ABSTRACT
Objective To explore the recovery mechanism of language network among post-stroke aphasic patients by investigating the difference of whole-brain amplitude of low frequency fluctuations (ALFF) and the Granger causality analysis (GCA) between the patients and the controls. Methods From May, 2019 to May, 2021, 19 patients with aphasia after left hemispheric stroke and 17 age- and sex- matched healthy controls finished functional magnetic resonance imaging scanning. All the patients assessed with Chinese version of Western Aphasia Battery (WAB). Restplus was used for fMRI data analysis. Regions with significant difference of ALFF between groups were chosen as regions of interests (ROI) for the following GCA analysis. Results ALFF in left inferior frontal gyrus triangle (LIFGtri) and left medial frontal gyrus (LMFG) were significantly lower in the patients than in the controls. Effective connectivity from LIFGtri to LMFG and from LMFG to the right cerebellar Crus I were significantly lower in the patients. Effective connectivity from right cerebellar Crus II to LIFGtri, from right cerebellar Crus I to LMFG were significantly greater in the patients. Conclusion Unidirectional negative regulatory pathway such as LIFGtri→LMFG might be injured in post-stroke aphasia, while, connectivities between right cerebellar Crus II→LIFGtri and right cerebellar Crus I →LMFG enhanced. Right cerebellum might be the potential target for the language recovery.
ABSTRACT
Objective To explore the recovery mechanism of language network among post-stroke aphasic patients by investigating the difference of whole-brain amplitude of low frequency fluctuations (ALFF) and the Granger causality analysis (GCA) between the patients and the controls. Methods From May, 2019 to May, 2021, 19 patients with aphasia after left hemispheric stroke and 17 age- and sex- matched healthy controls finished functional magnetic resonance imaging scanning. All the patients assessed with Chinese version of Western Aphasia Battery (WAB). Restplus was used for fMRI data analysis. Regions with significant difference of ALFF between groups were chosen as regions of interests (ROI) for the following GCA analysis. Results ALFF in left inferior frontal gyrus triangle (LIFGtri) and left medial frontal gyrus (LMFG) were significantly lower in the patients than in the controls. Effective connectivity from LIFGtri to LMFG and from LMFG to the right cerebellar Crus I were significantly lower in the patients. Effective connectivity from right cerebellar Crus II to LIFGtri, from right cerebellar Crus I to LMFG were significantly greater in the patients. Conclusion Unidirectional negative regulatory pathway such as LIFGtri→LMFG might be injured in post-stroke aphasia, while, connectivities between right cerebellar Crus II→LIFGtri and right cerebellar Crus I →LMFG enhanced. Right cerebellum might be the potential target for the language recovery.
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Objective:To compare the difference in resting state networks among leukoaraiosis (LA) patients with or without mild cognitive impairment, and healthy controls, as well as the functional connectivity under Granger causality analysis (GCA). Methods:Subjects aged 40 to 80 years, including 34 LA-MCI patients, 15 LA patients with normal cognition and 33 healthy controls, accepted resting-state functional magnetic resonance imaging. Independent component analysis was used to separate functional brain networks, and difference of activation was determined with two sample t-test. GCA was used to analyze effective connectivity of these functional networks. Results:Eight resting state networks were obtained, including default mode network, motor network, medial visual network, lateral visual network, right-memory network, left-memory network, auditory network and executive network. Activation was different among three groups. Effective connectivity of RSNs was also different among three groups. Conclusion:Components of the resting state networks keep changing as LA progressing. Activation decreases as patients' cognition impaired. The direction and strength of connections remodel.
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ObjectiveThe purpose of this research was to investigate functional connectivity of 16-channel electroencephalograph(EEG) in δ frequency band based on Granger causality analysis.MethodsThe experimental data was recorded at a sampling rate of 200 Hz from temporal lobe epilepsy (TLE) patients(6 left and 3 right TLE,and 9 normals as control group.Ten of EEG segments of 20 s length for three different states:epileptiform discharges (ED) state in interictal durations,non-ED state for TLE patients,and control state for the normal.The δ band components (1～4 Hz) were filtered from EEGs.The functional connection values Iδ between two EEG δ components were calculated separately by Granger causality analysis.The two EEG components were from inferoposterior temporal lobe (left:T5,right:T6) to frontal lobe (Fp1,Fp2,F3,F4,and parietal lobe (C3,C4) for three states.ResultsThe Iδ values for ED state was 0.1323±0.0329～0.1670±0.028 9,which was significantly higher than that of non-ED state (0.0300±0.0130～0.0420±0.0072) (P＜0.05).The Iδ values for the control group (0.0153±0.0028～0.0193±0.0057) was much lower than that of ED state (P＜0.01),and no obvious distinctions were observed compared with non-ED state at P=0.05 level.ConclusionThere is a stronger connection of EEG' s δ bands from the inferoposterior temporal lobe to frontal and parietal lobe for the ED state,and the over-discharges transmission is from inferoposterior temporal lobe to other brain regions.There is a weaker connection from the top temporal lobe to frontal and parietal lobe for non-ED state and control group,and the onset zones is not inferoposterior temporal lobe.
ABSTRACT
How does brain select and adjust the distributed neural activities to achieve its function? To address this problem,researchers introduce Granger causality analysis method to brain functional study,which deals with the estimation of causal influences among multi-variables.First,basic principles of Granger Causality and its improved algorithm structural vector autoregression (SVAR) are introduced.Then several technical problems are reviewed which should be noted when analyzing brain functional signals by Granger Causality Methods.In the end,the application foreground of Granger Causality in epilepsy localization is introduced by taking idiopathic generalized epilepsy as the example.