“Connectomic surgery”: diffusion tensor imaging (DTI) tractography as a targeting modality for surgical modulation of neural networks

Deep brain stimulation (DBS) is being used to treat a growing number of neurological disorders. Until recently, DBS has been thought to act mainly by suppressing local neuronal activity, essentially producing a functional lesion. Numerous studies are now demonstrating that DBS has widespread network effects mediated by white matter pathways. The new science of connectomics aims to map the connectivity between brain regions in health and disease. Targeting DBS specifically to pathways which exhibit pathological connectivity could greatly expand the possibilities for treating brain diseases. This brief review examines the current state of brain imaging for visualization of these networks and describes how DBS might be used to restore normal connectivity in pathological states

High frequency deep brain stimulation attenuates subthalamic and cortical rhythms in Parkinson's disease

Parkinson's disease (PD) is marked by excessive synchronous activity in the beta (8–35 Hz) band throughout the cortico-basal ganglia network. The optimal location of high frequency deep brain stimulation (HF DBS) within the subthalamic nucleus (STN) region and the location of maximal beta hypersynchrony are currently matters of debate. Additionally, the effect of STN HF DBS on neural synchrony in functionally connected regions of motor cortex is unknown and is of great interest. Scalp EEG studies demonstrated that stimulation of the STN can activate motor cortex antidromically, but the spatial specificity of this effect has not been examined. The present study examined the effect of STN HF DBS on neural synchrony within the cortico-basal ganglia network in patients with PD. We measured local field potentials dorsal to and within the STN of PD patients, and additionally in the motor cortex in a subset of these patients. We used diffusion tensor imaging (DTI) to guide the placement of subdural cortical surface electrodes over the DTI-identified origin of the hyperdirect pathway (HDP) between motor cortex and the STN. The results demonstrated that local beta power was attenuated during HF DBS both dorsal to and within the STN. The degree of attenuation was monotonic with increased DBS voltages in both locations, but this voltage-dependent effect was greater in the central STN than dorsal to the STN (p < 0.05). Cortical signals over the estimated origin of the HDP also demonstrated attenuation of beta hypersynchrony during DBS dorsal to or within STN, whereas signals from non-specific regions of motor cortex were not attenuated. The spatially-specific suppression of beta synchrony in the motor cortex support the hypothesis that DBS may treat Parkinsonism by reducing excessive synchrony in the functionally connected sensorimotor network

Optical Deconstruction of Parkinsonian Neural Circuitry

Deep brain stimulation (DBS) is a therapeutic option for intractable neurological and psychiatric disorders, including Parkinson's disease and major depression. Because of the heterogeneity of brain tissues where electrodes are placed, it has been challenging to elucidate the relevant target cell types or underlying mechanisms of DBS. We used optogenetics and solid-state optics to systematically drive or inhibit an array of distinct circuit elements in freely moving parkinsonian rodents and found that therapeutic effects within the subthalamic nucleus can be accounted for by direct selective stimulation of afferent axons projecting to this region. In addition to providing insight into DBS mechanisms, these results demonstrate an optical approach for dissection of disease circuitry and define the technological toolbox needed for systematic deconstruction of disease circuits by selectively controlling individual components

Report on the Joint Workshop on the Relations between Health Inequalities, Ageing and Multimorbidity, Iceland, May 3-4, 2023

This paper is a summary of key presentations from a workshop in Iceland on May 3– 4, 2023 arranged by Aarhus University and with participation of the below-mentioned scientists.Below you will find the key messages from the presentations made by:Professor Jan Vandenbroucke, Department of Clinical Epidemiology, Aarhus University, Emeritus Professor, Leiden University; Honorary Professor, London School of Hygiene &amp; Tropical Medicine, UKProfessor, Chair Henrik Toft Sørensen, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, DenmarkProfessor David H. Rehkopf, Director, the Stanford Center for Population Health Sciences, Stanford University, CA., USProfessor Jaimie Gradus, Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Johan Mackenbach, Emeritus Professor, Department of Public Health, Erasmus University Rotterdam, HollandProfessor, Chair M Maria Glymour, Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, Massachusetts, USProfessor, Dean Sandro Galea, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Victor W. Henderson, Departments of Epidemiology &amp; Population Health and of Neurology &amp; Neurological Sciences, Stanford University, Stanford, CA, US; Department of Clinical Epidemiology, Aarhus University, Aarhus, D

A high-performance speech neuroprosthesis

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into tex

Neural population dynamics in human motor cortex during movements in people with ALS

The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation. DOI: http://dx.doi.org/10.7554/eLife.07436.00

α-Synuclein Suppression by Targeted Small Interfering RNA in the Primate Substantia Nigra

The protein α-synuclein is involved in the pathogenesis of Parkinson's disease and other neurodegenerative disorders. Its toxic potential appears to be enhanced by increased protein expression, providing a compelling rationale for therapeutic strategies aimed at reducing neuronal α-synuclein burden. Here, feasibility and safety of α-synuclein suppression were evaluated by treating monkeys with small interfering RNA (siRNA) directed against α-synuclein. The siRNA molecule was chemically modified to prevent degradation by exo- and endonucleases and directly infused into the left substantia nigra. Results compared levels of α-synuclein mRNA and protein in the infused (left) vs. untreated (right) hemisphere and revealed a significant 40–50% suppression of α-synuclein expression. These findings could not be attributable to non-specific effects of siRNA infusion since treatment of a separate set of animals with luciferase-targeting siRNA produced no changes in α-synuclein. Infusion with α-synuclein siRNA, while lowering α-synuclein expression, had no overt adverse consequences. In particular, it did not cause tissue inflammation and did not change (i) the number and phenotype of nigral dopaminergic neurons, and (ii) the concentrations of striatal dopamine and its metabolites. The data represent the first evidence of successful anti-α-synuclein intervention in the primate substantia nigra and support further development of RNA interference-based therapeutics

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics