ライフサイエンスコーパス: dynamic causal modeling

1 eous interactions among these networks using dynamic causal modeling.

2 magnetic resonance imaging and analyzed with dynamic causal modeling.

3 cated by psychophysiological interaction and dynamic causal modeling.

4 etal brain regions, were characterized using dynamic causal modeling.

5 motion-processing network were modeled using dynamic causal modeling.

6 mentary motor area (SMA) were assessed using dynamic causal modeling.

7 arity modulates AON activity in humans using dynamic causal modeling, a type of effective connectivit

8 Dynamic causal modeling analyses demonstrated that input

9 activity formed the regions of interest for dynamic causal modeling analyses, which revealed attenua

10 Here we use dynamic causal modeling and a Bayesian model evidence te

11 al networks using electroencephalography and dynamic causal modeling and found that in young adults w

12 In this work, we used dynamic causal modeling approach to provide insights int

13 Furthermore, dynamic causal modeling confirmed that FTO variants modu

14 Dynamic causal modeling corroborated and extended these

15 Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI exper

16 Previously, dynamic causal modeling (DCM) indicated that mid LPFC in

17 Using dynamic causal modeling (DCM) of cross-spectral densitie

18 left hemisphere regions were examined using dynamic causal modeling (DCM) of functional magnetic res

19 Dynamic causal modeling (DCM) of regional responses in t

20 rally specific EEG variability, we performed dynamic causal modeling (DCM) on the fMRI data.

21 We used fMRI with dynamic causal modeling (DCM) to investigate evidence fo

22 Moreover, dynamic causal modeling (DCM) was performed to identify

23 tional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) were used to study multire

24 In previous studies involving dynamic causal modeling (DCM) which embodies the hemodyn

25 By applying dynamic causal modeling (DCM), a Bayesian technique for

26 on network was quantified using (stochastic) dynamic causal modeling (DCM).

27 d secondary somatosensory cortex by means of dynamic causal modeling (DCM).

28 ring a rhyming judgment task in adults using dynamic causal modeling (DCM).

29 Dynamic causal modeling demonstrated that changes in bac

30 s of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and e

31 Dynamic causal modeling for event related responses reve

32 Effective connectivity analyses using dynamic causal modeling found an excitatory pathway from

33 ant based on neural mass modeling within the Dynamic causal modeling framework, further suggested exc

34 Dynamic causal modeling indicated this increase of activ

35 tic tractography) and functional (stochastic dynamic causal modeling) measures of prefrontal-limbic c

36 of functional magnetic resonance imaging and dynamic causal modeling might be used in the future for

37 Using dynamic causal modeling of concurrently collected EMG-fM

38 Moreover, dynamic causal modeling of each motivational system conf

39 We used Dynamic Causal Modeling of effective connectivity and Ba

40 Here we used dynamic causal modeling of event-related potentials, com

41 We applied Dynamic Causal Modeling of evoked electromagnetic respon

42 uishes between these two hypotheses by using dynamic causal modeling of fMRI data acquired in a prese

43 Using dynamic causal modeling of fMRI data in 586 healthy subj

44 Dynamic causal modeling of frontoparietal connectivity c

45 We used dynamic causal modeling of functional magnetic resonance

46 We applied dynamic causal modeling of functional magnetic resonance

47 This evidence is furnished by dynamic causal modeling of mismatch responses, elicited

48 Second, we used dynamic causal modeling of the patient's fMRI data to un

49 Dynamic causal modeling of this system revealed that evo

50 Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

51 Dynamic causal modeling revealed a modulation of the con

52 Dynamic causal modeling revealed asymmetrical LPFC inter

53 effective connectivity analysis, the optimal dynamic causal modeling revealed enhanced connectivity a

54 Dynamic causal modeling revealed that posterior cingulat

55 area (SMA), using both mediator analysis and dynamic causal modeling, revealed that (1) THAL fMRI blo

56 Dynamic causal modeling showed that each task preferenti

57 st, analyses of effective connectivity using dynamic causal modeling showed that magnocellular-biased

58 Dynamic causal modeling showed that this reconfiguration

59 Dynamic causal modeling suggested that stimulus expectat

60 Axiomatic tests combined with dynamic causal modeling suggested that ventromedial pref

61 Regardless of genotype, however, dynamic causal modeling supports unidirectional gustator

62 e regions and on their interactions, we used dynamic causal modeling to analyze functional magnetic r

63 Employing dynamic causal modeling to assay the synaptic mechanisms

64 ed functional magnetic resonance imaging and dynamic causal modeling to characterize effective connec

65 We then used dynamic causal modeling to contrast 12 possible models o

66 We applied dynamic causal modeling to demonstrate that the most lik

67 s theory in the tactile modality, we applied dynamic causal modeling to electroencephalography (EEG)

68 zophrenia patients, 42 healthy controls) and dynamic causal modeling to examine effective connectivit

69 In addition, we applied dynamic causal modeling to identify the neural model tha

70 We then used dynamic causal modeling to infer premovement interaction

71 ifferentially from previous studies, we used dynamic causal modeling to model neural activity recorde

72 used an effective connectivity method (i.e., dynamic causal modeling) to investigate the consequences

73 Dynamic causal modeling was used to investigate the neur

74 Dynamic causal modeling was used with Bayesian model sel

75 ng functional magnetic resonance imaging and dynamic causal modeling, we examined effective connectiv

76 Furthermore, using dynamic causal modeling, we found that drug-induced diff

77 Critically, using fMRI latency analysis and dynamic causal modeling, we go on to demonstrate functio

78 sing distortion-corrected functional MRI and dynamic causal modeling, we investigated the interaction

79 Using dynamic causal modeling, we tested: (1) whether activity

80 al-interaction in a general linear model and dynamic causal modeling) were used to assess the impact

81 Moreover, dynamic causal modeling with Bayesian model selection id