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Piu Chan

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8 papers

1 author row

JBHI Journal 2021 Journal Article

Striatal Subdivisions Estimated via Deep Embedded Clustering With Application to Parkinson's Disease

Yu Li
Aiping Liu
Taomian Mi
Runyu Yang
Piu Chan
Martin J. McKeown
Xun Chen
Feng Wu

Recent fMRI connectivity-based parcellation (CBP) methods have been developed to obtain homogeneous and functionally coherent brain parcels. However, most of these studies utilize traditional clustering methods that neglect hidden nonlinear features. To enhance parcellation performance, here we propose a deep embedded connectivity-based parcellation (DECBP) framework and apply it to determine functional subdivisions of the striatum in public resting state fMRI data sets. This framework integrates fMRI connectivity features into deep embedded clustering (DEC), a deep neural network based on a stacked autoencoder. Compared to three prevalent clustering methods and their combinations with principal component analysis (PCA), the DECBP exhibited a significantly higher similarity between scans, individuals, and groups, indicating enhanced reproducibility. The generated reliable parcellations were also largely consistent with other public atlases. We further explored the functional subunits in the striatum in a data set from 23 Parkinson's disease (PD) subjects and 27 age-matched healthy controls (HC). All putaminal subregions of PD demonstrated lower interhemispheric connectivity than those of HC, which might reflect imbalance in the pathological progression of PD. Such hypo-connectivity was also observed between putaminal subregions and other brain regions, reflecting neuroimaging manifestations of the altered cortico-striato-thalamo-cortical circuit. These observed weaker couplings were associated with PD severity and duration. Our results support the utilization of the DECBP framework and suggest that abnormal connectivity in putaminal subregions may be a potential indicator of PD.

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YNICL Journal 2019 Journal Article

Connectome-wide network analysis of white matter connectivity in Alzheimer's disease

Chenfei Ye
Susumu Mori
Piu Chan
Ting Ma

A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients.

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YNICL Journal 2018 Journal Article

Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation

Aiping Liu
Sue-Jin Lin
Taomian Mi
Xun Chen
Piu Chan
Z. Jane Wang
Martin J. McKeown

Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences. HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features. Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders.

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YNIMG Journal 2013 Journal Article

Cerebellum and integration of neural networks in dual-task processing

Tao Wu
Jun Liu
Mark Hallett
Zheng Zheng
Piu Chan

Performing two tasks simultaneously (dual-task) is common in human daily life. The neural correlates of dual-task processing remain unclear. In the current study, we used a dual motor and counting task with functional MRI (fMRI) to determine whether there are any areas additionally activated for dual-task performance. Moreover, we investigated the functional connectivity of these added activated areas, as well as the training effect on brain activity and connectivity. We found that the right cerebellar vermis, left lobule V of the cerebellar anterior lobe and precuneus are additionally activated for this type of dual-tasking. These cerebellar regions had functional connectivity with extensive motor- and cognitive-related regions. Dual-task training induced less activation in several areas, but increased the functional connectivity between these cerebellar regions and numbers of motor- and cognitive-related areas. Our findings demonstrate that some regions within the cerebellum can be additionally activated with dual-task performance. Their role in dual motor and cognitive task processes is likely to integrate motor and cognitive networks, and may be involved in adjusting these networks to be more efficient in order to perform dual-tasking properly. The connectivity of the precuneus differs from the cerebellar regions. A possible role of the precuneus in dual-tasks may be to monitor the operation of active brain networks.

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YNIMG Journal 2011 Journal Article

Effective connectivity of brain networks during self-initiated movement in Parkinson's disease

Tao Wu
Liang Wang
Mark Hallett
Yi Chen
Kuncheng Li
Piu Chan

Patients with Parkinson's disease (PD) have difficulty in performing self-initiated movements. The neural mechanism of this deficiency remains unclear. In the current study, we used functional MRI (fMRI) and psychophysiological interaction (PPI) methods to investigate the changes in effective connectivity of the brain networks during performance of self-initiated movement in PD patients. Effective connectivity is defined as the influence one neuronal system exerts over another. fMRIs were acquired in 18 PD patients and in 18 age- and sex-matched healthy controls, when performing a self-initiated right hand tapping task. We chose the left primary motor cortex (M1), rostral supplementary motor area (pre-SMA), left premotor cortex (PMC), left putamen, and right cerebellum as index areas for PPI analysis. During the performance of self-initiated movement, connectivity between the putamen and M1, PMC, SMA, and cerebellum was decreased in PD patients compared to controls. In contrast, connections between the M1, pre-SMA, PMC, parietal cortex, and cerebellum were increased in PD patients compared to controls. In addition, the M1, pre-SMA, PMC, and cerebellum also had less connectivity with the dorsal lateral prefrontal cortex in PD. In PD patients, the effective connectivity between the putamen and M1, PMC, SMA, and cerebellum negatively correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) motor scores; whereas the connectivity between the M1, pre-SMA, PMC, and cerebellum positively correlated with the UPDRS motor scores. Our findings demonstrate that the pattern of interactions of brain networks is disrupted in PD during performance of self-initiated movements. The striatum-cortical and striatum-cerebellar connections are weakened. In contrast, the connections between cortico-cerebellar motor regions are strengthened and may compensate for basal ganglia dysfunction. These altered interregional connections are more deviant when the disorder is more severe, and, therefore, our results give further insight into the explanation for the difficulty in performing self-initiated movements in PD.

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YNIMG Journal 2010 Journal Article

Effective connectivity of neural networks in automatic movements in Parkinson's disease

Tao Wu
Piu Chan
Mark Hallett

Patients with Parkinson's disease (PD) have difficulty in performing learned movements automatically. The neural mechanism of this deficiency remains unclear. In the current study, we used functional MRI (fMRI) and psychophysiological interaction (PPI) methods to investigate the changes in effective connectivity of the brain networks when movements become automatic in PD patients and age-matched normal controls. We found that during automaticity, the rostral supplementary motor area, cerebellum, and cingulate motor area had increased effective connectivity with brain networks in PD patients. In controls, in addition to these regions, the putamen also had automaticity-related strengthened interactions with brain networks. The dorsal lateral prefrontal cortex had more connectivity at the novel stage than in the automatic stage in normal subjects, but not in PD patients. The comparison of the PPI results between the groups showed that the rostral supplementary motor area, cerebellum, and cingulate motor area had significantly more increased effective connectivity with several regions in normal subjects than in PD. The changes of effective connectivity in some areas negatively correlated with the Unified Parkinson's Disease Rating Scale (UPDRS). Our findings show that some of the factors related to PD patients having difficulty achieving automaticity are less efficient neural coding of movement and failure to shift execution of automatic movements more subcortically. The changes of effective connectivity become more abnormal as the disorder progresses. In addition, in PD, the connections of the attentional networks are altered.

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YNIMG Journal 2006 Journal Article

Discriminative analysis of relapsing neuromyelitis optica and relapsing–remitting multiple sclerosis based on two-dimensional histogram from diffusion tensor imaging

Fuchun Lin
Chunshui Yu
Tianzi Jiang
Kuncheng Li
Chaozhe Zhu
Wanlin Zhu
Wen Qin
Yunyun Duan

It is difficult to completely differentiate patients with relapsing neuromyelitis optica (RNMO) from relapsing–remitting multiple sclerosis (RRMS) for their similarities in clinical manifestation. In this study, we proposed a novel approach, using two-dimensional histogram of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the brain derived from diffusion tensor imaging (DTI) as classification feature, to discriminate patients with RNMO from RRMS. In this approach, two-dimensional principal component analysis (2D-PCA) was used to extract feature and reduce dimensionality of matrix-formed data efficiently. Then linear discriminant analysis (LDA) was performed on these extracted features to find the best projection direction to separate patients with RNMO from RRMS. Finally, a minimum distance classifier was generated on the basis of projection scores. The correct recognition rate of our method reached 85. 7%, validated by the leave-one-out method. This result was much higher than that using feature of ADC or FA separately (59. 5% for ADC, 76. 2% for FA). In conclusion, the proposed method on the basis of combined features is more effective for classification than those merely using the features separately, and it may be helpful in differentiating RNMO from RRMS patients.

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YNIMG Journal 2006 Journal Article

Quantitative analysis along the pyramidal tract by length-normalized parameterization based on diffusion tensor tractography: Application to patients with relapsing neuromyelitis optica

Fuchun Lin
Chunshui Yu
Tianzi Jiang
Kuncheng Li
Xiaobo Li
Wen Qin
Hong Sun
Piu Chan

In this study, we introduced a length-normalized parameterization method to establish anatomical correspondence of white matter fiber tracts across subjects and applied this method to investigate the presence of abnormal diffusion along the pyramidal tract (PYT) of relapsing neuromyelitis optica (RNMO) patients without visible brain lesions. In this approach, the part of the PYT between the lowest slice of the cerebral peduncle and the uppermost slice of the lateral ventricle was reconstructed to establish the anatomical correspondence across subjects using diffusion tensor tractography. Then it was parameterized by normalizing its length and dividing equally the normalized length into a certain number of segments, so that the comparability of each segment across subjects along the PYT was established. Tract-specific diffusion indices, including directionally averaged diffusivity (D av), fractional anisotropy (FA), primary diffusivity (λ 1) and transverse diffusivity (λ 23), were obtained from each segment. Thus, the distribution maps of these indices along the PYT were obtained. The distribution maps of D av, FA, and λ 23 of RNMO patients were significantly different from those of healthy controls, especially in the lower part of the PYT. The differences may be caused by secondary degeneration to lesions in the spinal cord. In conclusion, a length-normalized parameterization method is proposed to establish anatomical correspondence for the PYT. Compared with existed methods, a major merit of our method is to provide comparability across subjects along the PYT on the basis of diffusion tensor tractography and to make it possible for the quantitative analysis along the fiber tract. This method can also be used to quantitatively analyze other white matter fiber tracts between two definite anatomic landmarks in many neurological or psychiatric diseases.

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