1AbstractTo extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer’s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer’s and healthy brain data has been challenging in older adults (age > 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting-edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer’s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer’s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject-level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject-level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.

Migraine is a heterogeneous disorder with variable symptoms and responsiveness to therapy. Due to previous analytic shortcomings, variance in migraine symptoms has been weakly and inconsistently related to brain function. Taking advantage of neural network organization measured through resting-state functional connectivity (RSFC) and advanced statistical analysis, sophisticated symptom-brain mapping can now be performed. In the current analysis we used data from two sites (n=102 and 41), and performed Canonical Correlation Analysis (CCA), relating RSFC with a broad range of migraine symptoms ranging from headache characteristics to sleep abnormalities. This identified three dimensions of covariance between symptoms and RSFC. Importantly, none of these dimensions bore any relationship with subject motion. The first dimension was related to headache intensity, headache frequency, pain catastrophizing, affect, sleep disturbances, and somatic abnormalities, and was associated with frontoparietal and dorsal attention network connectivity, both of which are major cognitive networks. Additionally, RSFC scores from this dimension, both the baseline value and the change from baseline to post-intervention, were associated with clinical responsiveness to mind-body therapy. The second dimension was related to an inverse association between pain and anxiety, and to default mode network connectivity. The final dimension was related to pain catastrophizing, and salience, sensorimotor and default mode network connectivity. These unique symptom/brain-mappings over three dimensions provide novel network targets to modify specific ensembles of symptoms. In addition to performing CCA, we evaluated the current clustering of migraine patients into episodic and chronic subtypes, and found no evidence to support this clustering. However, when using RSFC scores from the three significant dimensions, we identified a novel clustering of migraine patients into four biotypes with unique functional connectivity patterns. These findings provide new insight into individual variability in migraine, and could serve as the foundation for novel therapies that take advantage of migraine heterogeneity.

Hypnotizability, one’s ability to experience cognitive, emotional, behavioral, and physical changes in response to suggestions in the context of hypnosis, is a highly stable trait associated with increased functional connectivity between the left dorsolateral prefrontal cortex (L-DLPFC) and dorsal anterior cingulate cortex (dACC). We conducted a preregistered, triple-blinded, randomized controlled trial to test the ability of continuous theta-burst stimulation (cTBS) over a personalized neuroimaging-based L-DLPFC target to temporarily enhance hypnotizability. We tested our hypothesis in 78 patients with fibromyalgia syndrome (FMS), a functional pain disorder for which hypnosis has consistently been shown to be beneficial as a nonpharmacological treatment option. Pre-to-post cTBS change in Hypnotic Induction Profile scores (HIP; a standardized measure of hypnotizability) was significantly greater in the Active versus Sham group. Our findings suggest a causal relationship between L-DLPFC and dACC function and hypnotizability. Dose-response optimization should be further examined to formalize guidelines for future clinical utilization.Trial registrationClinicalTrials.govNCT02969707