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The degradation in visual quality of images is often seen due to a variety of noise added inevitably at the time of image acquisition. Its restoration has thus become a fundamental and significant problem in image processing. Many attempts are made in recent past to efficiently denoise images. But, the best possible solution to this problem is still an open research problem. This paper validates the effectiveness of one such popular image denoising approach, where an adaptive image patch clustering is followed by the two step denoising algorithm in Principal Component Analysis (PCA) domain. First step uses Marchenko--Pastur law based hard thresholding of singular values in the singular value decomposition (SVD) domain and the second step removes remaining noise in PCA domain using Linear Minimum Mean-Squared-Error (LMMSE), a soft thresholding. The experimentation is conducted on gray-scale images corrupted by four different noise types namely speckle, salt pepper, Gaussian, and Poisson. The efficiency of image denoising is quantified in terms of popular image quality metrics peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and the denoising time. The comprehensive performance analysis of the denoising approach against the four noise models underlies its suitability to various applications. This certainly gives the new researchers a direction for selection of image denoising method. [ABSTRACT FROM AUTHOR]

Tremendous development in Internet of Things (IoT) and mobile devices lead to several images pooled on social media websites and communicated through networking channels. These images are mostly corrupted with impulse noises due to hot pixels generated in the camera sensors and communication channels. Adaptive mean filter technique removes impulse noise at low density but is unsuccessful as noise density increases and computationally expensive. In this paper, automatic adaptive filtering technique for removal of impulse (salt and pepper) noise is demonstrated. The proposed algorithm consists of impulse noise detection and noise removal modules. An automatic impulse noise detection module is based on mean and variance technique that selects the noisy pixels among the entire image. The noise removal module is based on replacement of noisy pixel through mean and edge direction using Gabor filter. The proposed technique demonstrated better robustness compared with existing techniques. [ABSTRACT FROM AUTHOR]

Background: The Hachinski ischemic score (HIS) scale is used to exclude as much as possible, individuals with dominant cerebrovascular pathology from those with dominant AD pathology. White matter hyperintensities (WMH) are the gold‐standard for evaluating cerebrovascular pathology and linked to worsening cognitive impairment. HIS ≤ 4 are used to minimize non‐AD pathology in ADNI participants. Here we sought to understand whether HIS scores for hypertension (HMHYPERT) in ADNI3 were associated with greater WMH burden (and hence cerebrovascular pathology) even within individuals with HIS ≤ 4. Method: We compared HMHYPERT scores between individuals with normal cognition (CN) and with mild cognitive impairment (MCI), and participants and correlated with percent WMH burden. A total of 263 subjects screened for ADNI3 (92 MCI [38 F], age = 73 ± 6; 146 CN [94 F], age = 74 ± 7) who had HIS scores and FLAIR MRI acquisitions within a year of each other. 25 All AD participants were excluded. WMH burden was calculated by segmenting them using the Lesion Segmentation Toolbox and normalizing their volume by total intracranial volume. We used the HMHYPERT as a vascular risk factor, excluding those with any focal neurological symptoms. For analysis, we dichotomized participants into those with HMHYPERT = 0 and HMHYPERT>0. To examine whether a non‐zero HMHYPERT score corresponds to greater WMH burden, we examined the prevalence of WMH in between these 2 groups and within the MCI and CN groups. Result: Although not significant, HMHYPERT scores were higher in MCI. (Fig. 1A, MCI = 0.48±0.50, CN = 0.39±0.49). We found a greater proportion of MCI subjects had HMHYPERT>0 than CN subjects (Fig. 1B, HMHYPERT = 0: CN = 61.0% and MCI = 52.2%; HMHYPERT > 0: CN = 40.0% and MCI = 48.1%). WMH burden was greater in MCI (0.33±0.53) than CN (0.20±0.32) (Fig. 2A). MCI participants with HMHYPERT>0 had the highest WMH burden followed by MCI participants with HMHYPERT = 0, CN with HMHYPERT >0, and CN with HMHYPERT = 0 had the lowest WMH burden. (Fig. 2B). Conclusion: Cerebrovascular pathology increases significantly with diagnosis, but its severity increases further with findings of hypertension even when total HIS ≤ 4. [ABSTRACT FROM AUTHOR]

Background: The Hachinski Ischemia Scale (HIS) differentiates AD and vascular dementia and is used to include individuals with minimal vascular risk factors in ADNI i.e., HIS ≤ 4. HIS score includes history of hypertension (HMHYPERT), a known risk for mild cognitive impairment (MCI) and vascular dementia. Cerebrovascular disease affects performance on tasks involving executive function, such as, planning and processing speed more than memory. Here, we determined if HMHYPERT scores influences executive functioning in all ADNI3 participants with normal cognition (CN) and MCI even with minimal vascular risks. Method: We included a total of 272 participants (102 MCI, age = 74± 8 [42F, 50 HMHYPERT>0], MoCA = 23 ±3 and 170 CN, age = 73 ±6, [109F, 62 HMHYPERT>0], MoCA = 26 ±3), who underwent HIS, Trails Making Test B, Clock Drawing and Copy scores for executive function, and The Rey Auditory Verbal Learning Test (RAVLT) and Logical Memory Test (LOGMEM) scores for memory performance within 12 months. We converted both memory tests into z‐scores and created a composite memory score. Similarly, we created a composite executive function score. To determine if HMHYPERT was associated particularly with executive function, we ran the linear regression analyses twice for the two composite cognitive scores. The following model was used: Composite score ∼ Normalized Age + Sex + Cognitive status * HMHYPERT. Result: Executive function: Fig. 1 A shows scores for Trails B, Clock Draw and Clock Copy. The regression analysis showed that executive function was associated with only sex and HYMHYPERT (p<0.05). Memory function: Fig. 1 B shows scores for Logical Memory and RAVLT tests in CN and MCI. As expected, MCI groups demonstrate the lowest averages compared to CN groups. As expected, regression analysis showed that memory performance was strongly determined by diagnostic status (p<0.001). Age was significantly associated with memory performance (p<0.05). HMHYPERT was only marginally significantly associated with memory performance (p = 0.08). Conclusion: Using HMHYPERT score as a measure of vascular risk, we observed notable differences in executive function. Although HIS scores ≤ 4 minimize vascular pathology in ADNI3, cognitive performance is still associated with these scores. [ABSTRACT FROM AUTHOR]

Background: Converging literatures suggest that deep brain stimulation (DBS) in Parkinson's disease affects multiple circuit mechanisms. One proposed mechanism is the normalization of primary motor cortex (M1) pathophysiology via effects on the hyperdirect pathway. Objectives: We hypothesized that DBS would reduce the current intensity necessary to modulate motor‐evoked potentials from focally applied direct cortical stimulation (DCS). Methods: Intraoperative subthalamic DBS, DCS, and preoperative diffusion tensor imaging data were acquired in 8 patients with Parkinson's disease. Results: In 7 of 8 patients, DBS significantly reduced the M1 DCS current intensity required to elicit motor‐evoked potentials. This neuromodulation was specific to select DBS bipolar configurations. In addition, the volume of activated tissue models of these configurations were significantly associated with overlap of the hyperdirect pathway. Conclusions: DBS reduces the current necessary to elicit a motor‐evoked potential using DCS. This supports a circuit mechanism of DBS effectiveness, potentially involving the hyperdirect pathway that speculatively may underlie reductions in hypokinetic abnormalities in Parkinson's disease. © 2020 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Background: Resting‐state functional connectivity (rsFC) has demonstrated promise as a biomarker for Alzheimer’s disease. Traditional rsFC assumes that brain connectivity is unvarying during the entire acquisition window. However, recent studies show that brain connection patterns or states are dynamic and change within a few seconds. We modified existing analyses of dynamic functional connectivity to identify differences between patients with Mild Cognitive Impairment (MCI) and controls. Method: 50 MCI patients (22 males, 74±8 years old) and 55 controls (35 males, age=75±8 y.o.) from ADNI3 with T1 and fast BOLD fMRI (TR=600ms) images were analyzed. Preprocessing consisted of motion correction, despiking, physiological noise correction, and registration to MNI space. Sliding windows had a stride length of 1 TR and contained 50 TRs. Correlation matrices were generated between brain regions of the default mode (DMN), salience (Sal), dorsal attention (DAN), and frontoparietal (FPN) networks. The data was then harmonized using ComBat to remove scanner‐related differences. Distinct brain connectivity patterns or states were extracted with Davies‐Bouldin optimized k‐means clustering. Dwell time (i.e., the number of TRs for which a state is sustained) and transition probability (i.e., the probability of changing state) were calculated. Significant differences were assessed using a t‐test without correcting for multiple comparisons. Result: We identified eight states (Figure 1). No significant differences were discovered in dwell time (Figure 2). Six transition probabilities had p<0.05 (Figure 3). These included the transition from 2 to 5 (p=0.03), 2 to 6 (p=0.04), 2 to 7 (p=0.04), 4 to 3 (p=0.005), 6 to 3 (p=0.01), 8 to 4 (p=0.05), and 8 to 7 (p=0.05). Conclusion: Our study showed MCI patients were less likely to transition from state 4 to 3 compared to controls. State 4 is characterized by positive rsFC within the DMN and FPN as well as between the networks. Compared to state 4, state 3 had decreased rsFC within the DMN and FPN and between the networks. State 3 also shows increased positive rsFC within and between Sal and DAN. Interaction between the 4 networks has been implicated in AD. The inability to perform this transition could explain cognitive deficits observed in MCI. [ABSTRACT FROM AUTHOR]