Department of Information Technology, Centre for Healthcare Technologies, Sri Sirasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai (OMR), Chennai, India.
Department of Pediatrics, Sri Ramachandra Institute of Higher Education and Research, Sri Ramachandra Medical University, Chennai, India.
Curr Med Imaging. 2020;16(9):1059-1073. doi: 10.2174/1573405615666191111142039.
The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research.
This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks.
The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA.
Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group.
Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.
自闭症谱系包括高功能自闭症（HFA）和低功能自闭症（LFA）。脑映射研究表明，自闭症患者的大脑行为特征存在重叠。一般来说，高功能个体被认为具有更高的智力和更好的语言处理能力。然而，与他们的功能能力相关的具体机制仍在研究中。
本研究通过功能连接模式以及脑连接参数来研究自闭症谱系中的重叠现象，并使用深度置信网络对这些类别进行区分。
从 ABIDE 数据库中获取了高功能和低功能自闭症组的任务型功能磁共振成像（fMRI）数据，其中低功能组有 58 人，高功能组有 43 人，他们在进行语言处理任务时参与了研究。分析了大脑的语言处理区域和默认模式网络（DMN）。通过图论程序绘制了功能连接图。计算了个体组的脑连接参数，如格兰杰因果关系（GC）和相位斜率指数（PSI）。将这些参数输入深度置信网络（DBN），以将考虑中的受试者分类为 LFA 或 HFA。
结果显示高功能组的功能连接增强。研究发现，位于颞叶的初级听觉皮层与其他感兴趣区域的额外相互作用，补充了他们增强的连接。使用 DBN 验证了结果，高功能组的分类准确率为 85.85%，低功能组的分类准确率为 81.71%。
由于众所周知，自闭症涉及到智力的增强但不平衡的成分，因此高功能组在语言处理方面的优势以及增强连接的责任区域的原因已经得到了认可。因此，本研究表明初级听觉皮层在高功能年轻成年人语言处理主导作用中的作用，对于区分自闭症谱系中的不同群体具有重要意义。
