The present study investigated the neural correlates of cognitive fatigue in Multiple Sclerosis (MS), looking specifically at the relationship between self-reported fatigue and objective measures of cognitive fatigue. In Experiment 1, functional magnetic resonance imaging (fMRI) was used to examine where in the brain BOLD activity covaried with “state” fatigue, assessed during performance of a task designed to induce cognitive fatigue while in the scanner. In Experiment 2, diffusion tensor imaging (DTI) was used to examine where in the brain white matter damage correlated with increased “trait” fatigue in individuals with MS, assessed by the Fatigue Severity Scale (FSS) completed outside the scanning session. During the cognitively fatiguing task, the MS group had increased brain activity associated with fatigue in the caudate as compared with HCs. DTI findings revealed that reduced fractional anisotropy in the anterior internal capsule was associated with increased self-reported fatigue on the FSS. Results are discussed in terms of identifying a “fatigue-network” in MS.

A method for electroencephalography (EEG) - near-infrared spectroscopy (NIRS) based assessment of neurovascular coupling (NVC) during anodal transcranial direct current stimulation (tDCS). Anodal tDCS modulates cortical neural activity leading to a hemodynamic response, which was used to identify impaired NVC functionality. In this study, the hemodynamic response was estimated with NIRS. NIRS recorded changes in oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) concentrations during anodal tDCS-induced activation of the cortical region located under the electrode and in-between the light sources and detectors. Anodal tDCS-induced alterations in the underlying neuronal current generators were also captured with EEG. Then, a method for the assessment of NVC underlying the site of anodal tDCS was proposed that leverages the Hilbert-Huang Transform. The case series including four chronic (>6 months) ischemic stroke survivors (3 males, 1 female from age 31 to 76) showed non-stationary effects of anodal tDCS on EEG that correlated with the HbO2 response. Here, the initial dip in HbO2 at the beginning of anodal tDCS corresponded with an increase in the log-transformed mean-power of EEG within 0.5Hz-11.25Hz frequency band. The cross-correlation coefficient changed signs but was comparable across subjects during and after anodal tDCS. The log-transformed mean-power of EEG lagged HbO2 response during tDCS but then led post-tDCS. This case series demonstrated changes in the degree of neurovascular coupling to a 0.526 A/m(2) square-pulse (0-30 s) of anodal tDCS. The initial dip in HbO2 needs to be carefully investigated in a larger cohort, for example in patients with small vessel disease.

This work explores joint classification of gender, age and race. Specifically, we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment towards classification of named soft biometrics, as well as towards mitigation of soft biometrics related bias. The proposed algorithm achieves promising results on the UTKFace and the Bias Estimation in Face Analytics (BEFA) datasets and was ranked first in the the BEFA Challenge of the European Conference of Computer Vision (ECCV) 2018.

Monogenic or Mendelian forms of hypertension are described as a group of conditions characterized by insults to the normal regulation of blood pressure by the kidney and adrenal gland. These alterations stem from single mutations that lead to maladaptive overabsorption of electrolytes with fluid shift into the vasculature, and consequent hypertension. Knowledge of these various conditions is essential in diagnosing pediatric or early-onset adult hypertension as they directly affect treatment strategies. Precise diagnosis with specific treatment regimens aimed at the underlying physiologic derangement can restore normotension and prevent the severe sequelae of chronic hypertension.