Machine learning based on functional and structural connectivity in mild cognitive impairment.

OBJECTIVE: Alzheimer's disease (AD) is a chronic, degenerative neurological disorder characterized by progressive cognitive decline and mental behavioral abnormalities. Mild cognitive impairment (MCI) is regarded as a transitional stage in the progression from normal elderly individuals to patients with AD. While studies have identified abnormalities in brain connectivity in patients with MCI, including functional and structural connectivity, accurately identifying patients with MCI in clinical screening remains challenging. We hypothesized that utilizing machine learning (ML) based on both functional and structural connectivity could yield meaningful results in distinguishing between patients with MCI and normal elderly individuals, so as to provide valuable information for early diagnosis and precise evaluation of patients with MCI. METHODS: Following clinical criteria, we recruited 32 patients with MCI for the patient group, and 32 normal elderly individuals for the control group. All subjects underwent examinations for resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). Subsequently, significant functional and structural connectivity features were selected and combined with a support vector machine for classification of the patient and control groups. RESULTS: We observed significantly different functional connectivity in the frontal lobe and putamen between the MCI group and normal controls. The results based on functional connectivity features demonstrated a classification accuracy of 71.88% and an area under the curve (AUC) value of 0.78. In terms of structural connectivity, we found that decreased fractional anisotropy in patients with MCI was significantly associated with Montreal Cognitive Assessment scores, specifically in regions such as the precuneus and cingulate gyrus. The classification results using the structural connectivity feature yielded an accuracy of 92.19% and an AUC value of 0.99. Lastly, combining functional and structural connectivity features resulted in a classification accuracy and AUC value of 93.75% and 0.99, respectively. CONCLUSIONS: In this study, we demonstrated a high classification performance, underscoring the potential of both brain functional and structural connectivity in distinguishing patients with MCI from normal elderly individuals. Furthermore, the integration of functional connectivity and structural connectivity features indicated that utilizing rs-fMRI and DTI could enhance the accuracy and specificity of identifying patients with MCI compared with relying on a single neuroimaging technique.

Alterations of Cerebral Blood Flow and its Connectivity Patterns Measured with Arterial Spin Labeling in Mild Cognitive Impairment.

OBJECTIVES: Cerebral blood flow (CBF) is an important index for measuring brain function. Studies have shown that regional CBF changes inconsistently in mild cognitive impairment (MCI). Arterial spin labeling (ASL) is widely used in the study of CBF in patients with MCI. However, alterations in CBF connectivity in these patients remain poorly understood. METHODS: In this study, 3D pseudo-continuous arterial spin labeling (3D-pCASL) technology was used to investigate the changes in regional CBF and CBF connectivity between 32 MCI patients and 32 healthy controls. The normalized CBF was used to reduce inter-subject variations. Both group comparisons in the CBF and correlations between CBF alterations and cognitive scores were assessed. CBF connectivity of brain regions with regional CBF differences was also compared between groups. RESULTS: We found that compared with that in controls, the CBF was significantly reduced in the left superior parietal gyrus in MCI patients, whereas it was increased in the left precentral gyrus, right superior temporal gyrus, right putamen, and left supplementary motor area. In patients with MCI, significant correlations were identified between CBF and neuropsychological scales. Importantly, MCI patients exhibited CBF disconnections between the left supplementary motor area and the left superior parietal gyrus. CONCLUSION: This study found that there are not only changes in regional CBF but also in CBF connectivity patterns in MCI patients compared with controls. These observations may provide a novel explanation for the neural mechanism underlying the pathophysiology in patients with Alzheimer's disease and MCI.

Magnetic resonance imaging characteristics of brain metastases in small cell lung cancer.

BACKGROUND: Lung is the most common primary site of brain metastases (BMs). For different pathological types of BMs have some similar characteristics, it is still a challenge to confirm the origin based on their characteristics directly. BMs of small cell lung cancer (SCLC) have favorable therapeutic expectations due to their high sensitivity to radiotherapy. This study sought to identify unique characteristics of BMs in SCLC, aiming to assist in clinical decision-making. METHODS: Patients diagnosed with BMs of lung cancer who received radiotherapy from January 2017 to January 2022 were reviewed (N = 284). Definitive diagnosis of BMs of SCLC was reached for 36 patients. All patients underwent head examination using magnetic resonance imaging. The number, size, location, and signal characteristics of lesions were analyzed. RESULTS: There were 7 and 29 patients with single focus and non-single focus, respectively. Ten patients had diffuse lesions, and the remaining 26 patients had a total of 90 lesions. These lesions were divided into three groups according to size: <1, 1-3, and >3 cm (43.33%, 53.34%, and 3.33%, respectively). Sixty-six lesions were located in the supratentorial area, primarily including cortical and subcortical lesions (55.56%) and deep brain lesions (20%). Moreover, 22 lesions were located in the infratentorial area. According to diffusion-weighted imaging and T1-weighted contrast enhancement, the imaging characteristics were classified into six patterns. Hyperintensity in diffusion-weighted imaging and homogeneous enhancement was the most common pattern of BMs in SCLC (46.67%), while partial lesions showed hyperintensity in diffusion-weighted imaging without enhancement (7.78%). CONCLUSIONS: The manifestations of BMs in SCLC were multiple lesions (diameter: 1-3 cm), hyperintensity in diffusion-weighted imaging, and homogeneous enhancement. Interestingly, hyperintensity in diffusion-weighted imaging without enhancement was also one of the characteristics.

Pediatric dumbbell-shaped orbital schwannoma with extension to the cranial cavity: A case report and literature review.

Orbital schwannomas are rare in children, especially those with intracranial extension. Herein, our report refers to a 12-year-old boy who had a cranial-orbital mass with a dumbbell-like appearance. The total neoplasms was successfully removed via a transcranial approach, and the pathological diagnostic result was schwannoma. Neither radiotherapy nor chemotherapy was performed after surgery, and no recurrences were observed for 3 months. Our report suggests that orbital schwannomas should be differentiated from other types of orbital tumors with sufficient evidence and that complete surgical resection remains the first choice to cure this disease.

Diffusion Tensor Imaging Study of Olfactory Identification Deficit in Patients With Mild Cognitive Impairment.

Objective: To explore the relationship between white matter changes and olfactory ability among patients with mild cognitive impairment (MCI) and to develop a tool to predict the development of Alzheimer's disease among patients with MCI. Methods: The Montreal Cognitive Assessment (MoCA) was used for cognitive assessments, and the 70% isopropanol test paper was used to evaluate olfactory function. Tract-based spatial statistics, based on the diffusion tensor imaging technology, were used to obtain relevant parameters, and behavioral and imaging results were compared between patients with MCI (n = 36) and healthy older adults (n = 32). Results: The olfactory ability of MCI patients was lower overall, which was positively correlated with the MoCA score. Fractional anisotropy (FA) changes significantly of all parameters. Lower FA regions were mainly located in the corpus callosum, the orbitofrontal gyrus, and the left occipital lobe. The olfactory score was significantly correlated with the FA value of the orbitofrontal gyrus. Fibrous connections in several brain regions, such as the entorhinal cortex, were stronger in patients with MCI. Conclusion: The olfactory ability of MCI patients in our group was positively correlated with the neuropsychological scale results. Impairment in olfactory function was superior to memory deficits for predicting cognitive decline among cognitively intact participants. The fibrous connections in several brain regions, such as the entorhinal cortex, were higher in patients with MCI, which suggested that there may be a compensatory mechanism in the olfactory pathway in MCI patients. The decline in olfactory function may be a significant and useful indicator of neuropathological changes in MCI patients and an effective marker for the development of cognitive decline and dementia.