Reconstruction of behavior-relevant individual brain activity: an individualized fMRI study

Decoding six basic emotions from brain functional connectivity patterns

Although distinctive neural and physiological states are suggested to underlie the six basic emotions, basic emotions are often indistinguishable from functional magnetic resonance imaging (fMRI) voxelwise activation (VA) patterns. Here, we hypothesize that functional connectivity (FC) patterns across brain regions may contain emotion-representation information beyond VA patterns. We collected whole-brain fMRI data while human participants viewed pictures of faces expressing one of the six basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) or showing neutral expressions. We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space. Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions. An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified. Finally, we demonstrated that within the same brain regions, FC-based decoding consistently performed better than VA-based decoding. Taken together, our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.

Person-specific and precision neuroimaging: Current methods and future directions

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.

Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

Cue-triggered activity replay in human early visual cortex

The recall of learned temporal sequences by a visual cue is an important form of experience-based neural plasticity. Here we observed such reactivation in awake human visual cortex using intracranial recording. After repeated exposure to a moving dot, a flash of the dot was able to trigger neural reactivation in the downstream receptive field along the motion path. This effect was observed only when the cue appeared near the receptive field. The estimated traveling speed was faster compared to the activation induced by the real motion. We suggest a range-limited, time-compressed reactivation as a result of repeated visual exposure in awake human visual cortex.

Task-related functional magnetic resonance imaging-based neuronavigation for the treatment of depression by individualized repetitive transcranial magnetic stimulation of the visual cortex

To determine whether repetitive transcranial magnetic stimulation (rTMS) of the visual cortex (VC) provides effective and well-tolerated treatment and whether magnetic resonance imaging (MRI) measures functional change of the VC as a biomarker of therapeutic effect in major depressive disorder (MDD), we performed a sham-controlled, double-blind, randomized, three-arm VC rTMS treatment study in 74 MDD patients. Neuronavigated rTMS (10 Hz, 90% of resting motor threshold, 1,600 pulses over 20 min twice per day) was performed over the VC for five days. Clinical outcome was measured by Hamilton Depression Rating Scale (HAMD-24) at days 0, 1, 3, 5 and after terminating rTMS, with follow-up at four weeks. MRI was measured at days 0 and 5. The individualized group exhibited the greatest change in HAMD-24 scores after VC rTMS for 5 days (F=5.53, P=0.005), which were maintained during follow-up period (F=4.22, P=0.016). All patients reported good tolerance. Changes in VC task-related functional MRI correlated with symptomatic reduction in the individualized group. Treatment reduced the initially abnormal increase in resting state functional connectivity from the VC to the pre/subgenual anterior cingulate cortex at day 5, especially in the individualized group. We demonstrated therapeutic potential and good tolerance of VC rTMS in MDD patients, indicated by biomarkers of fMRI measurement.