Evaluation and Treatment of Vascular Cognitive Impairment by Transcranial Magnetic Stimulation

Abstract

The exact relationship between cognitive functioning, cortical excitability, and synaptic plasticity in dementia is not completely understood. Vascular cognitive impairment (VCI) is deemed to be the most common cognitive disorder in the elderly since it encompasses any degree of vascular-based cognitive decline. In different cognitive disorders, including VCI, transcranial magnetic stimulation (TMS) can be exploited as a noninvasive tool able to evaluate in vivo the cortical excitability, the propension to undergo neural plastic phenomena, and the underlying transmission pathways. Overall, TMS in VCI revealed enhanced cortical excitability and synaptic plasticity that seem to correlate with the disease process and progression. In some patients, such plasticity may be considered as an adaptive response to disease progression, thus allowing the preservation of motor programming and execution. Recent findings also point out the possibility to employ TMS to predict cognitive deterioration in the so-called “brains at risk” for dementia, which may be those patients who benefit more of disease-modifying drugs and rehabilitative or neuromodulatory approaches, such as those based on repetitive TMS (rTMS). Finally, TMS can be exploited to select the responders to specific drugs in the attempt to maximize the response and to restore maladaptive plasticity. While no single TMS index owns enough specificity, a panel of TMS-derived measures can support VCI diagnosis and identify early markers of progression into dementia. This work reviews all TMS and rTMS studies on VCI. The aim is to evaluate how cortical excitability, plasticity, and connectivity interact in the pathophysiology of the impairment and to provide a translational perspective towards novel treatments of these patients. Current pitfalls and limitations of both studies and techniques are also discussed, together with possible solutions and future research agenda.

tDCS-Induced Memory Reconsolidation Effects and Its Associations With Structural and Functional MRI Substrates in Subjective Cognitive Decline

“…Finally, it is relevant to consider that the responder and non-responder groups classified following our tDCS-reconsolidation protocol, which entailed structural and functional neuroimaging differences, did not differ in baseline cognition, demographic variables, or clinical data. Thus, the stated imaging group differences are not likely explained by potential dissimilarities regarding vascular risk factors, which might have implied potential cortical excitability differences in response to NIBS, as previously reported (reviewed in Cantone et al, 2020 ).…”

An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment

“…Some studies reported that the language understanding network, centered on the dominant hemisphere of the MTG, is the neural basis of the brain's language understanding function (41). As we know, language impairment is an important manifestation of vascular dementia (42). All of the brain regions mentioned above are part of the DMN, the main function of which is to extract the process of episodic memory, cognition, and emotion (43).…”

“…Some studies reported that the language understanding network, centered on the dominant hemisphere of the MTG, is the neural basis of the brain's language understanding function ( 41 ). As we know, language impairment is an important manifestation of vascular dementia ( 42 ).…”

Editorial: Non-invasive Brain Stimulation in the Study and Modulation of Metaplasticity in Neurological Disorders

“…However, our understanding of the cellular and molecular mechanisms underlying distinct forms of synaptic plasticity, including metaplasticity, remains limited. Moreover, impaired synaptic plasticity, the so-called "maladaptive plasticity, " has been associated with the pathogenesis and trajectory of several brain diseases, including contributions to the dysfunctional remodeling of underlying neural networks (2)(3)(4)(5).…”