Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Personalized brain stimulation of memory networks.

BACKGROUND: The finding that transcranial magnetic stimulation (TMS) can enhance memory performance via stimulation of parietal sites within the Cortical-Hippocampal Network counts as one of the most exciting findings in this field in the past decade. However, the first independent effort aiming to fully replicate this finding found no discernible influence of TMS on memory performance. OBJECTIVE: We examined whether this might relate to interindividual spatial variation in brain connectivity architecture, and the capacity of personalisation methodologies to overcome the noise inherent across independent scanners and cohorts. METHODS: We implemented recently detailed personalisation methodology to retrospectively compute individual-specific parietal targets and then examined relation to TMS outcomes. RESULTS: Closer proximity between actual and novel fMRI-personalized targets associated with greater improvement in memory performance. CONCLUSION: These findings demonstrate the potential importance of aligning brain stimulation targets according to individual-specific differences in brain connectivity, and extend upon recent findings in prefrontal cortex.

Functional dedifferentiation of the brain during healthy aging.

Nonpathological aging is associated with significant cognitive deficits. Thus, the underlying neurobiology of aging-associated cognitive decline warrants investigation. In a recent study, Chong et al. (Chong JSX, Ng KK, Tandi J, Wang C, Poh J-H, Lo JC, Chee MWL, Zhou JH. J Neurosci 39: 5534-5550, 2019) provided insights into the association between cognitive decline and the loss of functional specialization in the brains of older adults. Here, we introduce the novel graph theoretical approach utilized and discuss the significance of their findings and broader implications on aging. We also provide alternate perspectives of their findings and suggest directions for future work.