Functional logistic discrimination with sparse PCA and its application to the structural MRI

2019 ◽  
Vol 46 (1) ◽  
pp. 147-162 ◽  
Author(s):  
Yuko Araki ◽  
Atsushi Kawaguchi
Keyword(s):  
Structural Mri ◽  
Sparse Pca ◽  
Logistic Discrimination

Discriminant Analysis and its Applications in Epidemiology

1980 ◽  
Vol 19 (04) ◽  
pp. 220-226 ◽  
Author(s):  
P. A. Lachenbruch ◽  
W. R. Clarke
Keyword(s):  
Discriminant Analysis ◽  
Historical Review ◽  
Review Article ◽  
Logistic Discrimination

This review article discusses current use of discriminant analysis in epidemiology. Contents include historical review, simple extensions and generalizations, examples, evaluation of rules, logistic discrimination, and robustness.

Examining the episodic-semantic interplay during future thinking – A reanalysis of external details

2020 ◽  
Author(s):  
Cherie Strikwerda-Brown ◽  
John Hodges ◽  
Olivier Piguet ◽  
Muireann Irish
Keyword(s):  
Life Story ◽  
Semantic Content ◽  
Structural Mri ◽  
Future Thinking ◽  
Fine Grained ◽  
Differential Vulnerability ◽  
Clinical Disorders ◽  
External Component ◽  
Future Narratives ◽  
General Semantic

Traditional analyses of autobiographical construction have tended to focus on the ‘internal’ or episodic details of the narrative. Contemporary studies employing fine-grained scoring measures, however, reveal the ‘external’ component of autobiographical narratives to contain important information relevant to the individual’s life story. Here, we used the recently developed NExt scoring protocol to explore profiles of external details generated by patients with Alzheimer’s disease (AD) (n = 11) and semantic dementia (SD) (n = 13) on a future thinking task. Voxel-based morphometry analyses of structural MRI were used to determine the neural correlates of external detail profiles in each patient group. Overall, distinct NExt profiles were observed across past and future temporal contexts in AD and SD groups, which involved elevations in external details, in the context of reduced internal details, relative to healthy Controls. Specifically, AD patients provided significantly more General Semantic details compared with Controls during past retrieval, whereas Specific Episode external details were elevated during future simulation. These increased external details within future narratives related to grey matter integrity in medial and lateral frontal regions in AD. By contrast, SD patients displayed an elevation of Specific Episode, Extended Episode, and General Semantic details exclusively during future simulation relative to Controls, which related to integrity of medial and lateral parietal regions. Our findings suggest that the compensatory external details generated during future simulation comprise an array of episodic and semantic details that vary in terms of specificity and self-relevance. Moreover, these profiles appear to be differentially affected depending on the locus of underlying neuropathology in dementia. Adopting a fine-grained approach to external details provides important information regarding the interplay between episodic and semantic content during future stimulation and highlights the differential vulnerability and preservation of distinct components of the constructed narrative in clinical disorders.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Diffusion MRI data, sulcal anatomy, and tractography for eight species from the Primate Brain Bank

2021 ◽  
Author(s):  
Katherine L. Bryant ◽  
Dirk Jan Ardesch ◽  
Lea Roumazeilles ◽  
Lianne H. Scholtens ◽  
Alexandre A. Khrapitchev ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Large Scale ◽  
Structural Mri ◽  
Relative Volume ◽  
Primate Species ◽  
Brain Bank ◽  
Cingulum Bundle ◽  
Primate Brain ◽  
Multiple Levels ◽  
Comparative Neuroscience

AbstractLarge-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal–frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.

Pinball Loss Twin Support Vector Clustering

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-23
Author(s):  
M. Tanveer ◽  
Tarun Gupta ◽  
Miten Shah ◽  
Keyword(s):  
Loss Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Structural Mri ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Support Vector Clustering ◽  
Hinge Loss ◽  
Pinball Loss ◽  
Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.

scholarly journals Microstructural Measures of the Inferior Longitudinal Fasciculus Predict Later Cognitive and Language Development in Infants Born With Extremely Low Birth Weight

2021 ◽  
pp. 088307382110198
Author(s):  
Matthew C. Bugada ◽  
Julia E. Kline ◽  
Nehal A. Parikh
Keyword(s):  
Diffusion Tensor Imaging ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Language Development ◽  
Fractional Anisotropy ◽  
Diffusion Tensor ◽  
Structural Mri ◽  
Extremely Low Birth Weight ◽  
Extremely Preterm ◽  
Inferior Longitudinal Fasciculus

Objective: Extremely preterm children are at high risk for adverse neurodevelopmental outcomes. Identifying predictors of discrete developmental outcomes early in life would allow for targeted neuroprotective therapies when neuroplasticity is at its peak. Our goal was to examine whether diffusion magnetic resonance imaging (MRI) metrics of the inferior longitudinal and uncinate fasciculi early in life could predict later cognitive and language outcomes. Study Design: In this pilot study, 43 extremely low-birth-weight preterm infants were scanned using diffusion MRI at term-equivalent age. White matter tracts were assessed via diffusion tensor imaging metrics of fractional anisotropy and mean diffusivity. The Language and Cognitive subscale scores of the Bayley Scales of Infant & Toddler Development-III at 18-22 months corrected age were our outcomes of interest. Multiple linear regression models were created to assess diffusion metrics of the inferior longitudinal and uncinate fasciculi as predictors of Bayley scores. We controlled for brain injury score on structural MRI, maternal education, birth weight, and age at MRI scan. Results: Of the 43 infants, 36 infants had high-quality diffusion tensor imaging and returned for developmental testing. The fractional anisotropy of the inferior longitudinal fasciculus was associated with Bayley-III scores in univariate analyses and was an independent predictor of Bayley-III cognitive and language development over and above known predictors in multivariable analyses. Conclusions: Incorporating new biomarkers such as the fractional anisotropy of the inferior longitudinal fasciculus with structural MRI findings could enhance accuracy of neurodevelopment predictive models. Additional research is needed to validate our findings in a larger cohort.

Sulcal characteristics patterns and gyrification gradient at different stages of Anorexia Nervosa: a structural MRI evaluation

2021 ◽  
pp. 111350
Author(s):  
Enrico Collantoni ◽  
Christopher R Madan ◽  
Valentina Meregalli ◽  
Paolo Meneguzzo ◽  
Enrica Marzola ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Structural Mri ◽  
Mri Evaluation
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