News


Centering matters for PPI analysis

posted Jan 21, 2017, 10:56 AM by Xin Di

We found that the default setting of PPI calculation in SPM5 and SPM8 may generate spurious PPI effects, because of an interaction between mean centering and deconvolution process. We suggest that the psychological variable should be centered before calculating the PPI term, or we should add a deconvolve-reconvolve reversion of the physiological variable into the PPI model to account for reconvolution artifacts. For details on this issue, please read our new paper, which was just published in Human Brain Mapping. 

Di X, Reynolds RC, Biswal BB (2017). Imperfect (de)convolution may introduce spurious psychophysiological interactions and how to avoid it. Hum Brain Mapp doi: 10.1002/hbm.23413. 

On task modulated connectivity of the amygdala

posted Jun 3, 2016, 12:19 PM by Xin Di

Our latest paper used meta-analysis to identify task modulated connectivity of the amygdala by different task domains. For more information see the full paper below:

Di X, Huang J, Biswal BB (accepted). Task-modulated brain connectivity of the amygdala: a meta-analysis of psychophysiological interactions. Brain Struct Funct doi:10.​1007/​s00429-016-1239-4link 

Sex differences of gray matter volume alterations in ASD

posted Nov 27, 2015, 1:21 PM by Xin Di   [ updated Nov 27, 2015, 1:22 PM ]

Our latest paper on sex differences of gray matter volume alterations in autism spectrum disorder (ASD) is out:


Task related connectivity changes

posted Sep 16, 2015, 6:43 AM by Xin Di

It is still challenging to study task related connectivity changes. We have been using a crude but simple sliding window method to characterize changes in connectivity during a block-designed task. This is a straightforward method to explore connectivity dynamics during tasks. Congratulations to my long term collaborators Zening Fu and Dr. Zhiguo Zhang.

Characterizations of resting-state modulatory interactions in human brain

posted Sep 4, 2015, 6:58 AM by Xin Di

Our new study on characterizations of resting-state modulatory interaction in the whole brain has just been published online

Abstract
Functional connectivity between two brain regions measured using functional MRI (fMRI) have been shown to be modulated by other regions even in resting-state, i.e. without performing specific tasks. We aimed to characterize large scale modulatory interactions by performing ROI-based (region of interest) physiophysiological interaction (PPI) analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all the significant modulatory interactions, there were considerably more negative than positive effects, i.e. in more cases an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases that the three ROIs were from a single module, suggesting increased within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases that the three ROIs were from two modules, suggesting a tendency of between modules segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole brain characteristics of modulatory interactions, and may provide guidance for future studies of connectivity dynamics in both resting-state and task-state.

Modulatory interactions between the default mode network and task positive networks

posted May 7, 2014, 9:57 AM by Xin Di   [ updated May 7, 2014, 9:58 AM ]

A new paper has been accepted by PeerJ

Modulatory interactions between the default mode network and task positive networks in resting-state

Several papers have been accepted over past months

posted Oct 29, 2013, 8:25 PM by Xin Di

Di X, Biswal BB (in press). Dynamic Brain Functional Connectivity Modulated by Resting-State Networks. Brain Structure and Function DOI:10.1007/s00429-013-0634-3. 
Di X, Rypma B, Biswal BB (2014). Correspondence of Executive Function Related Functional and Anatomical Alterations in Aging Brain. Prog Neuropsychopharmacol Biol Psychiatry 48(3):41–50.
Di X, Gohel S, Kim EH and Biswal BB (2013). Task vs. Rest - Different Network Configurations between the Coactivation and the Resting-State Brain Networks. Front. Hum. Neurosci. 7:493.
Di X, Biswal BB (2013). Modulatory interactions of resting-state brain functional connectivity. PLoS ONE 8(8): e71163. 

Dynamic causal modeling (DCM) on resting-state fMRI data

posted Aug 27, 2013, 7:51 PM by Xin Di

There is a growing interest to study the causal brain network structures in resting-state using dynamic causal modeling (DCM). It was suggested to model the spontaneous fluctuations as stochastic processes, however, we suggest to explicitly model the low-frequency fluctuation signals using Fourier series. In this study, we used Fourier series to model the input of DCM in resting-state, and studied the network structure underlying the default mode network (DMN). The current data validated the usage of deterministic DCM to study resting-state fMRI data by implementing Fourier series to model the low-frequency fluctuations. For more information, please click here

Di X, Biswal BB (2013). Identifying the Default Mode Network Structure Using Dynamic Causal Modeling on Resting-state Functional Magnetic Resonance Imaging. Neuroimage.

ALFF and functional connectivity

posted Apr 7, 2013, 9:53 AM by Xin Di

Our new paper was accepted by Frontiers in Human Neuroscience. This study examined the relationship between the amplitude of low frequency fluctuation (ALFF) and the functional connectivity in resting-state. The results demonstrated that the connectivity measured by both ICA and ROI based correlations was correlated with local ALFFs. The results may suggest a functional significance of local fluctuations on functional connectivity, but may also suggest that the ALFF need to be controlled when studying resting-state functional connectivity.

The paper: 


Brain metabolic networks

posted Oct 2, 2012, 8:27 PM by Xin Di

Our paper that studied the covariant networks of brain metabolism has been accepted by Brain Connectivity: 

Di X
, Biswal BB, Alzheimer's Disease Neuroimaging Initiative, (2012). Metabolic Brain Covariant Networks as Revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect. link

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