Effects of white matter hyperintensity on cognitive function in PD patients: a meta-analysis

BackgroundParkinson’s disease (PD) is often accompanied by cognitive dysfunction, which imposes a heavy burden on patients, their families, and society. Early identification and intervention are particularly important, but reliable biomarkers for identifying PD-related cognitive impairment at an early stage are currently lacking. Although numerous clinical studies have investigated the association between brain white matter hyperintensity (WMH) and cognitive decline, the findings regarding the relationships between WMH and cognitive dysfunction in PD patients have been inconsistent. Therefore, this study aims to conduct a meta-analysis of the effect of WMH on PD cognitive function.MethodsThis study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines. We systematically searched relevant literature from databases such as PubMed, Web of Science, EMBASE, CNKI, and CBM. The retrieval time was limited to database records created up until December 31, 2022. Additionally, we manually retrieved references for full-text reading. Statistical data analysis was performed using RevMan 5.3 and Stata 15.0 software.ResultsThis study encompassed 23 individual studies and involved 2,429 patients with PD. The group of PD with mild cognitive impairment (PD-MCI) exhibited a significantly higher overall level of WMH than the group of PD with normal cognitive function (PD-NC) (SMD = 0.37, 95% CI: 0.21–0.52, p < 0.01). This finding was consistent across subgroup analyses based on different ethnicities (Asian or Caucasian), WMH assessment methods (visual rating scale or volumetry), and age matching. In addition to the overall differences in WMH load between the PD-MCI and PD-NC groups, the study found that specific brain regions, including periventricular white matter hyperintensity (PVH) and deep white matter hyperintensity (DWMH), had significantly higher WMH load in the PD-MCI group compared to the PD-NC group. The study also conducted a meta-analysis of WMH load data for PD with dementia (PDD) and PD without dementia (PDND), revealing that the overall WMH load in the PDD group was significantly higher than that in the PDND group (SMD = 0.98, 95% CI: 0.56–1.41, p < 0.01). This finding was consistent across subgroup analyses based on different ethnicities and age matching. Moreover, regarding specific brain regions (PVH or DWMH), the study found that the PDD group had significantly higher WMH load than the PDND group (p < 0.01).ConclusionWMH was associated with PD cognitive dysfunction. The early appearance of WMH may indicate PD with MCI

Temporally and Spatially Constrained ICA of fMRI Data Analysis

<div><p>Constrained independent component analysis (CICA) is capable of eliminating the order ambiguity that is found in the standard ICA and extracting the desired independent components by incorporating prior information into the ICA contrast function. However, the current CICA method produces constraints that are based on only one type of prior information (temporal/spatial), which may increase the dependency of CICA on the accuracy of the prior information. To improve the robustness of CICA and to reduce the impact of the accuracy of prior information on CICA, we proposed a temporally and spatially constrained ICA (TSCICA) method that incorporated two types of prior information, both temporal and spatial, as constraints in the ICA. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects who performed a movement task. Additionally, the performance of TSCICA was compared with the ICA method, the temporally CICA (TCICA) method and the spatially CICA (SCICA) method. The results from the simulation and from the real fMRI data demonstrated that TSCICA outperformed TCICA, SCICA and ICA in terms of robustness to noise. Moreover, the TSCICA method displayed better robustness to prior temporal/spatial information than the TCICA/SCICA method.</p></div

A comparison of TSCICA, TCICA, SCICA and FastICA.

<p>(A) The spatial correlation coefficient of TSCICA1, TCICA, SCICA1 and FastICA for an individual subject. (B) The spatial correlation coefficient of TSCICA2, TCICA, SCICA2 and FastICA for an individual subject. (C) The mean spatial correlation coefficients of TSCICA1, TCICA, SCICA1 and FastICA. (D) The mean spatial correlation coefficients of TSCICA2, TCICA, SCICA2 and FastICA. The error bar represents the standard deviation. TSCICA1/SCICA1 represents TSCICA/SCICA using the first spatial reference. TSCICA2/SCICA2 represents TSCICA/SCICA using the second spatial reference. The error bar represents the standard deviation. Note: asterisk represents P<0.01.</p

Mean cluster quality index of all the ICA methods.

<p>The error bar represents the standard deviation. Note: asterisk represents P<0.01.</p

Results of the nonparametric Wilcoxon tests for paired samples in CNR condition.

<p>Note:asterisk represents P<0.01.</p

Results of the nonparametric Wilcoxon tests for paired samples in errorrate condition.

<p>Note:asterisk represents P<0.01.</p

The spatial activation that was estimated by all of the methods.

<p>(A) The activated regions that were detected by GLM. (B) The activated regions of the target component that were estimated by TSCICA using the first spatial reference. (C) The activated regions of the target component that were estimated by SCICA using the first spatial reference. (D) The activated regions of the target component that were estimated by TCICA. (E) The activated regions of the target component that were estimated by TSCICA using the second spatial reference. (F) The activated regions of the target component that were estimated by SCICA using the second spatial reference. (G) The activated regions of the target component that were estimated by FastICA.</p

Results of the comparison of TSCICA and GLM.

<p>(A) The spatial activation map of the target component that was extracted by TSCICA. (B) The spatial activation that was detected by GLM. (C) The temporal reference (dotted line) and the time course (solid line) that correspond to the target component that was extracted by TSCICA. CC is the correlation coefficient between the estimated time course and the temporal reference.</p

Results of the nonparametric Wilcoxon tests for paired samples in overlaprate condition.

<p>Note:asterisk represents P<0.01.</p

Pre-defined regions and the simulated fMRI response.

<p>(A) The predefined ROI for the simulated datasets including one task-related component. (B) One spatial reference with a 10% overlap rate applied to the simulated datasets including one task-related component. (C) The predefined ROI1 and ROI2 of the simulated datasets including two task-related components. (D) The simulated fMRI response added to the two ROIs. Solid line corresponds to the time course added to ROI1 and dotted line corresponds to the time course that was added to ROI2. (E) The spatial reference that was applied to the simulated datasets, including two task-related components. (F) The temporal reference that was applied to the simulated datasets, including two task-related components.</p