conditions).…”

Section: Discussionmentioning

confidence: 99%

Brain network dysfunction in youth with obsessive-compulsive disorder induced by simple uni-manual behavior: The role of the dorsal anterior cingulate cortex

Friedman

Burgess

Ramaseshan

et al. 2017

Psychiatry Research: Neuroimaging

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In an effort to elucidate differences in functioning brain networks between youth with obsessive-compulsive disorder and controls, we used fMRI signals to analyze brain network interactions of the dorsal anterior cingulate cortex (dACC) during visually coordinated motor responses. Subjects made a uni-manual response to briefly presented probes, at periodic (allowing participants to maintain a “motor set”) or random intervals (demanding reactive responses). Network interactions were assessed using psycho-physiological interaction (PPI), a basic model of functional connectivity evaluating modulatory effects of the dACC in the context of each task condition. Across conditions, OCD were characterized by hyper-modulation by the dACC, with loci alternatively observed as both condition-general and condition-specific. Thus, dynamically driven task demands during simple uni-manual motor control induce compensatory network interactions in cortical-thalamic regions in OCD. These findings support previous research in OCD showing compensatory network interactions during complex memory tasks, but establish that these network effects are observed during basic sensorimotor processing. Thus, these patterns of network dysfunction may in fact be independent of the complexity of tasks used to induce brain network activity. Hypothesis-driven approaches coupled with sophisticated network analyses are a highly valuable approach in using fMRI to uncover mechanisms in disorders like OCD.

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The dysconnection hypothesis (2016)

Cited by 500 publications

References 142 publications

A generative model of whole-brain effective connectivity

A generative model of whole-brain effective connectivity

Motor system dysfunction in the schizophrenia diathesis: Neural systems to neurotransmitters

Brain network dysfunction in youth with obsessive-compulsive disorder induced by simple uni-manual behavior: The role of the dorsal anterior cingulate cortex

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