Functional magnetic resonance imaging with encoding task in patients with mild cognitive impairment and different severity of leukoaraiosis

Abnormal brain activities of cognitive processes in cerebral small vessel disease: A systematic review of task fMRI studies

Cerebral small vessel disease (CSVD) is characterized by widespread functional changes in the brain, as evident from abnormal brain activations during cognitive tasks. However, the existing findings in this area are not yet conclusive. We systematically reviewed 25 studies reporting task-related fMRI in five cognitive domains in CSVD, namely executive function, working memory, processing speed, motor, and affective processing. The findings highlighted: (1) CSVD affects cognitive processes in a domain-specific manner; (2) Compensatory and regulatory effects were observed simultaneously in CSVD, which may reflect the interplay between the negative impact of brain lesion and the positive impact of cognitive reserve. Combined with behavioral and functional findings in CSVD, we proposed an integrated model to illustrate the relationship between altered activations and behavioral performance in different stages of CSVD: functional brain changes may precede and be more sensitive than behavioral impairments in the early pre-symptomatic stage; Meanwhile, compensatory and regulatory mechanisms often occur in the early stages of the disease, while dysfunction/decompensation and dysregulation often occur in the late stages. Overall, abnormal hyper-/hypo-activations are crucial for understanding the mechanisms of small vessel lesion-induced behavioral dysfunction, identifying potential neuromarker and developing interventions to mitigate the impact of CSVD on cognitive function.

Random-Forest-Algorithm-Based Applications of the Basic Characteristics and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment

Background

Mild cognitive impairment (MCI) is considered the early stage of Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics and serum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate approach.

Methods

The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum biomarkers were detected as preprandial blood glucose, triglyceride, total cholesterol, Aβ1-40, Aβ1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by automatic anatomical labeling. The basic characteristics included gender, education level, and previous disease history of hypertension, diabetes, and coronary heart disease. The data were randomly divided into training sets and test ones. The recursive random forest algorithm was applied to the diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity, and specificity were calculated, respectively, and so were the ROC curve and the area under the curve (AUC) of the test set.

Results

When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was 78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the specificity was 100%. RFE analysis showed that age, Aβ1-40, and cerebellum_4_6 were the most important basic feature, serum biomarker, imaging biomarker, respectively.

Conclusion

Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the specificity of 100% in our model. As a machine learning method, a random forest can help diagnose MCI effectively while screening important influencing factors.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Neurologic Factors in Patients with Vascular Mild Cognitive Impairment Based on fMRI

This study focused on the application of functional magnetic resonance imaging and neuropsychology in diagnosis of vascular mild cognitive impairment (MCI) and the exploration of its relevant factors. The study enrolled 28 patients with vascular MCI in an observation group and 30 healthy individuals in a control group. All patients underwent magnetic resonance imaging. An automatic segmentation algorithm based on graph theory was adopted to process the images. Age, sex, disease course, Montreal Cognitive Assessment score, regional homogeneity, and amplitude of low-frequency fluctuation levels were recorded. There were no significant differences in age, gender, and course of disease between the observation group and the control group (P > 0.05). The level of regional homogeneity in the left posterior cerebellum in the observation group was significantly higher than that in the control group (P < 0.05).The regional homogeneity level of bilateral cingulate cortex was negatively correlated with Montreal Cognitive Assessment score (P < 0.05). The amplitude of low-frequency fluctuation of bilateral inferior parietal lobe, parietal lobe, and prefrontal lobe in the observation group was significantly lower than that in the control group, and the amplitude of low-frequency fluctuation of bilateral anterior cingulate gyrus, superior medial frontal gyrus, orbital frontal gyrus, right middle frontal gyrus, and right auxiliary motor area was higher than that in the control group (P < 0.05). Heart disease, such as myocardial infarction and atrial fibrillation, is a high risk factor for vascular MCI. Functional magnetic resonance imaging combined with an automatic segmentation algorithm can noninvasively observe the changes of a patient's brain tissue, which can be used in the recognition of vascular MCI. The global network attributes of patients with depression tend to be more randomized and have stronger resilience under targeted attacks.