Abstract: Spontaneous brain activity or off-line activity after memory encoding is associated with memory consolidation. A few recent resting-state functional magnetic resonance imaging (RS-fMRI) studies indicate that the RS-fMRI could map off-line memory consolidation effects. However, the gene effects on memory consolidation process remain largely unknown. Here we collected two RS-fMRI sessions, one before and another after an episodic memory encoding task, from two groups of healthy young adults, one with apolipoprotein E (APOE) ε2/ε3 and the other with APOE ε3/ε4. The ratio of regional homogeneity (ReHo), a measure of local synchronization of spontaneous RS-fMRI signal, of the two sessions was used as an index of memory-consolidation. APOE ε3/ε4 group showed greater ReHo ratio within the medial temporal lobe (MTL). The ReHo ratio in MTL was significantly correlated with the recognition memory performance in the APOE ε3/ε4 group but not in ε2/ε3 group. Additionally, APOE ε3/ε4 group showed lower ReHo ratio in the occipital and parietal picture-encoding areas. Our results indicate that APOE ε3/ε4 group may have a different off-line memory consolidation process compared to ε2/ε3 group. These results may help generate future hypotheses that the off-line memory consolidation might be impaired in Alzheimer’s disease.
Abstract: The corpus callosum (CC) is one of focused target areas which may play an important role in the pathophysiology of attention deficit hyperactivity disorder (ADHD). Conventional structural magnetic resonance imaging (MRI) studies have revealed the macrostructural abnormalities of CC and its subdivisions in ADHD compared with controls. However, no study has examined the macrostructural and microstructural characteristics of the CC in the same ADHD group. In this study, MRI morphometric and diffusion tensor imaging (DTI) techniques were combined to explore the area and measure fractional anisotropy (FA) abnormality of CC and its seven subdivisions in children with ADHD. Twenty-eight boys with ADHD (13.3+/-1.5 years) and 27 age- and gender- matched controls (13.2+/-0.9 years) were included. We co-registered individual structural MRI and DTI images manually and subdivided the midsagittal CC into seven subdivisions. The area and FA of the CC and its subdivisions were then compared between the patients and the matched controls. Results showed that ADHD had decreased area of entire CC, anterior middle-body, and isthmus. Meanwhile, reduced FA value of the isthmus was found in the ADHD group compared with the controls. Our study indicated that not only macrostructural abnormalities but also microstructural alterations in CC, especially in isthmus occurred in ADHD. The abnormality of the isthmus, the subdivision that contains the fibers connecting posterior regions of brain, may play an important role in the pathophysiology of ADHD and may be implicated in the disorders of attention. Copyright 2009 Elsevier B.V. All rights reserved.
Abstract: Functional connectivity has become one of the important approaches to understanding the functional organization of the human brain. Recently, functional near-infrared spectroscopy (fNIRS) has been demonstrated as a feasible method to study resting–state functional connectivity (RSFC) in the sensory and motor systems. However, whether such fNIRS-based RSFC can be revealed in high-level and complex functional systems remains unknown. In the present study, the feasibility of such an approach was tested on the language system, of which the neural substrates have been well documented in the literature. After determination of a seed-channel by a language localizer task, the correlation strength between the low frequency fluctuations of the fNIRS signal at the seed-channel and those at all other channels was used to evaluate the language system RSFC. Our results show a significant RSFC between the left inferior frontal cortex and superior temporal cortex, components both associated with the dominant language regions. Moreover, the RSFC map demonstrated left lateralization of the language system. In conclusion, the present study successfully utilized fNIRS-based RSFC to study a complex and high-level neural system and provides further evidence for the validity of the fNIRS-based RSFC approach
Abstract: Recently, resting state functional connectivity (RSFC) studies based on fMRI and EEG/MEG have provided
valuable insight into the intrinsic functional architecture of the human brain. However, whether
functional near infrared spectroscopy (fNIRS), a suitable imaging method for infant and patient populations,
can be used to examine RSFC remains elusive. Using an ETG-4000 Optical Topography System,
the present study measured 29 adult subjects (14 females) over the sensorimotor and auditory cortexes
during a resting session and a motor-localizer task session. The RSFC maps were computed by seed-based
correlation analysis and data-driven cluster analysis. The results from both analyses showed robust RSFC
maps, which were not only consistent with the localizer task-related activation results, but also those
of previous fMRI findings. Moreover, the strong consistency between the seed-based correlation analysis
and the data-driven cluster analysis further validated the use of fNIRS to assess RSFC. The potential
influence of a specific low-frequency filtering range (0.04–0.15 Hz and 0.01–0.08 Hz) and three fNIRS
parameters (oxy-Hb, deoxy-Hb, and total-Hb) on RSFC results were also examined.
Abstract: As a promising non-invasive imaging technique, functional near infrared spectroscopy (fNIRS) has recently earned increasing attention in resting-state functional connectivity (RSFC) studies. Preliminary fNIRS-based RSFC studies adopted a seed correlation approach and yielded interesting results. However, the seed correlation approach has several inherent problems, such as neglecting of interactions among multiple regions and a dependence on seed region selection. Moreover, ineffectively reduced noise and artifacts in fNIRS measurements also negatively affect RSFC results. In this study, independent component analysis (ICA) was introduced to meet these challenges in RSFC detection based on resting-state fNIRS measurements. The results of ICA on data from the sensorimotor system and the visual systems both showed functional system-specific RSFC maps. Results from comparison between ICA and the conventional seed correlation approach demonstrated,both qualitatively and quantitatively, the superior performance of ICA with higher sensitivity and specificity, especially in the case of higher noise level. The apability of ICA to separate noise and artifacts from resting-state fNIRS data was also demonstrated, and the extracted noise and artifacts were illustrated. Finally, some practical issues on performing ICA on resting-state fNIRS data were discussed.
Abstract: Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance 22
imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group 23
ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data 24
reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is 25
difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. 26
Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject 27
concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and 28
generates variable-independent component decompositions. In this study, a rigorous theoretical analysis 29
was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to 30
demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To 31
solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and 32
real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results 33
compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI 34
studies, especially those with large data sets.
Abstract: Numerous studies argue that cortical reorganization may contribute to the restoration of motor function following stroke. However, the evolution of changes in the process of the post-stroke reorganization has been little studied. This study sought to identify dynamic changes in the functional organization, particularly topological characteristics, of the motor execution network during the stroke recovery process. Ten patients (nine male and one female) with subcortical infarctions were assessed by neurological examination and scanned with resting-state functional magnetic resonance imaging across five consecutive time points in a single year. The motor execution network of each subject was constructed using a functional connectivity matrix between 21 brain regions and subsequently analysed using graph theoretical approaches. Dynamic changes in topological configuration of the network during the process of recovery were evaluated by a mixed model. We found that the motor execution network gradually shifted towards a random mode during the recovery process, which suggests that a less optimized reorganization is involved in regaining function in the affected limbs. Significantly increased regional centralities within the network were observed in the ipsilesional primary motor area and contralesional cerebellum, whereas the ipsilesional cerebellum showed decreased regional centrality. Functional connectivity to these brain regions demonstrated consistent alterations over time. Notably, these measures correlated with different clinical variables, which provided support that the findings may reflect the adaptive reorganization of the motor execution network in stroke patients. In conclusion, the study expands our understanding of the spectrum of changes occurring in the brain after stroke and provides a new avenue for investigating lesion-induced network plasticity.
Notes: Scientific Commentaries： Christian Gerloff and Mark Hallett, Big news from small world networks after stroke, Brain. 2010 133(4):952-955
Abstract: In this study, we investigated the changes in topological architectures of brain functional networks
in attention-deficit/hyperactivity disorder (ADHD). Functional magnetic resonance images
(fMRI) were obtained from 19 children with ADHD and 20 healthy controls during resting state. Brain
functional networks were constructed by thresholding the correlation matrix between 90 cortical and
subcortical regions and further analyzed by applying graph theoretical approaches. Experimental
results showed that, although brain networks of both groups exhibited economical small-world topology,
altered functional networks were demonstrated in the brain of ADHD when compared with the
normal controls. In particular, increased local efficiencies combined with a decreasing tendency in
global efficiencies found in ADHD suggested a disorder-related shift of the topology toward regular
networks. Additionally, significant alterations in nodal efficiency were also found in ADHD, involving
prefrontal, temporal, and occipital cortex regions, which were compatible with previous ADHD studies.
The present study provided the first evidence for brain dysfunction in ADHD from the viewpoint
of global organization of brain functional networks by using resting-state fMRI.
Abstract: In this study, a resting-state fMRI based classifier, for the first time, was proposed and applied to discriminate children with attentiondeficit/hyperactivity disorder (ADHD) from normal controls. On the basis of regional homogeneity (ReHo), a mapping of brain function at resting state, PCA-based Fisher discriminative analysis (PC-FDA) was trained to build a linear classifier. Permutation test was then conducted to identify the brain areas with the most significant contribution to the final iscrimination. Experimental results showed a correct classification rate of 85% using a leave-one-out crossvalidation.
Moreover, some highly discriminative brain regions, like the prefrontal cortex and anterior cingulate cortex, well confirmed the
previous findings on ADHD. Interestingly, some important but less reported regions such as the thalamus were also identified. We conclude that the classifier, using resting-state brain function as classification feature, has potential ability to improve current diagnosis and treatment evaluation of ADHD.
Abstract: Previous studies have witnessed that complex brain networks have
the properties of high global and local efficiency. In this study, we investigated
the gender effect on brain functional networks measured using functional
magnetic resonance imaging (fMRI). Our experimental results showed that
there were no significant difference in global and local efficiency between male
and female. However, the gender-related effects on nodal efficiency were found
at several brain regions, including the left middle frontal gyrus, right superior
temporal gyrus, left middle cingulum gyrus, left hippocampus, right
hippocampus, right parahippocampal and left amygdala. These results were
compatible with previous findings. To our knowledge, this study provided the
first evidence of gender effect on the efficiency of brain functional networks
using resting-state fMRI.
Abstract: Current analysis of diffusion tensor imaging (DTI) is based mostly on a region of interest (ROI)
in an image dataset, which is specified by users. This method is not always reliable, however, because of
the uncertainty of manual specification. We introduce an improved fiber-based scheme rather than an
ROI-based analysis to study in DTI datasets of 31 normal subjects the asymmetry of the cingulum, which
is one of the most prominent white matter fiber tracts of the limbic system. The present method can
automatically extract the quantitative anisotropy properties along the cingulum bundles from tractography.
Moreover, statistical analysis was carried out after anatomic correspondence specific to the cingulum
across subjects was established, rather than the traditional whole-brain registration. The main merit of our
method compared to existing counterparts is that to find such anatomic correspondence in cingulum, a
scale-invariant parameterization method by arc-angle was proposed. It can give a continuous and exact
description on any segment of cingulum. More interestingly, a significant left-greater-than-right asymmetry
pattern was obtained in most segments of cingulum bundle (50–25 degrees), except in the most
posterior portion of cingulum (25–50 degrees).
Abstract: A local image model is proposed to eliminate the adverse impact of both artificial and inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based image segmentation methods. The estimation and correction procedures for intensity inhomogeneities are no longer indispensable because the highly convoluted spatial distribution of different tissues in the brain is taken into consideration. On the basis of the local image model, multicontext fuzzy clustering (MCFC) is proposed for classifying 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid automatically. In MCFC, multiple clustering contexts are generated for each pixel, and fuzzy clustering is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. To maintain the statistical reliability and spatial continuity of membership distributions, a fusion strategy is adopted to integrate the clustering outcomes from different contexts. The fusion result is taken as the final membership value of the pixel. Experimental results on both real MR images and simulated volumetric MR data show that MCFC outperforms the classic fuzzy c-means (FCM) as well as other egmentation methods that deal with intensity inhomogeneities.
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