Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Alireza Talesh Jafadideh; Babak Mohammadzadeh Asl
Volume 14, Issue 2 , July 2020, , Pages 143-157
Abstract
Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors. Comparison study between ASD and typically control (TC) subjects through magnetic resonance imaging (MRI) provides valuable understanding ...
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Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors. Comparison study between ASD and typically control (TC) subjects through magnetic resonance imaging (MRI) provides valuable understanding for differences in brain function. Recently, through dynamic functional connectivity (DFC) analysis, it is found that brain functional connectivity possesses dynamic nature and shows transient connectivity patterns (“states”) repeating over time. In this comparison study between ASD and TC, we employed the rest functional MRI (rfMRI) data of San Diego State University (SDSU) of ABIDE II database to examine the brain intra and inter network connectivity and also to investigate the relations of age and social responsiveness scale (SRS) score (score measuring autistic traits) to brain inter regions connectivity strength. These aims were implemented in all DFC states. The ASD subjects experienced more the state with less intra and inter network connections. Further, the DMN segregation reduction from other functional networks emerged as a common them. Furthermore, in ASD, the connection strength between auditory and visual networks was decreased by increasing the age. In ASD, the SRS had more positive relation to connectivity strength existing between cerebellar, auditory, visual networks and cognitive control network in comparison to TC. All these results demonstrate that some differences exist in brain network connection of ASD in comparison to the TC subjects and these differences can be more distinctively revealed by employing DFC analysis.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Alireza Talesh Jafadideh; Babak Mohammadzadeh Asl
Volume 10, Issue 4 , January 2017, , Pages 347-359
Abstract
Minimum variance beamformer (MVB) and its extensions are most widely used techniques in brain source localization due to their high spatial resolution. Unfortunately, beacause of using data covariance matrix, these methods often fail when the number of samples of the recorded data sequences is ...
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Minimum variance beamformer (MVB) and its extensions are most widely used techniques in brain source localization due to their high spatial resolution. Unfortunately, beacause of using data covariance matrix, these methods often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. For solving this problem, Fast Fully Adaptive (FFA) algorithm was developed a few years ago. This method is a multistage adaptive processing technique drawing its inspiration from the butterfly structure of the Fast Fourier Transform (FFT) and decreasing the data requirement significantly. Unfortunately, the high sensitivity of FFA to data partitioning sequences and also its low performance in low SNRs pose a doubt on using it as a reliable localizer for short time brain activities. In this paper, a preprocessing step is proposed to enhance the FFA method. In this step, the brain is divided into separate areas, the components of each area are determined, the data is projected to each area using components of that area. After that, FFA is applied to the projected data. The performance of the enhanced FFA is compared with FFA method by using simulated ERP and real ERF data. In all simulations, enhanced FFA shows the better performance in terms of localization error (enhancement about 2-10 mm) and spread radius (enhancement about 4-9 mm). In addition, the proposed method for real ERF data shows accurate localization result with the most concentrated power spectrum, compared to FFA approach. It is noteworthy that enhanced FFA offers less sensitivity to data partitioning sequences. Emprical results illustrate that enhanced FFA can be implemented as a reliable method for localizing brain short time activities.
