Advanced Neuroimaging Methods in Brain and Neurological Disorders closed

Dear Colleagues,

Whole-brain analyses have been traditionally conducted with classical statistics, either hypothesis testing or Bayesian inference. However, these classical model-driven paradigms have contributed to the replication crisis in neuroimaging analysis, mainly due to their high type-I error. One promising solution to avoid inflated false positive rates is machine learning (ML). Automated diagnostic algorithms based on ML are data-driven methods that automatically advance and eventually detect a class, with a high generalization ability. This statistical modelling process is performed using a combination of signs, symbols and features extracted from the data, without any a priori assumption. Today, the use of this type of algorithms in the field of neuroimaging, i.e., Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Functional MRI (fMRI), Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET) and so on, has received much attention and many researchers started to exploit them either alongside or to replace frequentist inference.

In addition, different ML methods are being used in automated diagnostic algorithms. Recently, they have been successfully applied to diagnose various types of neurological diseases and conditions, such as Alzheimer, Schizophrenia, Parkinson, Autism, etc. They can analyze data acquired from different types of scanners, extract deep features in high dimensional spaces and support clinicians in the diagnosis of several diseases. Given the ability of these algorithms in assisting physicians in diagnostics, continuing research in this field is of great value and importance.

This special issue of "Advanced Neuroimaging Methods in Brain and Neurological Disorders" hosted by the gues editors uses the latest and most advanced image processing and image analysis methods to study brain and neurological diseases and conditions. This approach is quite comprehensive and manages the clinical imaged data. The articles in this special issue raise our level of knowledge and ultimately lead to improved diagnostic methods and more effective treatments for brain and neurological diseases. The aim of this special issue is to present the current state-of-the-art theory and practical approaches and both therapeutic/diagnostic applications to brain and neurological diseases. In this special issue, we will invite articles that explore novel problems in brain imaging, requiring these kinds of methods/techniques in the development of computer-aided diagnosis (CAD) systems for the detection of neurological disorders.

Prof. Dr. Juan Manuel Gorriz Saez
Dr. Shuihua Wang
Dr. Roohallah Alizadehsani
Guest Editors

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