Brain volume loss due to donanemab

2021 ◽  
Author(s):  
Scott Ayton
Keyword(s):  
Brain Volume ◽  
Volume Loss ◽  
Brain Volume Loss

Brain Volume Loss During the First Year of Interferon-Beta Treatment in Multiple Sclerosis: Baseline Inflammation and Regional Brain Volume Dynamics

2016 ◽  
Vol 26 (5) ◽  
pp. 532-538 ◽  
Author(s):  
Angela Vidal-Jordana ◽  
Jaume Sastre-Garriga ◽  
Francisco Pérez-Miralles ◽  
Deborah Pareto ◽  
Jordi Rio ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Interferon Beta ◽  
Brain Volume ◽  
First Year ◽  
Volume Loss ◽  
Brain Volume Loss ◽  
Regional Brain ◽  
Regional Brain Volume

scholarly journals Learning ability correlates with brain atrophy and disability progression in RRMS

2018 ◽  
Vol 90 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Maria Pia Sormani ◽  
Nicola De Stefano ◽  
Gavin Giovannoni ◽  
Dawn Langdon ◽  
Daniela Piani-Meier ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Volume ◽  
Practice Effect ◽  
Wilcoxon Test ◽  
Learning Ability ◽  
Lesion Volume ◽  
Volume Loss ◽  
Disability Progression ◽  
Brain Volume Loss ◽  
Quartile Group

ObjectiveTo assess the prognostic value of practice effect on Paced Auditory Serial Addition Test (PASAT) in multiple sclerosis.MethodsWe compared screening (day −14) and baseline (day 0) PASAT scores of 1009 patients from the FTY720 Research Evaluating Effects of Daily Oral therapy in Multiple Sclerosis (FREEDOMS) trial. We grouped patients into high and low learners if their PASAT score change was above or below the median change in their screening PASAT quartile group. We used Wilcoxon test to compare baseline disease characteristics between high and low learners, and multiple regression models to assess the respective impact of learning ability, baseline normalised brain volume and treatment on brain volume loss and 6-month confirmed disability progression over 2 years.ResultsThe mean PASAT score at screening was 45.38, increasing on average by 3.18 from day −14 to day 0. High learners were younger (p=0.003), had lower Expanded Disability Status Scale score (p=0.031), higher brain volume (p<0.001) and lower T2 lesion volume (p=0.009) at baseline. Learning status was not significantly associated with disability progression (HR=0.953, p=0.779), when adjusting for baseline normalised brain volume, screening PASAT score and treatment arm. However, the effect of fingolimod on disability progression was more pronounced in high learners (HR=0.396, p<0.001) than in low learners (HR=0.798, p=0.351; p for interaction=0.05). Brain volume loss at month 24 tended to be higher in low learners (0.17%, p=0.058), after adjusting for the same covariates.ConclusionsShort-term practice effects on PASAT are related to brain volume, disease severity and age and have clinically meaningful prognostic implications. High learners benefited more from fingolimod treatment.

Does Brain Volume Loss Predict Cognitive and Narrative Discourse Performance Following Traumatic Brain Injury?

2014 ◽  
Vol 23 (2) ◽  
Author(s):  
Karen Lê ◽  
Carl Coelho ◽  
Jennifer Mozeiko ◽  
Frank Krueger ◽  
Jordan Grafman
Keyword(s):  
Working Memory ◽  
Brain Volume ◽  
Brain Lesion ◽  
Cognitive Status ◽  
Narrative Discourse ◽  
Volume Loss ◽  
Regression Analyses ◽  
Cognitive Measures ◽  
Immediate Memory ◽  
Brain Volume Loss

Purpose In this study, the authors investigated the relationship between brain volume loss and performance on cognitive measures, including working memory, immediate memory, executive functions, and intelligence, and a narrative discourse production task. An underlying goal was to examine the prognostic potential of a brain lesion metric for discourse outcomes. It was hypothesized that brain volume loss would correlate with and predict cognitive and narrative discourse measures and have prognostic value for discourse outcomes. Method One hundred sixty-seven individuals with penetrating head injury participated. Correlational and regression analyses were performed for the percentages of total brain and hemispheric volume loss and scores on 4 cognitive measures (WMS–III Working Memory and Immediate Memory primary indexes, D-KEFS Sorting Test, and WAIS–III Full Scale IQ) and 7 narrative discourse measures (T-units, grammatical complexity, cohesion, local and global coherence, story completeness, and story grammar). Results The volumetric measures had significant small-to-moderate correlations with all cognitive measures but only one significant correlation with the discourse measures. Findings from regression analyses were analogous but revealed several models that approached significance. Conclusion Findings suggest that an overall measure of brain damage may be more predictive of general cognitive status than of narrative discourse ability. Atrophy measures in specific brain regions may be more informative.

Craniosynostosis following hemispherectomy in a 2.5-month-old boy with intractable epilepsy

2011 ◽  
Vol 8 (5) ◽  
pp. 450-454 ◽  
Author(s):  
Mohammad-Ali Jazayeri ◽  
John N. Jensen ◽  
Sean M. Lew
Keyword(s):  
Surgical Treatment ◽  
Brain Volume ◽  
Intractable Epilepsy ◽  
Infantile Spasms ◽  
Cranial Vault ◽  
Volume Loss ◽  
Brain Volume Loss ◽  
Course Of Action ◽  
Cranial Vault Remodeling

The authors report on the case of a 6-week-old boy who presented with infantile spasms. At 2.5 months of age, the patient underwent a right hemispherectomy. Approximately 3 months postoperatively, the patient presented with left coronal craniosynostosis. Subsequent cranial vault remodeling resulted in satisfactory cosmesis. Four years after surgery, the patient remains seizure free without the need for anticonvulsant medications. The authors believe this to be the first reported case of iatrogenic craniosynostosis due to hemispherectomy, and they describe 2 potential mechanisms for its development. This case suggests that, in the surgical treatment of infants with intractable epilepsy, minimization of brain volume loss through disconnection techniques should be considered, among other factors, when determining the best course of action.

Obesity and brain volume loss in multiple sclerosis

Neurology ◽  
2018 ◽  
Vol 91 (24) ◽  
pp. 1079-1080
Author(s):  
Ruth Ann Marrie ◽  
Helmut Butzkueven ◽  
Alberto Ascherio
Keyword(s):  
Multiple Sclerosis ◽  
Brain Volume ◽  
Volume Loss ◽  
Brain Volume Loss

Brain volume loss and no evidence of disease activity over 3 years in multiple sclerosis patients under interferon beta 1a subcutaneous treatment

2019 ◽  
Vol 59 ◽  
pp. 175-178
Author(s):  
J.I. Rojas ◽  
F. Sanchez ◽  
F. Caro ◽  
J. Miguez ◽  
L. Patrucco ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Disease Activity ◽  
Interferon Beta ◽  
Brain Volume ◽  
Volume Loss ◽  
Brain Volume Loss ◽  
Multiple Sclerosis Patients

scholarly journals BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension

2021 ◽  
Vol 13 ◽  
Author(s):  
Ghulam Md Ashraf ◽  
Stylianos Chatzichronis ◽  
Athanasios Alexiou ◽  
Nikolaos Kyriakopoulos ◽  
Badrah Saeed Ali Alghamdi ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Brain Volume ◽  
Heterogeneous Data ◽  
Imaging Biomarker ◽  
Volume Loss ◽  
Imaging Data ◽  
Clinical Dementia Rating ◽  
Brain Volume Loss ◽  
Brain White Matter ◽  
The Brain

A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.

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