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The combination of TMS and EEG has the potential to capture relevant features of Alzheimer’s disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45–80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients’ classification as well as longitudinal disease tracking.

It is likely that when using an artificially augmented hand with six fingers, the natural five plus a robotic one, corticospinal motor synergies controlling grasping actions might be different. However, no direct neurophysiological evidence for this reasonable assumption is available yet. We used transcranial magnetic stimulation of the primary motor cortex to directly address this issue during motor imagery of objects’ grasping actions performed with or without the Soft Sixth Finger (SSF). The SSF is a wearable robotic additional thumb patented for helping patients with hand paresis and inherent loss of thumb opposition abilities. To this aim, we capitalized from the solid notion that neural circuits and mechanisms underlying motor imagery overlap those of physiological voluntary actions. After a few minutes of training, healthy humans wearing the SSF rapidly reshaped the pattern of corticospinal outputs towards forearm and hand muscles governing imagined grasping actions of different objects, suggesting the possibility that the extra finger might rapidly be encoded into the user’s body schema, which is integral part of the frontal-parietal grasping network. Such neural signatures might explain how the motor system of human beings is open to very quickly welcoming emerging augmentative bioartificial corticospinal grasping strategies. Such an ability might represent the functional substrate of a final common pathway the brain might count on towards new interactions with the surrounding objects within the peripersonal space. Findings provide a neurophysiological framework for implementing augmentative robotic tools in humans and for the exploitation of the SSF in conceptually new rehabilitation settings.

There is growing evidence that placebo effects can meaningfully modulate the brain. However, there has been little consideration of whether these changes may overlap with regions/circuits targeted by depression treatments and what the implications of this overlap would be on measuring efficacy in placebo-controlled clinical trials. In this systematic review and meta-analysis, we searched PubMed/Medline and Google Scholar for functional MRI and PET neuroimaging studies of placebo effects. Studies recruiting both healthy subjects and patient populations were included. Neuroimaging coordinates were extracted and included for Activation Likelihood Estimation (ALE) meta-analysis. We then searched for interventional studies of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS) for depression and extracted target coordinates for comparative spatial analysis with the placebo effects maps. Of 1169 articles identified, 34 neuroimaging studies of placebo effects were included. There were three significant clusters of activation: left dorsolateral prefrontal cortex (DLPFC) (x = −41, y = 16, z = 34), left sub-genual anterior cingulate cortex (sgACC)/ventral striatum (x = −8, y = 18, z = −15) and the right rostral anterior cingulate cortex (rACC) (x = 4, y = 42, z = 10). There were two significant deactivation clusters: right basal ganglia (x = 20, y = 2, z = 7) and right dorsal anterior cingulate cortex (dACC) (x = 1, y = −5, z = 45). TMS and DBS targets for depression treatment overlapped with the left DLPFC cluster and sgACC cluster, respectively. Our findings identify a common set of brain regions implicated in placebo effects across healthy individuals and patient populations, and provide evidence that these regions overlap with depression treatment targets. We model the statistical impacts of this overlap and demonstrate critical implications on measurements of clinical trial efficacy for this field.

Virtual reality (VR) applications are pervasive of everyday life, as in working, medical, and entertainment scenarios. There is yet no solution to cybersickness (CS), a disabling vestibular syndrome with nausea, dizziness, and general discomfort that most of VR users undergo, which results from an integration mismatch among visual, proprioceptive, and vestibular information. In a double-blind, controlled trial, we propose an innovative treatment for CS, consisting of online oscillatory imperceptible neuromodulation with transcranial alternating current stimulation (tACS) at 10 Hz, biophysically modelled to reach the vestibular cortex bilaterally. tACS significantly reduced CS nausea in 37 healthy subjects during a VR rollercoaster experience. The effect was frequency-dependent and placebo-insensitive. Subjective benefits were paralleled by galvanic skin response modulation in 25 subjects, addressing neurovegetative activity. Besides confirming the role of transcranially delivered oscillations in physiologically tuning the vestibular system function (and dysfunction), results open a new way to facilitate the use of VR in different scenarios and possibly to help treating also other vestibular dysfunctions.