Objectives Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. Methods Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain "rsfMRI epilepsy networks." Results SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebellothalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs. Conclusions IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these "rsfMRI epilepsy networks" could reflect epileptogenesis in TLE. Key Points• ICA of resting-state fMRI carries disease-specific information about epilepsy.• Machine learning can classify these components with 97.5% accuracy.• "Subject-specific epilepsy networks" could quantify "epileptogenesis" in vivo.

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Ultrasonography is a noninvasive method in medical field and is generally used for imaging the abnormal tissue growth. The tissue growth can be benign or malignant and to diagnose the quality of the tissue growth based on the stiffness is a challenge. Orthogonal wave velocity is computed by observing the orthogonal wave propagation in determining the stiffness of a tissue in Ultrasound Transient Elasticity. This requires an ultra-fast scanner which works at frame rates more than 1000 fps. The major difficulty is in collecting huge amount of scanner information and process in the processing system. Hence the designs are very complex and costly. Sliding rectangle algorithm is an innovative approach used in extracting the needed information in measuring the orthogonal wave velocity from successive matrix arrays. In this approach, one image matrix array is integrated into multiple rectangles and in a multi matrix array period, only one rectangle is sent and balance rectangles are discarded. This rectangle is moved multi matrix array to multi matrix array. This information is super imposed on full matrix array information. The orthogonal wave speed is calculated rectangle by rectangle. This algorithm reduces the amount of information sent to the processing system. This will enable the information from the scanner to be ported to Laptops in processing through standard interfaces such as USB or Ethernet in DICOM format. This makes the transient elasticity technology viable to be used in tele-medical field applications.

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