神经影像学技术在帕金森病中的应用进展

帕金森病作为常见的神经系统退行性疾病,早期诊治依然存在困难.近几年神经影像学飞速发展,神经影像学技术在帕金森病的诊断和治疗方面的研究成果层出不穷.本文通过挖掘帕金森病神经影像学相关领域的研究趋势和热点,对帕金森病神经影像学近几年的研究成果和临床应用进行阐述.     

Functional modularity of the medial prefrontal cortex: Involvement in human empathy.

To investigate medial frontal lobe mediation of human empathy, the authors analyzed the activation areas in statistical parametric maps of 80 studies reporting neural correlates of empathic processing. The meta-analysis revealed 6 spatially distinct activation clusters in the medial part of the frontal lobe dorsal to the intercommissural plane. The most dorsal cluster coincided with the left supplementary motor area (SMA). Rostrally adjacent was a cluster that overlapped with the right pre-SMA. In addition, there were 3 left-hemispheric and 1 right-hemispheric clusters located at the border between the superior frontal and anterior cingulate gyrus. A broad spectrum of cognitive functions were associated with these clusters, including attention to one's own action, which was related to activations in the SMA, and valuation of other people's behavior and ethical categories, which was related to activations in the most rostroventral cluster. These data complement the consistent observation that lesions of the medial prefrontal cortex interfere with a patient's perception of own bodily state, emotional judgments, and spontaneous behavior. The results of the current meta-analysis suggest the medial prefrontal cortex mediates human empathy by virtue of a number of distinctive processing nodes. In this way, the authors' findings suggest differentiated aspects of self-control of behavior. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Differential brain activation patterns in adult attention-deficit hyperactivity disorder (ADHD) associated with task switching.

Objective: The main aim of the study was to examine blood oxygen level–dependent response during task switching in adults with attention-deficit/hyperactivity disorder (ADHD). Method: Fifteen male adults with ADHD and 14 controls participated and performed a task-switching paradigm. Results: Behaviorally, no specific executive control problems were observed in the ADHD participants, although they did display more errors in general. The neuroimaging data did show remarkable differences between the ADHD and control adults: Adults with ADHD engaged more strongly the dorsal anterior cingulate cortex, middle temporal gyrus, precuneus, lingual gyrus, precentral gyrus, and insula than did the healthy controls during task switching. Controls displayed more task-related activity in the putamen, posterior cingulate gyrus, medial frontal gyrus, thalamus, orbitofrontal cortex, and postcentral gyrus. Conclusions: ADHD adults did not display specific executive control problems at a behavioral level, but did engage different brain areas during task switching compared with healthy controls. The results are discussed in the framework of the executive frontostriatal circuitry, conflict detection, and attentional networks. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

针刺效应的神经影像学研究

介绍了研究针刺机理的神经影像技术方法:fMRI(functional magnetic resonance imaging),PET(positron emission tomography),EEG(electroencephalography)和MEG(magnetoencephalography).介绍了针刺研究中穴位点和对照方法的选取.分别从针刺对于大脑网络、边缘系统和自主神经系统的调节3个方面概述了针刺效应神经生理机制的研究成果.指出了目前的研究所存在的问题,并对未来的研究进行展望.总结和概述了目前针刺效应的神经影像学研究和进展,希望有助于加速针刺机理的研究和认识,早日揭示针刺机理,发扬祖国传统医学.     

Cerebral growth in Fragile X syndrome: review and comparison with Down syndrome

Neuroimaging studies have shown selective changes in brain size in Fragile X syndrome (FraX), which include reductions in the posterior cerebellar vermis, age-dependent increases in hippocampal volume, and enlarged caudate nucleus and thalamus. Contrasting with these limbic and subcortical anomalies, much less is known about the neocortex in FraX. The present study attempted to examine cerebral and lobar-level volumetric changes in young males with FraX (2-7 years), by comparing groups of subjects with full mutation (FM) and mosaicism (Mos) with both age-matched controls and subjects with developmental language delay (DLD) and Down syndrome (DS). For this purpose, we used high resolution (i.e, SPGR) MRI scans and semi-automated methods for segmenting (tissue class) and parcellating (i.e., Talairach) the brain. In agreement with previous studies, we found no changes in overall brain or cerebrum size in FraX. Nevertheless, boys with FM FraX had relative reductions in temporal lobe volume (primarily gray matter) and relative preservation/enlargement of parietal white matter volume. While temporal lobe reductions were not specific, since they were also observed in DLD and DS subjects, parietal preservation/enlargement was only seen in FraX. The relevance of these preliminary findings was emphasized by comparisons between FraX groups, which revealed more marked changes in FM FraX than in Mos FraX (i.e., gene dosage). While cross-sectional analyses revealed marked age-dependent decreases in DS, a group showing marked global and lobar volumetric reductions, there were no changes over time in FraX. These neuroimaging data are discussed in the context of FraX neurobiology and other developmental disorders.     

Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features

Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.     

Alzheimer's detection using various feature extraction approaches using a multimodal multi-class deep learning model

Alzheimer's disease is a chronic brain condition that takes a toll on memory and potential to do even the most basic tasks. With no specific solution viable at this time, it's critical to pinpoint the start of Alzheimer's disease so that necessary steps may be initiated to limit its progression. We used three distinct neuroanatomical computational methodologies namely 3D-Subject, 3D-Patches, and 3D-Slices to construct a multimodal multi-class deep learning model for three class and two class Alzheimer's classification using T1w-MRI and AV-45 PET scans obtained from ADNI database. Further, patches of various sizes were created using the patch-extraction algorithm designed with torch package leading to separate datasets of patch size 32, 40, 48, 56, 64, 72, 80, and 88. In addition, Slices were produced from images using either uniform slicing, subset slicing, or interpolation zoom approaches then joined back to form a 3D image of varying depth (8,16,24,32,40,48,56, and 64) for the Slice-based technique. Using T1w-MRI and AV45-PET scans, our multimodal multi-class Ensembled Volumetric ConvNet framework obtained 93.01% accuracy for AD versus NC versus MCI (highest accuracy achieved using multi-modalities as per our knowledge). The 3D-Subject-based neuroanatomy computation approach achieved 93.01% classification accuracy and it overruled Patch-based approach which achieved 89.55% accuracy and Slice-Based approach that achieved 89.37% accuracy. Using a 3D-Patch-based feature extraction technique, it was discovered that patches of greater size (80, 88) had accuracy over 89%, while medium-sized patches (56, 64, and 72) had accuracy ranging from 83 to 88%, and small-sized patches (32, 40, and 48) had the least accuracy ranging from 57 to 80%. From the three independent algorithms created for 3D-Slice-based neuroanatomy computational approach, the interpolation zoom technique outperformed uniform slicing and subset slicing, obtaining 89.37% accuracy over 88.35% and 82.83%, respectively. Link to GitHub code: https://github.com/ngoenka04/Alzheimer-Detection .