-
Epilepsia Sep 2020One of the greatest challenges of achieving successful surgical outcomes in patients with epilepsy is the ability to properly localize the seizure onset zone (SOZ). Many... (Meta-Analysis)
Meta-Analysis
Resting-state functional magnetic resonance imaging with independent component analysis for presurgical seizure onset zone localization: A systematic review and meta-analysis.
OBJECTIVE
One of the greatest challenges of achieving successful surgical outcomes in patients with epilepsy is the ability to properly localize the seizure onset zone (SOZ). Many techniques exist for localizing the SOZ, including intracranial electroencephalography, magnetoencephalography, and stereoelectroencephalography. Recently, resting-state functional magnetic resonance imaging (rs-fMRI) in conjunction with independent component analysis (ICA) has been utilized for presurgical planning of SOZ resection, with varying results. In this meta-analysis, we analyze the current role of rs-fMRI in identifying the SOZ for presurgical planning for patients with drug-resistant epilepsy. Specifically, we seek to demonstrate its current effectiveness compared to other methods of SOZ localization.
METHODS
A literature review was conducted using the PubMed, MEDLINE, and Embase databases up to May of 2020. A total of 253 articles were screened, and seven studies were chosen for analysis. Each study was analyzed for SOZ localization by ground truth, SOZ localization by rs-fMRI with ICA, principal component analysis, or intrinsic connectivity contrast, and outcomes of surgery. A meta-analysis was performed to analyze how ground truth compares to rs-fMRI in SOZ localization.
RESULTS
The odds ratio comparing ground truth to rs-fMRI was 2.63 (95% confidence interval = 0.66-10.56). Average concordance of rs-fMRI SOZ localization compared with ground truth localization across studies was 71.3%.
SIGNIFICANCE
In the hunt for less invasive presurgical planning for epilepsy surgery, rs-fMRI with ICA provides a promising avenue for future standard practice. Our preliminary results show no significant difference in surgical outcomes between traditional standards of SOZ localization and rs-fMRI with ICA. We believe that rs-fMRI could be a step forward in this search. Further investigation comparing rs-fMRI to traditional methods of SOZ localization should be conducted, with the hope of moving toward relying solely on noninvasive screening methods.
Topics: Drug Resistant Epilepsy; Electrocorticography; Electroencephalography; Functional Neuroimaging; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Neurosurgical Procedures; Preoperative Care; Principal Component Analysis; Rest; Statistics as Topic; Stereotaxic Techniques
PubMed: 32770853
DOI: 10.1111/epi.16637 -
Clinical Neurophysiology : Official... May 2019Interictal high resolution (HR-) electric source imaging (ESI) and magnetic source imaging (MSI) are non-invasive tools to aid epileptogenic zone localization in...
OBJECTIVE
Interictal high resolution (HR-) electric source imaging (ESI) and magnetic source imaging (MSI) are non-invasive tools to aid epileptogenic zone localization in epilepsy surgery candidates. We carried out a systematic review on the diagnostic accuracy and quality of evidence of these modalities.
METHODS
Embase, Pubmed and the Cochrane database were searched on 13 February 2017. Diagnostic accuracy studies taking post-surgical seizure outcome as reference standard were selected. Quality appraisal was based on the QUADAS-2 framework.
RESULTS
Eleven studies were included: eight MSI (n = 267), three HR-ESI (n = 127) studies. None was free from bias. This mostly involved: selection of operated patients only, interference of source imaging with surgical decision, and exclusion of indeterminate results. Summary sensitivity and specificity estimates were 82% (95% CI: 75-88%) and 53% (95% CI: 37-68%) for overall source imaging, with no statistical difference between MSI and HR-ESI. Specificity is higher when partially concordant results were included as non-concordant (p < 0.05). Inclusion of indeterminate test results as non-concordant lowered sensitivity (p < 0.05).
CONCLUSIONS
Source imaging has a relatively high sensitivity but low specificity for identification of the epileptogenic zone.
SIGNIFICANCE
We need higher quality studies allowing unbiased test evaluation to determine the added value and diagnostic accuracy of source imaging in the presurgical workup of refractory focal epilepsy.
Topics: Brain Mapping; Electroencephalography; Epilepsy; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Sensitivity and Specificity
PubMed: 30824202
DOI: 10.1016/j.clinph.2018.12.016 -
Alzheimer's Research & Therapy Sep 2021An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the...
BACKGROUND
An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia.
METHODS
We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis.
RESULTS
Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance.
CONCLUSIONS
Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
Topics: Alzheimer Disease; Brain; Cognitive Dysfunction; Disease Progression; Humans; Machine Learning; Magnetic Resonance Imaging; Neuroimaging
PubMed: 34583745
DOI: 10.1186/s13195-021-00900-w -
Clinical Neurophysiology : Official... Jul 2020On 31st December 2019, China notified the World Health Organization of an outbreak of atypical pneumonia from patients at a local seafood market in Wuhan, Hubei, China,...
On 31st December 2019, China notified the World Health Organization of an outbreak of atypical pneumonia from patients at a local seafood market in Wuhan, Hubei, China, responsible for a new coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that caused COVID-19 disease, which spread rapidly around the world. WHO declared a state of pandemic (11th March, 2020), which has caused more than 1 million infected and more than 110,000 deaths; it was observed that up to 29% of those infected were health care personnel. The main route of transmission of SARS-CoV2 is through respiratory secretions and direct contact with contaminated surfaces and material. The pandemic induced an international saturation of health care services and a rupture in the supply chain of protective equipment for healthcare personnel, which poses a high occupational risk to all. Based on the different healthcare systems, human resources, infrastructure and medical emergencies that will warrant the conduct of clinical neurophysiology studies and the lack of a guide for the management of the situation, it was decided by an expert task force of the Latin American Chapter of the International Federation of Clinical Neurophysiology to carry out these guidelines for the protection of patient and healthcare professionals conducting clinical neurophysiological studies.
Topics: Advisory Committees; Ambulatory Care; Betacoronavirus; COVID-19; Coronavirus Infections; Disinfection; Electroencephalography; Health Personnel; Humans; Hygiene; Inpatients; Latin America; Magnetoencephalography; Masks; Neurophysiological Monitoring; Occupational Diseases; Pandemics; Personal Protective Equipment; Pneumonia, Viral; Polysomnography; Risk Factors; SARS-CoV-2
PubMed: 32417701
DOI: 10.1016/j.clinph.2020.04.011