Transcutaneous Vagus Nerve Stimulation (tVNS) applications in cognitive aging: a review and commentary

Differentiating healthy from pathological aging trajectories is extremely timely, as the global population faces an inversion where older adults will soon outnumber younger 5:1. Many cognitive functions (e.g., memory, executive functions, and processing speed) decline with age, a process that can begin as early as midlife, and which predicts subsequent diagnosis with dementia. Although dementia is a devastating and costly diagnosis, there remains limited evidence for medications, therapies, and devices that improve cognition or attenuate the transition into dementia. There is an urgent need to intervene early in neurodegenerative processes leading to dementia (e.g., depression and mild cognitive impairment). In this targeted review and commentary, we highlight transcutaneous Vagus Nerve Stimulation (tVNS) as a neurostimulation method with unique opportunities for applications in diseases of aging, reviewing recent literature, feasibility of use with remote data collection methods/telehealth, as well as limitations and conflicts in the literature. In particular, small sample sizes, uneven age distributions of participants, lack of standardized protocols, and oversampling of non-representative groups (e.g., older adults with no comorbid diagnoses) limit our understanding of the potential of this method. We offer recommendations for how to improve representativeness, statistical power, and generalizability of tVNS research by integrating remote data collection techniques

Visual and Imagery Magnitude Comparisons Are Affected Following Left Parietal Lesion

We describe Jane Dow (JD), a young right-handed female with acalculia following a cerebral infarction in the left intraparietal sulcus. We investigated automatic processing of different types of magnitudes that were presented visually or through imagery. We employed the size congruity task and the mental clock task that differ in stimuli presentation and in working memory load. In the size congruity task, for physical comparisons, JD presented a lack of facilitation effect, suggesting a deficit in the automatic processing of numerical values. In the mental clock task, JD performed as accurate as controls did but much slower. In both tasks, JD presented a steeper distance effect compared to controls, suggesting a deficit in a domain-general comparison process. Our findings present an atypical pattern of magnitude processing following a left parietal lesion that appears not only for visually presented stimuli but also for imagery-based magnitudes. These finding support recent theories suggesting different types of magnitudes are interconnected with each other

Quantities, amounts, and the numerical core system

Numerical cognition is essential to many aspects of life and arithmetic abilities predict academic achievements better than reading (Estrada et al., 2004). Accordingly, it is important to understand the building blocks of numerical cognition, the neural tissue involved, and the developmental trajectories. In the last two decades research has made impressive strides forward in studying numerical cognition and brain mechanisms involved in arithmetic. This advance was marked by suggestions of a numerical core system that can be characterized as a set of intuitions for quantities innately available to humans (Brannon et al., 2006

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis