A full-brain, bootstrapped analysis of diffusion tensor imaging robustly differentiates Parkinson disease from healthy controls.

There is a compelling need for early, accurate diagnosis of Parkinson's disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72-0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis.

Altered intrinsic regional activity and corresponding brain pathways reflect the symptom severity of functional dyspepsia.

BACKGROUND: Increasing evidence shows central abnormalities in functional dyspepsia (FD) patients, but whether the symptom severity is directly reflected in altered brain patterns remains unclear. The purpose of this study was to explore how FD affected the resting functional brain patterns for different degrees of symptom severity. METHODS: Functional magnetic resonance imaging was carried out in 40 FD patients and 20 healthy controls. The resting-state brain changes in regional homogeneity (ReHo) and seed correlation analysis were investigated in patients relative to controls. To what degree the brain changes reflected the severity of the disease was assessed by a pattern classification technique. KEY RESULTS: Altered ReHo values (p < 0.05, FDR corrected) were discovered in multiple brain areas in FD patients, and only the anterior cingulate cortex (ACC) and thalamus exhibited significant correlation with the severity of dyspepsia symptoms. Compared with controls, the neural signal changes of the thalamus were not found in the less severe FD patient group but in the relatively more severe group, while the ACC showed aberrations in both groups. Seed-based correlation analysis revealed ACC- and thalamus-related functional connectivity differences between FD patients and controls at a voxel-wise level, and the altered thalamic circuits provided the best performance in distinguishing FD patients with different levels of symptom severity. CONCLUSIONS & INFERENCES: Our results indicated that the functional abnormalities of the ACC and thalamus may occur at different clinical courses in FD. This may help us better understand the progression of FD.

Alterations of the default mode network in functional dyspepsia patients: a resting-state fmri study.

BACKGROUND: Increasing brain imaging studies have emphasized the role of regional brain activity abnormalities in functional dyspepsia (FD) during the resting state. The goal of this study was to investigate the default mode network (DMN) in FD patients and healthy controls (HCs). METHODS: Resting-state functional magnetic resonance imaging (fMRI) scanning was carried out on 49 patients and 39 HCs. Independent component analysis (ICA) was used to isolate the DMN in each subject. Group topography of the DMN was compared to study significant alteration in FD. A correlation analysis was then performed in the FD group to investigate the effects of symptom severity and the psychological factors on the DMN. KEY RESULTS: Significant spatial differences with the DMN in FD patients, compared with HCs, were mainly found in the dorsomedial prefrontal cortex (dmPFC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), pregenual anterior cingulate cortex (pACC), thalamus, parahippocampal gyrus, precuneus, parietal cortex, and temporal pole. Meanwhile, Nepean Dyspepsia Index (NDI) scores were positively correlated with the pACC, and was negative correlated with the OFC. However, both the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores were not correlated with any regions of interest showing differences between the FD patients and the HCs. CONCLUSIONS & INFERENCES: These findings suggested that the DMN might indeed undergo dysfunctional changes due to the abnormal persistent activity in FD patients. To a certain extent, the changes in the DMN were related to the FD-related symptom severity.

Reliability analysis of the resting state can sensitively and specifically identify the presence of Parkinson disease.

Parkinson disease (PD) is characterized by a number of motor and behavioral abnormalities that could be considered deficits of a "no task" or "resting" state, including resting motor findings and defects in emerging from a resting state (e.g., resting tremor, elevated resting tone, abulia, akinesia, apathy). PET imaging, and recently, the MRI technique of continuous arterial spin labeling (CASL) have shown evidence of changes in metabolic patterns in individuals with PD. The purpose of this study was to learn if the presence of PD could be "predicted" based on resting fluctuations of the BOLD signal. Participants were 15 healthy controls, 14 subjects with PD, and 1 subject who presented as a control but later developed PD. The amplitude of the low frequency fluctuation (ALFF) was used as an index of brain activity level in the resting state. Participants with PD using this index showed a reliable decrease in activity in a number of regions, including the supplementary motor cortex, the mesial prefrontal cortex, the right middle frontal gyrus, and the left cerebellum (lobule VII/VIII) as well as increased activity in the right cerebellum (lobule IV/V). Using a cross validation approach we term "Reliability Mapping of Regional Differences" (RMRD) to analyze our sample, we were able to reliably distinguish participants with PD from controls with 92% sensitivity and 87% specificity. Our "pre-diagnostic" subject segregated in our analysis with the PD group. These results suggest that resting fMRI should be considered for development as a biomarker and analytical tool for evaluation of PD.

Interindividual reaction time variability is related to resting-state network topology: an electroencephalogram study.

Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.