State-Dependent Effects of Ventromedial Prefrontal Cortex Continuous Thetaburst Stimulation on Cocaine Cue Reactivity in Chronic Cocaine Users

Cue-induced craving is a significant barrier to obtaining abstinence from cocaine. Neuroimaging research has shown that cocaine cue exposure evokes elevated activity in a network of frontal-striatal brain regions involved in drug craving and drug seeking. Prior research from our laboratory has demonstrated that when targeted at the medial prefrontal cortex (mPFC), continuous theta burst stimulation (cTBS), an inhibitory form of non-invasive brain stimulation, can decrease drug cue-related activity in the striatum in cocaine users and alcohol users. However, it is known that there are individual differences in response to repetitive transcranial magnetic stimulation (rTMS), with some individuals being responders and others non-responders. There is some evidence that state-dependent effects influence response to rTMS, with baseline neural state predicting rTMS treatment outcomes. In this single-blind, active sham-controlled crossover study, we assess the striatum as a biomarker of treatment response by determining if baseline drug cue reactivity in the striatum influences striatal response to mPFC cTBS. The brain response to cocaine cues was measured in 19 cocaine-dependent individuals immediately before and after real and sham cTBS (110% resting motor threshold, 3600 total pulses). Group independent component analysis (ICA) revealed a prominent striatum network comprised of bilateral caudate, putamen, and nucleus accumbens, which was modulated by the cocaine cue reactivity task. Baseline drug cue reactivity in this striatal network was inversely related to change in striatum reactivity after real (vs. sham) cTBS treatment (ρ = -.79; p < .001; R2Adj = .58). Specifically, individuals with a high striatal response to cocaine cues at baseline had significantly attenuated striatal activity after real but not sham cTBS (t9 = -3.76; p ≤ .005). These data demonstrate that the effects of mPFC cTBS on the neural circuitry of craving are not uniform and may depend on an individual’s baseline frontal-striatal reactivity to cues. This underscores the importance of assessing individual variability as we develop brain stimulation treatments for addiction

ME-ICA/tedana: 23.0.1

Release Notes This release changes many internal aspects of the code, will make future improvements easier, and will hopefully make it easier for more people to understand their results and contribute. The denoising results should be identical. Right before releasing this new version, we released version 0.0.13, which is the last version of the older code. If you want to confirm the consistency of results, these are the two versions you should compare. Instructions for comparing results are below. Key changes Large portions of the code were reorganized and modularized to make understanding the code easier and facilitate future development Breaking change: tedana can no longer be used to manually change component classifications. A separate program, ica_reclassify, can be used for this. This makes it easier for programs like Rica to output a list of component numbers to change and to then change them with ica_reclassify. The component classification process that designates components as "accepted" or "rejected" was completely rewritten so that every step in the process is modular and the inputs and outputs of every step are logged. The documentation includes descriptions of the newly outputted files and file contents. It is now possible to select different decision trees for component selection using the --tree option. The default tree is kundu and should replicate the current outputs. We also include minimal which is a simpler tree that is intended to provide more consistent results across a study, but still needs more testing and validation and may still change. Flow charts for these two options are here. Anyone can create their own decision tree. If one is using metrics that are already calculated, like kappa and rho, and doing greater/less than comparisons, one can make a decision tree with a user-provided json file. More complex calculations might require editing the tedana python code. This change also means any metric that has one value per component can be used in a selection process. This makes it possible to combine the multi-echo metrics used in tedana with other selection metrics, such as correlations to head motion. The documentation includes instructions on building and understanding this component selection process. Breaking change: No components are classified as ignored. "Ignored" has long confused users. It was intended to identify components with such low variation that it wasn't worth deciding whether to lose a statistical degree of freedom by rejecting them. They were treated identically to accepted components. Now they are classified as "accepted" and tagged as "Low variance" or "Borderline Accept". These classification tags now appear on the html report of the results. A registry of all files outputted by tedana is now stored with the outputs. This allows for multiple file naming methods and means internal and external programs that want to interact with the tedana outputs just need to load this file. Nearly 100% of the new code and 98% of all tedana code is covered by integration testing. Tedana python package management now uses pyproject.toml Minimum python version is now 3.8 and minimum pandas version is now 2.0 (might cause problems if the same python environment is used for packages that require older versions of pandas) More comprehensive documentation of changes is in pull request #756 and the full release notes are here: https://github.com/ME-ICA/tedana/releases/tag/23.0.0 Changes [REF] Decision Tree Modularization (#756) @jbteves @handwerkerd @n-reddy @marco7877 @tsal

ME-ICA/tedana: 0.0.12

Summary This would ordinarily not have been released, but an issue with one of our dependencies means that people cannot install tedana right now. The most notable change (which will potentially change your results!) is that PCA is now defaulting to the "aic" criterion rather than the "mdl" criterion. What's Changed [DOC] Add JOSS badges by @tsalo in https://github.com/ME-ICA/tedana/pull/815[FIX] Fixes broken component figures in report when there are more than 99 components by @manfredg in https://github.com/ME-ICA/tedana/pull/824[DOC] Add manfredg as a contributor for code by @allcontributors in https://github.com/ME-ICA/tedana/pull/825DOC: Use RST link for ME-ICA by @effigies in https://github.com/ME-ICA/tedana/pull/832[DOC] Fixing a bunch of warnings & rendering issues in the documentation by @handwerkerd in https://github.com/ME-ICA/tedana/pull/840[DOC] Replace mentions of Gitter with Mattermost by @tsalo in https://github.com/ME-ICA/tedana/pull/842[FIX] The rationale column of comptable gets updated when no manacc is given by @eurunuela in https://github.com/ME-ICA/tedana/pull/855Made AIC the default maPCA option by @eurunuela in https://github.com/ME-ICA/tedana/pull/849[DOC] Improve logging of component table-based manual classification by @tsalo in https://github.com/ME-ICA/tedana/pull/852[FIX] Add jinja2 version pin as workaround by @jbteves in https://github.com/ME-ICA/tedana/pull/870 New Contributors @manfredg made their first contribution in https://github.com/ME-ICA/tedana/pull/824 Full Changelog: https://github.com/ME-ICA/tedana/compare/0.0.11...0.0.1