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1 isting methods such as Granger causality and dynamic causal modeling.
2 eous interactions among these networks using dynamic causal modeling.
3  regions was modeled at the group level with dynamic causal modeling.
4 magnetic resonance imaging and analyzed with dynamic causal modeling.
5 cated by psychophysiological interaction and dynamic causal modeling.
6 etal brain regions, were characterized using dynamic causal modeling.
7 effective connectivity derived from spectral dynamic causal modeling.
8 motion-processing network were modeled using dynamic causal modeling.
9 mentary motor area (SMA) were assessed using dynamic causal modeling.
10 ormation flow among these nodes, we employed dynamic causal modeling.
11 arity modulates AON activity in humans using dynamic causal modeling, a type of effective connectivit
12                                              Dynamic causal modeling analyses demonstrated that input
13  activity formed the regions of interest for dynamic causal modeling analyses, which revealed attenua
14 f end-spectral bias were further revealed by dynamic causal modeling analysis.
15                                  Here we use dynamic causal modeling and a Bayesian model evidence te
16 al networks using electroencephalography and dynamic causal modeling and found that in young adults w
17                                 We then used dynamic causal modeling and identified several cerebella
18                        In this work, we used dynamic causal modeling approach to provide insights int
19                                              Dynamic causal modeling assessed evidence for effective
20                                 Furthermore, dynamic causal modeling confirmed that FTO variants modu
21                                              Dynamic causal modeling corroborated and extended these
22                           Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI exper
23                             We used spectral dynamic causal modeling (DCM) for resting-state fMRI dat
24                                  Previously, dynamic causal modeling (DCM) indicated that mid LPFC in
25                                        Using dynamic causal modeling (DCM) of cross-spectral densitie
26 nsitive perceptual learning process; (2) the dynamic causal modeling (DCM) of evoked responses uncove
27  left hemisphere regions were examined using dynamic causal modeling (DCM) of functional magnetic res
28                                              Dynamic causal modeling (DCM) of regional responses in t
29 rally specific EEG variability, we performed dynamic causal modeling (DCM) on the fMRI data.
30                                 Furthermore, dynamic causal modeling (DCM) revealed that the strength
31 e used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis t
32                                      We used dynamic causal modeling (DCM) to identify changes in eff
33                            We used fMRI with dynamic causal modeling (DCM) to investigate evidence fo
34                                    Moreover, dynamic causal modeling (DCM) was performed to identify
35 tional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) were used to study multire
36                In previous studies involving dynamic causal modeling (DCM) which embodies the hemodyn
37                                  By applying dynamic causal modeling (DCM), a Bayesian technique for
38 ploy independent structure-function mapping, dynamic causal modeling (DCM), and frequency-resolved fu
39 and dementia, in its construct validation of dynamic causal modeling (DCM), and human confirmation of
40   In the present study, we established-using dynamic causal modeling (DCM)-the direction of informati
41 on network was quantified using (stochastic) dynamic causal modeling (DCM).
42 d secondary somatosensory cortex by means of dynamic causal modeling (DCM).
43 ring a rhyming judgment task in adults using dynamic causal modeling (DCM).
44                                              Dynamic causal modeling demonstrated that changes in bac
45                                              Dynamic causal modeling evidence suggested that treatmen
46 s of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and e
47                                              Dynamic causal modeling for event related responses reve
48                                        Using dynamic causal modeling for magnetoencephalography with
49        Effective connectivity analyses using dynamic causal modeling found an excitatory pathway from
50 ant based on neural mass modeling within the Dynamic causal modeling framework, further suggested exc
51                                              Dynamic causal modeling indicated this increase of activ
52 tic tractography) and functional (stochastic dynamic causal modeling) measures of prefrontal-limbic c
53 of functional magnetic resonance imaging and dynamic causal modeling might be used in the future for
54                                        Using dynamic causal modeling of concurrently collected EMG-fM
55                                    Moreover, dynamic causal modeling of each motivational system conf
56                                      We used Dynamic Causal Modeling of effective connectivity and Ba
57                                 Here we used dynamic causal modeling of event-related potentials, com
58                                   We applied Dynamic Causal Modeling of evoked electromagnetic respon
59 uishes between these two hypotheses by using dynamic causal modeling of fMRI data acquired in a prese
60                                        Using dynamic causal modeling of fMRI data in 586 healthy subj
61                                              Dynamic causal modeling of frontoparietal connectivity c
62                                      We used dynamic causal modeling of functional magnetic resonance
63                                   We applied dynamic causal modeling of functional magnetic resonance
64                This evidence is furnished by dynamic causal modeling of mismatch responses, elicited
65                              Second, we used dynamic causal modeling of the patient's fMRI data to un
66                                              Dynamic causal modeling of this interaction revealed tha
67                                              Dynamic causal modeling of this system revealed that evo
68                               Using spectral dynamic causal modeling on resting-state functional magn
69  Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.
70 ogressed to more advanced frameworks such as dynamic causal modeling, recurrent neural networks, and
71                                              Dynamic causal modeling revealed a modulation of the con
72                                              Dynamic causal modeling revealed asymmetrical LPFC inter
73 effective connectivity analysis, the optimal dynamic causal modeling revealed enhanced connectivity a
74                                              Dynamic causal modeling revealed that posterior cingulat
75                                              Dynamic causal modeling revealed that VS-to-CBL connecti
76 area (SMA), using both mediator analysis and dynamic causal modeling, revealed that (1) THAL fMRI blo
77                                              Dynamic causal modeling showed that each task preferenti
78 st, analyses of effective connectivity using dynamic causal modeling showed that magnocellular-biased
79                                              Dynamic causal modeling showed that this reconfiguration
80                                              Dynamic causal modeling suggested that stimulus expectat
81                Axiomatic tests combined with dynamic causal modeling suggested that ventromedial pref
82                                              Dynamic causal modeling suggests a directional influence
83             Regardless of genotype, however, dynamic causal modeling supports unidirectional gustator
84 e regions and on their interactions, we used dynamic causal modeling to analyze functional magnetic r
85                                    Employing dynamic causal modeling to assay the synaptic mechanisms
86 ed functional magnetic resonance imaging and dynamic causal modeling to characterize effective connec
87                                 We then used dynamic causal modeling to contrast 12 possible models o
88                                   We applied dynamic causal modeling to demonstrate that the most lik
89 s theory in the tactile modality, we applied dynamic causal modeling to electroencephalography (EEG)
90 zophrenia patients, 42 healthy controls) and dynamic causal modeling to examine effective connectivit
91                      In addition, we applied dynamic causal modeling to identify the neural model tha
92 lysergic acid diethylamide (LSD), we applied dynamic causal modeling to infer effective connectivity
93                                 We then used dynamic causal modeling to infer premovement interaction
94 ifferentially from previous studies, we used dynamic causal modeling to model neural activity recorde
95                                        Using dynamic causal modeling to quantify changes in effective
96 ensory statistical learning errors, and used dynamic causal modeling to tap into the underlying neura
97 used an effective connectivity method (i.e., dynamic causal modeling) to investigate the consequences
98                                              Dynamic causal modeling was used to investigate the neur
99                                              Dynamic causal modeling was used to map frontoamygdalar
100                                              Dynamic causal modeling was used to quantify synaptic co
101                                              Dynamic causal modeling was used with Bayesian model sel
102 ng functional magnetic resonance imaging and dynamic causal modeling, we examined effective connectiv
103                           Furthermore, using dynamic causal modeling, we found that drug-induced diff
104  Critically, using fMRI latency analysis and dynamic causal modeling, we go on to demonstrate functio
105 sing distortion-corrected functional MRI and dynamic causal modeling, we investigated the interaction
106                                        Using dynamic causal modeling, we tested: (1) whether activity
107                                        Using dynamic causal modeling, we then assessed the neural dyn
108 al-interaction in a general linear model and dynamic causal modeling) were used to assess the impact
109                                    Moreover, dynamic causal modeling with Bayesian model selection id

 
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