Inhibition in early Alzheimer's disease: An fMRI-based study of effective connectivity

Rytsar, Romana; Fornari, Eleonora; Frackowiak, R. S. J.; Ghika, Joseph; Knyazeva, Maria G.

doi:10.1016/j.neuroimage.2011.05.029

Cited by 36 publications

(24 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approaches estimating the effective connectivity generally start with sets of assumptions on the inherent data structure (time series, correlation matrix or higher-order statistics) or underlying biophysics to be modeled, then seek the optimum models using criteria such as maximum likelihoods or Bayesian inferences, and finally invoke the learned model parameters to conclude causality or conditional dependences. The most common approaches include dynamic causal modeling (DCM) (Friston, Harrison et al 2003, Penny, Stephan et al 2004, Lee, Friston et al 2006, Stephan, Weiskopf et al 2007, Marreiros, Kiebel et al 2008, Stephan, Kasper et al 2008, Penny, Stephan et al 2010, Schuyler, Ollinger et al 2010, Seghier, Zeidman et al 2010, Stephan, Penny et al 2010, Daunizeau, David et al 2011, Friston, Li et al 2011, Li, Daunizeau et al 2011, Lohmann, Erfurth et al 2012, Friston, Kahan et al 2014), Granger causality analysis (Granger 1969, Goebel, Roebroeck et al 2003, Harrison, Penny et al 2003, Roebroeck, Formisano et al 2005, Deshpande, LaConte et al 2009), structural equation modeling (SEM) (McIntosh 1994, Buchel and Friston 1997, Horwitz, Tagamets et al 1999, Bullmore, Horwitz et al 2000), psychophysiological interaction (Friston, Buechel et al 1997), graphical causal modeling (Pearl 2000, Spirtes 2000), dynamic Bayesian networks (Rajapakse and Zhou 2007), and switching linear dynamic system (Smith, Pillai et al 2010); and have been actively employed in clinical studies to identify abnormal interactions in patients (e.g., Alzheimer’s disease (Agosta, Rocca et al 2010, Rytsar, Fornari et al 2011, Liu, Zhang et al 2012, Neufang, Akhrif et al 2014, Zhong, Huang et al 2014), depression (Schlosser, Wagner et al 2008, Almeida, Versace et al 2009, Goulden, McKie et al 2010, Moses-Kolko, Perlman et al 2010, Hamilton, Chen et al 2011, Iwabuchi, Peng et al 2014, Liu, Wu et al 2015), and schizophrenia (Schlosser, Gesierich et al 2003, Kim, Burge et al 2008, Benetti, Mechelli et al 2009, Crossley, Mechelli et al 2009, Dima, Roiser et al 2009, Allen, Stephan et al 2010, Diaconescu, Jensen et al 2011, Deserno, Sterzer et al 2012, Guller, Tononi et al 2012, Muk...…”

Section: Discussionmentioning

confidence: 99%

Functional Magnetic Resonance Imaging Methods

2015

View full text Add to dashboard Cite

Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the “resting state”). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.

show abstract

Section: Discussionmentioning

confidence: 99%

Functional Magnetic Resonance Imaging Methods

2015

View full text Add to dashboard Cite

show abstract

“…For example, while dynamic casual modeling has been shown to be more predictive of memory success than simple task activations (Gagnepain et al, 2011), it requires the analysis to be limited to only an a priori specified and small set of brain regions(Neufang et al, 2011; Rytsar et al, 2011). Yet, collectively, previous studies support that context-dependent connectivity has the potential to characterize neural correlates of synaptic, neuronal and/or neurovascular integrity as they relate to cognition and behavioral performance.…”

Section: Introductionmentioning

confidence: 99%

Flexible modulation of network connectivity related to cognition in Alzheimer's disease

McLaren¹,

Sperling²,

Atri³

2014

NeuroImage

View full text Add to dashboard Cite

Functional neuroimaging tools, such as fMRI methods, may elucidate the neural correlates of clinical, behavioral, and cognitive performance. Most functional imaging studies focus on regional task-related activity or resting state connectivity rather than how changes in functional connectivity across conditions and tasks are related to cognitive and behavioral performance. To investigate the promise of characterizing context-dependent connectivity-behavior relationships, this study applies the method of generalized psychophysiological interactions (gPPI) to assess the patterns of associative-memory-related fMRI hippocampal functional connectivity in Alzheimer’s disease (AD) associated with performance on memory and other cognitively demanding neuropsychological tests and clinical measures. Twenty-four subjects with mild AD dementia (ages 54–82, nine females) participated in a face-name paired-associate encoding memory study. Generalized PPI analysis was used to estimate the connectivity between the hippocampus and the whole brain during encoding. The difference in hippocampal-whole brain connectivity between encoding novel and repeated face-name pairs was used in multiple-regression analyses as an independent predictor for 10 behavioral, neuropsychological and clinical tests. The analysis revealed connectivity-behavior relationships that were distributed, dynamically overlapping, and task-specific within and across intrinsic networks; hippocampal-whole brain connectivity-behavior relationships were not isolated to single networks, but spanned multiple brain networks. Importantly, these spatially distributed performance patterns were unique for each measure. In general, out-of-network behavioral associations with encoding novel greater than repeated face-name pairs hippocampal-connectivity were observed in the default-mode network, while correlations with encoding repeated greater than novel face-name pairs hippocampal-connectivity were observed in the executive control network (p<0.05, cluster corrected). Psychophysiological interactions revealed significantly more extensive and robust associations between paired-associate encoding task-dependent hippocampal-whole brain connectivity and performance on memory and behavioral/clinical measures than previously revealed by standard activity-behavior analysis. Compared to resting state and task-activation methods, gPPI analyses may be more sensitive to reveal additional complementary information regarding subtle within- and between-network relations. The patterns of robust correlations between hippocampal-whole brain connectivity and behavioral measures identified here suggest that there are ‘coordinated states’ in the brain; that the dynamic range of these states is related to behavior and cognition; and that these states can be observed and quantified, even in individuals with mild AD.

show abstract

“…The IF increase found in AD might have different causes, perhaps due to a compensatory reorganization of brain circuits due to synaptic plasticity (Adams, 1991) or due to the fact that AD patients might fail in ignoring irrelevant inputs when integrating information to perform particular cognitive tasks (Rodriguez et al, 1999) or due to the reduction in inhibitory modulatory influence across the whole-brain network in AD (Amieva et al, 2004;Bentley et al, 2008;Rytsar et al, 2011); however, the exact mechanism producing an increase of IF in AD needs further investigation.…”

Section: Discussionmentioning

confidence: 99%