AI Peer Review for Neuroscience Manuscripts
From pseudoreplication in animal work to double dipping in fMRI, neuroscience has signature analytic traps. ManuscriptMind knows them.
Neuroscience reviewers have a well-known list of analytic concerns. Cells counted as independent when they come from a handful of animals. Regions of interest selected from the same data used to test them. Voxelwise comparisons run without correction. Small samples in expensive experiments.
ManuscriptMind reviews your neuroscience manuscript for these specific failure modes, alongside the design and reporting standards the field now expects.
What reviewers flag in Neuroscience papers
Pseudoreplication
Treating multiple cells, slices, or trials from one animal as independent observations, which inflates the effective sample size and the false-positive rate.
Circular analysis (double dipping)
Selecting voxels, regions, or neurons using the same data later used to estimate the effect, which guarantees an inflated result. A defining neuroimaging error.
Uncorrected voxelwise comparisons
Running tens of thousands of comparisons across the brain without proper correction for multiple comparisons.
Underpowered animal studies
Small n with no power justification, no randomization, and no blinding, leaving results fragile and hard to replicate.
Missing randomization and blinding
Experimental and control conditions assigned and assessed without blinding, opening the door to expectancy effects.
Statistical pitfalls specific to Neuroscience
- Pseudoreplication, with the unit of analysis (cell versus animal) misidentified
- Circular analysis from non-independent region or feature selection
- Voxelwise or electrode-wise multiplicity left uncorrected
- Underpowered designs reported without randomization or blinding
Reporting guidelines we check against
What ManuscriptMind checks in your Neuroscience manuscript
- Whether the unit of analysis is correct and replication is genuine
- Independence of selection and testing steps in your pipeline
- Correction for multiple comparisons across voxels, electrodes, or features
- Power, randomization, and blinding in experimental designs
- Whether the conclusions match the strength of the evidence
Review your Neuroscience manuscript before you submit
Upload your paper and get structured, severity-classified feedback in minutes. Methodology, statistics, and literature issues flagged with specific fixes. Free during beta.
Frequently asked questions
Does it catch pseudoreplication?
Yes. ManuscriptMind specifically checks whether your unit of analysis is correct, flagging cases where cells or trials from a few animals are treated as independent observations.
Does it understand neuroimaging analysis?
It looks for circular analysis (double dipping) and uncorrected voxelwise multiple comparisons, two of the most common reasons neuroimaging results fail to replicate.
Can it review animal studies?
Yes. For animal research it checks against ARRIVE-style expectations: sample size justification, randomization, blinding, and clear reporting of the experimental unit.
Is my unpublished data kept confidential?
ManuscriptMind never trains on your manuscripts and deletes data on request.