Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior

Endogenous brain states influence perception. In this manuscript the authors use human intracranial recordings to provide mechanistic insight into this process by showing that endogenous brain activity facilitates neural tuning and behavior in a stimulus and circuit specific manner

Classification accuracy for each recording.

<p>We compare the tradeoff between sensitivity and false discovery rate for out-of-bag predictions for the first 5 recordings. We also include results for recording 6 when using recording 5 to train the random forest. Performance is best for recordings 1, 2, 4 and 5. The neighborhood adjustment method (Sec 3.2.2) gives an improvement over the unadjusted cross prediction.</p

A spectrogram showing the short-time Fourier transform of the scratch bout from Fig 1.

<p>Vertical bands in the graph correspond to individual scratches. These bands are more distinct above 10 kHz.</p

Recording specifications.

<p>Recording specifications.</p

Evaluating and improving health equity and fairness of polygenic scores

Summary: Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias

Spectrograms with the filtered signal.

<p>(A,B) Spectrograms of a scratch bout recorded on two channels. (C,D) Corresponding candidate islands. We observe variation between peak heights and number of peaks. The red dots indicate locations that were marked as peaks using the method described in Sec 3.1.2.</p

Actual vs predicted scratching rates over time.

<p>We compare the true rates of scratching over time estimated using kernel smoothing to rates based on out-of-bag predictions from our method. We see that important trends are picked up, e.g. the decline in scratching over time in recording 4 and the three large groups of scratching in recording 3.</p

Performance response to training sample size.

<p>We compare the results from recordings 3 and 4 to results for subsamples of candidate islands from recording 4. The FDR-sensitivity tradeoffs for recordings 3 and 4 are plotted alongside the average tradeoffs across 10 subsamples from recording 4 of size 2,000 and 500.</p

Two local maxima in the filtered signal.

<p>The top of each dashed box corresponds to the height of the local maximum, and the vertical dashed lines show the 25 ms band around the local maximum. The bottom of the dashed box shows the height of the local maximum minus the threshold <i>h</i>. On the right, the minimum in the interval is much smaller than the threshold, so the local maximum is considered a peak. On the left, the minimum in the interval is above the dashed line, so that local maximum is not considered a peak.</p