scholarly journals Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

2020 ◽  
Author(s):  
Tao-Ran Li ◽  
Yue Wu ◽  
Juan-Juan Jiang ◽  
Jie-Hui Jiang ◽  
Ying Han
Keyword(s):  
Cognitive Decline ◽  
High Frequency ◽  
Exploratory Study ◽  
Cingulate Gyrus ◽  
Resonance Imaging ◽  
Test Set ◽  
Posterior Cingulate Gyrus ◽  
Preclinical Ad ◽  
Conversion Time ◽  
Frequency Features

Abstract Background: Diagnosing Alzheimer’s disease (AD) in the preclinical stage offers opportunities to early intervention, however, there is a lack of convenient biomarkers currently. By using the methods of radiomics analysis, we aimed to determine whether the features extracted from multi-parameter magnetic resonance imaging (MRI) can be used as potential biomarkers. Methods: This study is part of the SILCODE project (NCT03370744). All participants were cognitively healthy at baseline. The cohort 1 (n=183) was divided into individuals with preclinical AD (n=78) and controls (n=105) by amyloid-positron emission tomography, used as the training dataset (80%) and validation dataset (the rest 20%); cohort 2 (n=51) was divided into “converters” and “non-converters” by individuals’ future cognitive status, used as a separate test dataset; cohort 3 included 37 “converters” (13 from ADNI), was used as another test set for independent longitudinal researches. We extracted radiomics features from multi-parameter MRI of each participant, used t-tests, autocorrelation tests, and three independent selection algorithms, respectively, to select features. Then we established two classification models (support vector machine (SVM) and random forest (RF)) to verify the efficiency of retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort 3 by paired two-sample t-tests and survival analyses, in order to identify whether their levels change with cognitive decline and impact conversion time. Results: The SVM and RF models both showed excellent classification efficiency, with the average accuracy of 89.65%-95.90% and 87.07%-90.81% respectively in the validation set, 81.86%-89.10% and 83.19%-83.68% respectively in the test set. Three stable high-frequency features were identified, namely Large zone high-gray-level emphasis feature of right posterior cingulate gyrus, Variance feature of left superior parietal gyrus and Coarseness feature of left posterior cingulate gyrus, all based on structural MRI modality; their levels were correlated with amyloid-β deposition, played good roles in predicting future cognitive decline (AUCs 64.9%-76.1%). In addition, levels of the Variance feature at baseline timepoint decreased with cognitive decline, and can affect the conversion time (p<0.05). Conclusion: In this exploratory study, we show radiomics features of multi-parameter MRI can be potential biomarkers of preclinical AD.

scholarly journals Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer’s Disease: An Exploratory Study

2020 ◽  
Vol 8 ◽  
Author(s):  
Tao-Ran Li ◽  
Yue Wu ◽  
Juan-Juan Jiang ◽  
Hua Lin ◽  
Chun-Lei Han ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
High Frequency ◽  
Resonance Imaging ◽  
Posterior Cingulate Gyrus ◽  
Preclinical Ad ◽  
Mri Scans ◽  
Conversion Time ◽  
Frequency Features

Diagnosing Alzheimer’s disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into “converters” and “nonconverters” according to individuals’ future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer’s Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7–95.9% and 87.1–90.8% in the validation set and 81.9–89.1% and 83.2–83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649–0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p &lt; 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

Diffusion tensor in posterior cingulate gyrus: correlation with cognitive decline in Alzheimer??s disease

Neuroreport ◽  
2002 ◽  
Vol 13 (17) ◽  
pp. 2299-2302 ◽  
Author(s):  
Takashi Yoshiura ◽  
Futoshi Mihara ◽  
Koji Ogomori ◽  
Atsuo Tanaka ◽  
Koichiro Kaneko ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Diffusion Tensor ◽  
Cingulate Gyrus ◽  
Posterior Cingulate ◽  
Posterior Cingulate Gyrus

BoNT-A efficacy in high-frequency migraine: An open-label, single-arm, exploratory study applying the PREEMPT paradigm

Toxicon ◽  
2021 ◽  
Vol 190 ◽  
pp. S47
Author(s):  
Daniele Martinelli ◽  
Sebastiano Arceri ◽  
Roberto De Icco ◽  
Marta Allena ◽  
Elena Guaschino ◽  
...  
Keyword(s):  
High Frequency ◽  
Exploratory Study ◽  
Open Label

scholarly journals Optimizing Subjective Cognitive Decline to Detect Early Cognitive Dysfunction

2021 ◽  
Vol 80 (3) ◽  
pp. 1185-1196
Author(s):  
Silvia Chapman ◽  
Preeti Sunderaraman ◽  
Jillian L. Joyce ◽  
Martina Azar ◽  
Leigh E. Colvin ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Cognitive Dysfunction ◽  
Associative Memory ◽  
Short Term Memory ◽  
Subjective Cognitive Decline ◽  
Short Term ◽  
Term Memory ◽  
Memory Binding ◽  
Preclinical Ad ◽  
List Learning

Background: The utility of subjective cognitive decline (SCD) as an indicator of preclinical AD is overshadowed by its inconsistent association with objective cognition. Objective: This study examines if manipulations of SCD measurement affect its association with early cognitive dysfunction characteristic of preclinical AD. Methods: Cognitively healthy older adults (n = 110) completed SCD questionnaires that elicited complaints in general, compared to 5 years ago (retrospective SCD) and compared to their peers (age-anchored SCD) in binary and Likert scales. Outcome cognitive tasks included an associative memory task (Face-Name Test), a visual short-term memory binding task (STMB test), and a clinical neuropsychological list learning test (Selective Reminder Test). Results: SCD complaints, when compared to age-matched peers (age-anchored SCD) were endorsed less frequently than complaints compared to 5 years ago (retrospective SCD) (p < 0.01). In demographically adjusted regressions, age-anchored ordinal-rated SCD was associated with short term memory binding (β= –0.22, p = 0.040, CI = –0.45, –0.01), associative memory (β= –0.26, p = 0.018, CI = –0.45, –0.06), and list learning (β= –0.31, p = 0.002, CI = –0.51, –0.12). Retrospective and general ordinal-rated SCD was associated with associative memory (β= –0.25, p = 0.012, CI = –0.44, –0.06; β= –0.29, p = 0.003, CI = –0.47, –0.10) and list learning only (β= –0.25, p = 0.014, CI = –0.45, –0.05; β= –0.28, p = 0.004, CI = –0.48, –0.09). Conclusion: Ordinal age-anchored SCD appears better suited than other SCD measurements to detect early cognitive dysfunction characteristic of preclinical AD.

NILM: Multivariate DNN performance analysis with high frequency features

2021 ◽  
Author(s):  
Camilo Marino ◽  
Elias Masquil ◽  
Franco Marchesoni ◽  
Alicia Fernandez ◽  
Pablo Massaferro
Keyword(s):  
Performance Analysis ◽  
High Frequency ◽  
Frequency Features

scholarly journals Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

2018 ◽  
Vol 8 (8) ◽  
pp. 1258 ◽  
Author(s):  
Shuming Jiao ◽  
Zhi Jin ◽  
Chenliang Chang ◽  
Changyuan Zhou ◽  
Wenbin Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Critical Issue ◽  
Compression Process ◽  
Compression Scheme ◽  
Quality Degradation ◽  
Digital Format ◽  
Enormous Amount ◽  
Frequency Features

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

scholarly journals Awake Transcortical Approach Resection of Dominant Posterior Cingulate Gyrus Glioma: 2-Dimensional Operative Video

2018 ◽  
Vol 17 (1) ◽  
pp. E19-E20 ◽  
Author(s):  
N U Farrukh Hameed ◽  
Bin Wu ◽  
Fangyuan Gong ◽  
Jie Zhang ◽  
Hong Chen ◽  
...  
Keyword(s):  
Cingulate Gyrus ◽  
Posterior Cingulate ◽  
Posterior Cingulate Gyrus ◽  
Transcortical Approach

Volumetric reduction in various cortical regions of elderly patients with early-onset and late-onset mania

2010 ◽  
Vol 23 (1) ◽  
pp. 149-154 ◽  
Author(s):  
Shou-Hung Huang ◽  
Shang-Ying Tsai ◽  
Jung-Lung Hsu ◽  
Yi-Lin Huang
Keyword(s):  
Elderly Patients ◽  
Early Onset ◽  
Late Onset ◽  
Cingulate Gyrus ◽  
Posterior Cingulate Gyrus ◽  
Left Caudate ◽  
Cortical Regions ◽  
The Right ◽  
Bipolar Patients ◽  
Left Middle

ABSTRACTBackground: Few studies have examined alterations of the brain in elderly bipolar patients. As late-onset mania is associated with increased cerebrovascular morbidity and neurological damage compared with typical/early-onset mania, we investigated differences in the volume of various cortical regions between elderly patients with early-onset versus late-onset mania.Methods: We recruited 44 bipolar patients aged over 60 years, who underwent volumetric magnetic resonance imaging at 1.5 T. The analytic method is based on the hidden Markov random field model with an expectation-maximization algorithm. We determined the volume of each cortical region as a percentage of the total intracranial volume. The cutoff age for defining early versus late onset was 45 years.Results: The study participants consisted of 25 patients with early-onset mania and 19 patients with late-onset mania; their mean ages were 65.7 years and 62.8 years, respectively. The demographic variables of the two groups were comparable. The volumes of the left caudate nucleus (p = 0.022) and left middle frontal gyrus (p = 0.013) were significantly greater and that of the right posterior cingulate gyrus (p = 0.019) was significantly smaller in the late-onset group. More patients with late-onset mania had comorbid cerebrovascular disease (p = 0.072).Conclusions: The right posterior cingulate gyrus is smaller and the left caudate nucleus and left middle frontal gyrus are larger in patients with late-onset mania compared with those with early-onset mania. Volumetric change in brain regions may vary in elderly bipolar patients with early and late-onset mania.

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