About this Research Topic
Pervasive and elusive human variability, both across and within individuals, poses a major challenge in interpreting and decoding human brain activity. Differences in brain anatomy and functionality across individuals contribute to the inter-subject variability. Within an individual, changes in neural processing, non-stationarity of brain activities, the variation of neurophysiological mechanisms, and various unknown factors might give rise to the intra-subject variability.
Recently, there has been an increasing number of studies that have focused on appreciating rather than ignoring variability. Through the lens of variability, they have led to a better insight into individual differences and cross-session variations, facilitating precision functional brain mapping and decoding based on individual variability and similarity. For instance, the robustness of brain decoding has been improved by transfer learning techniques that are capable of tackling variability in data collected from different subjects across different sessions and days. On the other hand, the applicability of a neurophysiological biometric relies on its manifest inter-subject variability and minimal intra-subject variability. Critical questions, therefore, arise regarding how inter- and intra-subject variability can be observed, analyzed and modeled, what pros and cons researchers might gain from the variability, and how to deal with the variability in brain imaging and decoding.
The goal of this Research Topic is to encourage researchers to examine human variability in brain imaging and decoding, with a focus on both advantages and disadvantages of inter- and intra-subject variability in mapping and modeling brain functions. We welcome empirical, theoretical and meta-analytical work and encourage authors to re-examine their datasets through the scopes of human variability rather than averaged observations and overall interpretations. Subtopics of interest include, but are not limited to:
• The imaging and characteristics of inter- and intra-subject variability in neuroimaging data.
• Evaluating and tracking variability within a single subject and across multiple subjects.
• Obtaining neuroscientific findings from leveraging the variability in brain activities.
• Enhancing the performance of brain decoding against or through variability.
• Generic model learning of brain imaging
• Transfer learning and model adaptation based on inter-/intra-subject variability
Recently, there has been an increasing number of studies that have focused on appreciating rather than ignoring variability. Through the lens of variability, they have led to a better insight into individual differences and cross-session variations, facilitating precision functional brain mapping and decoding based on individual variability and similarity. For instance, the robustness of brain decoding has been improved by transfer learning techniques that are capable of tackling variability in data collected from different subjects across different sessions and days. On the other hand, the applicability of a neurophysiological biometric relies on its manifest inter-subject variability and minimal intra-subject variability. Critical questions, therefore, arise regarding how inter- and intra-subject variability can be observed, analyzed and modeled, what pros and cons researchers might gain from the variability, and how to deal with the variability in brain imaging and decoding.
The goal of this Research Topic is to encourage researchers to examine human variability in brain imaging and decoding, with a focus on both advantages and disadvantages of inter- and intra-subject variability in mapping and modeling brain functions. We welcome empirical, theoretical and meta-analytical work and encourage authors to re-examine their datasets through the scopes of human variability rather than averaged observations and overall interpretations. Subtopics of interest include, but are not limited to:
• The imaging and characteristics of inter- and intra-subject variability in neuroimaging data.
• Evaluating and tracking variability within a single subject and across multiple subjects.
• Obtaining neuroscientific findings from leveraging the variability in brain activities.
• Enhancing the performance of brain decoding against or through variability.
• Generic model learning of brain imaging
• Transfer learning and model adaptation based on inter-/intra-subject variability
Keywords: variability, neuroimaging, brain mapping, brain decoding, functional brain modeling, brain-computer interface, EEG, MEG, fMRI, fNIRS
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