David Baranger
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davidbaranger.bsky.social
David Baranger
@davidbaranger.bsky.social
1.1K followers 1.1K following 490 posts
Assistant Professor at the Medical College of Wisconsin. 🧀
Substance use, neuroscience, genetics, & development. 🧠🧬🍺
Rock climber & dad. He/him. 🧗
Opinions my own. 🤔
bearlab.science 🐻
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davidbaranger.bsky.social
𝐘𝐨𝐮𝐭𝐡 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐞𝐬 𝐨𝐟 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐋𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐒𝐮𝐛𝐬𝐭𝐚𝐧𝐜𝐞 𝐔𝐬𝐞 𝐃𝐢𝐬𝐨𝐫𝐝𝐞𝐫𝐬. New from us, led by @sarahepaul.bsky.social. PheWAS in ABCD identifies many potentially modifiable substance use risk factors!

www.medrxiv.org/content/10.1...
November 25, 2025 at 8:02 PM
davidbaranger.bsky.social
Great work led by Andrew Castillo extending sample size stability analyses to interactions! We've also added a function implementing these analyses to the InteractionPoweR R package: dbaranger.github.io/InteractionP... #rstats
November 14, 2025 at 8:09 PM
davidbaranger.bsky.social
Reliability and sample size have a non-linear relationship. Linear increases in reliability yield diminishing reductions in the required sample size.
November 11, 2025 at 7:43 PM
davidbaranger.bsky.social
Shout out to BEAR Lab research tech Daniella Fernandez (who only joined 4 months ago!) whose poster abstract was selected for a nanosymposium at the annual meeting of the Upper Midwest Chapter of SFN this weekend! #neuroskyence #MRI 🧠🍺
October 27, 2025 at 8:20 PM
davidbaranger.bsky.social
I'm sorry, what is this scale????
Reads: "Please rate the priority for publishing this article (1 is the highest priority, 10 is the lowest priority): 10,6,7,8,1,2,3,4..."
May 13, 2025 at 2:41 PM
davidbaranger.bsky.social
Reliability of a difference score vs. reliability of an interaction term.
April 28, 2025 at 9:52 PM
davidbaranger.bsky.social
Bryce Canyon, 2021
A photo of the "Thor's hammer" rock formation, taken at Bryce Canyon National Park in 2021
February 25, 2025 at 6:22 PM
davidbaranger.bsky.social
I am reminded of similar concerns regarding candidate genes (again, see my early work) and similarly wonder how much signal vs. noise would be thrown out. The approach I describe is a global analysis, and I think our results support the hypothesis that that is a reasonable approach for this task...
Spatial map correlations with task performance and cognition. Spatial map correlations between individual difference measure associations with task activation and the 2-back>0-back main effect of task, standardized regression estimates. Pearson correlation coefficient (r) and the robust p-value are shown below each plot. Spatial map correlations with psychopathology. Spatial map correlations between individual difference measure associations with task activation and the 2-back>0-back main effect of task, standardized regression estimates. Pearson correlation coefficient (r) and the robust p-value are shown below each plot.
February 2, 2025 at 7:30 PM
davidbaranger.bsky.social
But what if you don't have thousands and thousands of participants? This is where neural signatures really shine. We find that you only need a couple hundred participants to train a model that achieves near-peak performance!
February 1, 2025 at 8:04 PM
davidbaranger.bsky.social
How do signatures compare with a standard machine learning analysis? Using the full sample, we compared to an elastic-net model, finding that the signature was slightly worse at predicting cognition, but better at predicting psychopathology! The two approaches also capture non-overlapping variance.
The neural signature achieves similar performance to machine learning models trained on each outcome. A) The absolute value of the standardized regression estimate from models predicting each outcome using the neural signature or a linear elastic net regression machine learning (ML) algorithm. The dashed line indicates the diagonal. Colors reflect the type of individual difference measure. Bolded points indicate the model fit statistics for the neural signature and ML models significantly differ from each other after false discovery rate correction for multiple comparisons at p<0.05. B) Standardized regression estimates from models that included both the neural signature and predictions from a linear elastic net regression machine learning (ML) algorithm. Lines reflect the 95% confidence intervals. Bolded points survive false discovery rate correction for multiple comparisons at p<0.05. Note: ML model associations are largely positive, as models were separately trained to predict each outcome. Directionality of neural signature associations (e..g, positive for cognition and negative for psychopathology) are consistent with associations when ML model predictions are not included in regressions. These are examined in the section Neural signature associations reflect correspondence with the main effect of task.
February 1, 2025 at 8:04 PM
davidbaranger.bsky.social
But what do neural signature associations mean? Intriguingly, they correspond to the correlation between the main effect of task map and the individual difference (BWAS) map. So they tell you whether regions that change with the task are positively or negatively correlated with your trait!
Neural signature associations reflect spatial alignment with the main effect of task. A) Main effect of task, 2-back>0-back, colored by the standardized regression estimate. B) An example regional association map for attention problems (association of the 2-back>0-back regional contrast with the CBCL attention problems score), finding that attention problems are associated with less activation of key fronto-parietal working memory regions. The map is colored by the standardized regression estimate. C) Example spatial map correlation, between attention problems regional associations and the 2-back>0-back main effect of task, standardized regression estimates (r=-0.86, probust=1.7x10-4; plots for all outcomes are shown in Supplemental Figures 7&8). Attention problems are associated with less activation in regions that are activated by the 2-back condition (relative to 0-back), and conversely, more activation in regions that are deactivated by the 2-back condition. D) Neural signatures associations (regression estimates from Figure 3) and C (the spatial map correlation) for attention problems. E) Rank-ordered neural signature associations and rank-ordered map correlations. F) Neural signatures associations and the percent of individual regional associations that survive false discovery rate (fdr) multiple test correction. D-F) Colors reflect the type of individual difference measure (orange=Cognition, green=N-back performance, purple=Psychopathology).
February 1, 2025 at 8:04 PM
davidbaranger.bsky.social
What's more, the neural signature predictions were *more* reliable than activation in individual regions and *more* sensitive to individual differences across 33 measures!
Reliability of the Neural Signature and Individual Regional Activation. The split-half reliability (comparing runs within one scan) and longitudinal stability (comparing two scans) was examined for neural signature predictions during the 0-back condition, 2-back condition, and their difference (2-back > 0-back). Red points are the neural signatures, with 95% confidence intervals indicated by horizontal lines. ICC=intraclass correlation coefficient. Shaded blue regions represent the distribution of reliabilities across all regions (n=167) for each of the corresponding conditions and contrasts. Analyses were restricted to unrelated participants. A) Split-half reliability in the baseline session, N=6,551. B) Split-half reliability in the year 2 follow up, N=5,386. C) Longitudinal stability across the two waves, N=4,111. The neural signature is associated with individual differences in behavior, cognition, and psychopathology. The neural signature: positively correlates with A) N-back task performance, B) positively correlates with NIH toolbox task performance and composite measures, and C) negatively correlates with psychopathology. Shaded yellow represents the distribution of effects across all regions. Blue points reflect neural signature association estimates with 95% Confidence intervals (horizontal lines). Estimates reflect standardized regression coefficients.
February 1, 2025 at 8:04 PM
davidbaranger.bsky.social
But does this preliminary evidence generalize? Is this an approach that anyone with task fMRI data could use? To find out, we trained a working memory signature in the ABCD study (N=9,024). The classifier performed well and captured the known neurobiology of working memory.
Plots showing the performance of a classifier trained on working memory fMRI data. A) Main-effect of the 2-back > 0-back contrast in the N-back task, using 13,477 observations from 9,024 participants. Colors reflect the t-statistic of the test. B&C) An elastic-net classifier distinguishing high and low working memory conditions was trained. Model performance - area under the receiver operating characteristic curve (AUC) - was comparable across balanced split-halves in both the training splits (B; Split 1 AUC=0.884; Split 2 AUC=0.872) and testing splits (C; Split 1 AUC=0.877; Split 2 AUC= 0.867). D) A Haufe-transformation of variable (brain region) weights identified the contribution of individual regions to model performance. Plot shows the correlation of the haufe-transformed variable importance with the main-effect of the task in both splits. Points are individual regions. E) Visualization of the haufe-transformed variable importance of each brain region, averaged across splits. Variables with larger absolute weights contribute more to the model’s predictions. F) Histogram of model predictions for each MRI scan for each of the two conditions (purple: 0-back; orange; 2-back), combined across data splits. Values reflect the predicted probability that a scan is from the 2-back condition, given a forced choice between 2-back and 0-back. G) Model predictions for the two conditions for each individual scan (points), colored by density (number of overlapping points). The white line reflects the correlation between predictions for the two conditions. H) Histogram of the difference between the model predictions for the two conditions. Values reflect how separable the two conditions are for each scan. Positive values indicate that the 2-back condition had a higher 2-back probability than the 0-back condition .
February 1, 2025 at 8:04 PM
davidbaranger.bsky.social
Task fMRI is very useful for localization and understanding neural computations, but less so for studies of differences *between* people, largely because it is less reliable than we thought (Elliott 2020 is the canonical citation pubmed.ncbi.nlm.nih.gov/32489141/)
Example simulated data, showing that there can be large replicable within-subject effects, but these do not necessarily yield reliable difference scores. Plots are of box plots, with individual points shown.
February 1, 2025 at 8:04 PM
davidbaranger.bsky.social
Bookmark for all future grants: Use of Promotional Language in Grant Applications and Grant Success: "the percentage of promotional words was positively associated with the probability of receiving funding (NIH grants: odds ratio, 1.51 [95% CI, 1.10-2.11])." jamanetwork.com/journals/jam...
December 11, 2024 at 10:53 PM
davidbaranger.bsky.social
Distracting myself from current events by spending too long on this figure. 😅
A colorful raster plot showing the linear relationship between two variables.
November 5, 2024 at 8:57 PM
davidbaranger.bsky.social
Our article exploring the neurobiological correlates of prenatal cannabis exposure is now out! "Prenatal cannabis exposure, the brain, and
psychopathology during early adolescence" https://rdcu.be/dMJqH
July 8, 2024 at 2:26 PM
davidbaranger.bsky.social
Stoked for my first APS #APS24SF! I'll be presenting on our InteractionPoweR #rstats package in an invited session on sample size planning!
May 22, 2024 at 6:27 PM
davidbaranger.bsky.social
@Alex_PMiller And at the regional level, we see expected decreases (i.e., frontal cortex thickness), but otherwise lots of regions also show *increases* relative to participants with no substance use initiation!
March 13, 2024 at 2:19 PM
davidbaranger.bsky.social
@Alex_PMiller A bit more about why I really like this paper: this isn't a drugs = smaller brain story. In fact, we mostly see that substance use is associated with *larger* brain metrics!
March 13, 2024 at 2:17 PM
davidbaranger.bsky.social
From the supplement of the paper I'm writing - 2-year longitudinal stability of Neuroimaging IDPs from the ABCD study.
January 18, 2024 at 7:17 PM
davidbaranger.bsky.social
A quick data simulation/visualization about being cautious when using difference scores.
A series of charts illustrating that replicable within-subject differences may not yield reliable difference scores, because the variables themselves are not necessarily very reliable to begin with.
December 7, 2023 at 9:09 PM
davidbaranger.bsky.social
Interestingly, a few of these measures are correlated with adolescent psychopathology (externalizing), including two of the ones that survived more stringent correction, and longitudinal mediation analyses were significant for attention/ADHD
September 20, 2023 at 8:14 PM
davidbaranger.bsky.social
These were mostly measures from diffusion imaging, but included not only white matter tracts, but also measures of both cortical grey and white matter.
September 20, 2023 at 8:14 PM
davidbaranger.bsky.social
Only 3 associations survived correction for multiple tests, and 14 more survived a less stringent correction.
September 20, 2023 at 8:14 PM