The Alzheimer's Diseases Neuroimaging Initiative project has brought together geographically distributed investigators, each collecting data on the progression of Alzheimer's disease. The quantity ...and diversity of the imaging, clinical, cognitive, biochemical, and genetic data acquired and generated throughout the study necessitated sophisticated informatics systems to organize, manage, and disseminate data and results. We describe, here, a successful and comprehensive system that provides powerful mechanisms for processing, integrating, and disseminating these data not only to support the research needs of the investigators who make up the Alzheimer's Diseases Neuroimaging Initiative cores, but also to provide widespread data access to the greater scientific community for the study of Alzheimer's Disease.
Non-invasive brain stimulation (NIBS) has been widely used to treat mild cognitive impairment (MCI). However, there exists no consensus on the best stimulation sites.
To explore potential stimulation ...locations for NIBS treatment in patients with MCI, combining meta- and resting state functional connectivity (rsFC) analyses.
The meta-analysis was conducted to identify brain regions associated with MCI. Regions of interest (ROIs) were extracted based on this meta-analysis. The rsFC analysis was applied to 45 MCI patients to determine brain surface regions that are functionally connected with the above ROIs.
We found that the dorsolateral prefrontal cortex (DLPFC) and inferior frontal gyrus (IFG) were the overlapping brain regions between our results and those of previous studies. In addition, we recommend that the temporoparietal junction (including the angular gyrus), which was found in both the meta- and rsFC analysis, should be considered in NIBS treatment of MCI. Furthermore, the bilateral orbital prefrontal gyrus, inferior temporal gyrus, medial superior frontal gyrus, and right inferior occipital gyrus may be potential brain stimulation sites for NIBS treatment of MCI.
Our results provide several potential sites for NIBS, such as the DLFPC and IFG, and may shed light on the locations of NIBS sites in the treatment of patients with MCI.
The pathogenesis and clinical heterogeneity of Parkinson's disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of ...cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA + mutation carriers and 154 LRRK2 + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA + patients and unaffected GBA + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2 + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into "endotypes". The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.
Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their ...current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48-0.95 AUC) and differed between schizophrenia (0.54-0.95 AUC), bipolar (0.48-0.65 AUC), autism (0.52-0.81 AUC) and anorexia (0.62-0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies.
Provenance in neuroimaging MacKenzie-Graham, Allan J.; Van Horn, John D.; Woods, Roger P. ...
NeuroImage (Orlando, Fla.),
08/2008, Letnik:
42, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and ...history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.
Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical ...features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.
The use of UCH-L1 detection with point-of-care (POC) assay alone has not been characterized for clinical use. This study compares the accuracies of POC UCH-L1 and Neuron-Specific Enolase (NSE) ...Elecsys® levels for identifying TBI patients with structural abnormalities on neuroimaging.
The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Phase 1 Cohort, enrolled 1375 TBI patients (GCS 3–15) presenting to one of 18 US Level I trauma centers within 24 h of injury who had an admission head CT; blood samples were collected, along with 122 orthopedic and 209 healthy controls. The TBI cohort consisted of 810 CT-negative (CT-) and 549 CT-positive (CT+) subjects. Of the CT- subjects who had MRIs, 121 were MRI-positive (MRI+) and 333 were MRI-negative (MRI-). UCH-L1 POC showed best diagnostic performance for CT + versus CT-, 0–8 h post-injury with an AUC of 0·779 0·708–0.850 when compared to the 0–25 h interval, with an AUC of 0.684 0.655–0.712. NSE assay has an AUC of 0.695 0.619–0.770 for the 0–8 h interval and 0.634 0.603–0.665 for the 0–25 h interval. During the first 8 after injury, POC UCH-L1 outperforms NSE in identifying TBI patients with structural abnormalities on neuroimaging.
Rapidly evolving neuroimaging techniques are producing unprecedented quantities of digital data at the same time that many research studies are evolving into global, multi-disciplinary collaborations ...between geographically distributed scientists. While networked computers have made it almost trivial to transmit data across long distances, collecting and analyzing this data requires extensive metadata if the data is to be maximally shared. Though it is typically straightforward to encode text and numerical values into files and send content between different locations, it is often difficult to attach context and implicit assumptions to the content. As the number of and geographic separation between data contributors grows to national and global scales, the heterogeneity of the collected metadata increases and conformance to a single standardization becomes implausible. Neuroimaging data repositories must then not only accumulate data but must also consolidate disparate metadata into an integrated view. In this article, using specific examples from our experiences, we demonstrate how standardization alone cannot achieve full integration of neuroimaging data from multiple heterogeneous sources and why a fundamental change in the architecture of neuroimaging data repositories is needed instead.
David L. Stocum (1939-2023) Crawford, Karen; Del Rio-Tsonis, Katia; Cameron, Jo Ann ...
Development (Cambridge),
2023-Jul-15, 2023-07-15, Letnik:
150, Številka:
14
Journal Article
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Odprti dostop
David L. Stocum, a scientist whose contributions to and impact on the field of regeneration and developmental biology are legendary, and likely more pervasive than many know, passed away on 21 April ...2023. His illustrious career, exploring and characterizing the fundamentals of limb regeneration in salamanders, spanned nearly 60 years. Much of his work dissecting the tissue-level logic of regeneration established the framework for the molecular investigation of regeneration taking place today. His generous spirit as mentor and colleague, encyclopedic understanding of the literature, and enthusiasm for each new discovery and its place within the larger picture of scientific understanding distinguishes him as a giant in the history of regenerative biology. David's career path, the transformative role his teachers and mentors played along the way, and his own role in inspiring the next generation of researchers speaks strongly to the importance and power of basic education to society.
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