Brent G. Nelson

Bio: Brent G. Nelson is an academic researcher from University of Minnesota. The author has contributed to research in topics: Transcranial magnetic stimulation & Transcranial direct-current stimulation. The author has an hindex of 10, co-authored 14 publications receiving 972 citations. Previous affiliations of Brent G. Nelson include Veterans Health Administration & Brown University.

Consensus Recommendations for the Clinical Application of Repetitive Transcranial Magnetic Stimulation (rTMS) in the Treatment of Depression

Abstract: Objective To provide expert recommendations for the safe and effective application of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder (MDD). Participants Participants included a group of 17 expert clinicians and researchers with expertise in the clinical application of rTMS, representing both the National Network of Depression Centers (NNDC) rTMS Task Group and the American Psychiatric Association Council on Research (APA CoR) Task Force on Novel Biomarkers and Treatments. Evidence The consensus statement is based on a review of extensive literature from 2 databases (OvidSP MEDLINE and PsycINFO) searched from 1990 through 2016. The search terms included variants of major depressive disorder and transcranial magnetic stimulation. The results were limited to articles written in English that focused on adult populations. Of the approximately 1,500 retrieved studies, a total of 118 publications were included in the consensus statement and were supplemented with expert opinion to achieve consensus recommendations on key issues surrounding the administration of rTMS for MDD in clinical practice settings. Consensus process In cases in which the research evidence was equivocal or unclear, a consensus decision on how rTMS should be administered was reached by the authors of this article and is denoted in the article as "expert opinion." Conclusions Multiple randomized controlled trials and published literature have supported the safety and efficacy of rTMS antidepressant therapy. These consensus recommendations, developed by the NNDC rTMS Task Group and APA CoR Task Force on Novel Biomarkers and Treatments, provide comprehensive information for the safe and effective clinical application of rTMS in the treatment of MDD.

Low-Intensity Transcranial Current Stimulation in Psychiatry

Abstract: Neurostimulation is rapidly emerging as an important treatment modality for psychiatric disorders. One of the fastest-growing and least-regulated approaches to noninvasive therapeutic stimulation involves the application of weak electrical currents. Widespread enthusiasm for low-intensity transcranial electrical current stimulation (tCS) is reflected by the recent surge in direct-to-consumer device marketing, do-it-yourself enthusiasm, and an escalating number of clinical trials. In the wake of this rapid growth, clinicians may lack sufficient information about tCS to inform their clinical practices. Interpretation of tCS clinical trial data is aided by familiarity with basic neurophysiological principles, potential mechanisms of action of tCS, and the complicated regulatory history governing tCS devices. A growing literature includes randomized controlled trials of tCS for major depression, schizophrenia, cognitive disorders, and substance use disorders. The relative ease of use and abundant access to tC...

Human cognitive abilities: A survey of factor analytic studies

Abstract: (1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

Abstract: Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface; (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website (http://www.nitrc.org/projects/gretna/).

Resting-State fMRI: A Review of Methods and Clinical Applications

Abstract: SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Controllability of structural brain networks.

Abstract: Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.