neurodatasets_exploration

Use the notebook installer so it goes into the current kernel environment.

neurodatasets appears to be installed successfully. The output shows the package metadata and wheel were downloaded, and its main dependency requirements (pandas, numpy, python-dateutil, six) are already satisfied in the current notebook environment. No error messages are visible in the observed output, so the installation step for your original goal looks successful.

The package is installed. I’m not sure of neurodatasets’ exact loader function name yet, so the safest next step is to inspect the package API and see how brain_size_iq is exposed.

The latest output shows neurodatasets exposes a load_dataset function, which is the key piece needed for brain_size_iq. What’s still missing is confirmation that this dataset name loads correctly and what the loaded table looks like.

brain_size_iq is available through neurodatasets and is already loaded into df.

From the observed output:
- The dataset has 40 rows and 7 columns.
- Its columns are: ID, GENDER, FSIQ, VIQ, PIQ, MRI, and IQDI.
- The first rows show participant-level records with sex, IQ-related measures, MRI volume, and an IQ grouping label.

You can load it with:

df = nd.load_dataset("brain_size_iq")

and in the current notebook state, df now contains that dataset as a pandas DataFrame.

A good first EDA view here is the relationship between brain size (MRI) and full-scale IQ (FSIQ). I’d ignore ID for interpretation since it’s just an identifier.

The current Altair view is a good EDA start: it compares brain volume (MRI) with FSIQ and separates points by GENDER and IQDI. From the notebook state, the data are small enough to inspect directly, and ID should be treated as an identifier rather than an analytic feature.

To support the chart with explicit numeric evidence, the next useful step is to quantify the relationships among the brain-size and IQ variables.

This EDA already shows a clear first picture of the brain size data.

The Altair scatter plot of MRI vs FSIQ is the main visual takeaway:
- Each point is one person.
- Color by GENDER and shape by IQDI lets you compare groups without losing the raw observations.
- With 40 rows, plotting individuals directly is appropriate and avoids over-aggregation.

From the correlation table, the main findings are:
- MRI has a moderate positive relationship with IQ measures:
- MRI vs FSIQ: 0.358
- MRI vs VIQ: 0.337
- MRI vs PIQ: 0.387
- This suggests that larger brain volume tends to be associated with somewhat higher IQ scores, but the relationship is not strong. Brain size alone does not explain most of the variation in IQ in this sample.
- The IQ measures are very strongly related to each other:
- FSIQ vs VIQ: 0.947
- FSIQ vs PIQ: 0.934
- VIQ vs PIQ: 0.778
- So verbal, performance, and full-scale IQ are internally consistent in this dataset, which is expected.

What this means for the original question:
- Using Altair was a good fit because the scatter plot preserves individual-level variation and lets you visually compare gender and IQ groupings.
- The numeric results support what the plot is meant to show: there is a positive but modest association between brain size (MRI) and IQ, while the IQ subscales are much more tightly linked to each other than to brain volume.

One caution in interpretation:
- ID is only an identifier and should not be interpreted analytically.
- Because the dataset is small, patterns should be viewed as descriptive rather than definitive.

Let’s load gray_matter_patterns and do the initial inspection first.

The gray_matter_patterns dataset is now in the notebook as df.

From the observed output:
- It has 33 rows and 16 columns.
- The first column, GM, appears to list gray matter brain regions, such as bankssts, caudalanteriorcingulate, and entorhinal.
- The remaining columns are condition-specific pattern values, including SSD, MDD, AD_ADNI, BD, PD, Diabetes, HighBP, HighLipids, MET, DS_22q, Suicide, OCD_pediatric, OCD_adult, and AN.

This means the dataset is structured as a compact table of gray matter regions by disorder/condition, where each numeric entry looks like an effect or pattern score for that region-condition pair.

Let’s start the EDA with a compact column overview so we can spot data types and any missing values before plotting.

The current outputs give a solid first-pass EDA of gray_matter_patterns.

Key takeaways from what we can already see:

• The dataset is small and tidy: 33 gray matter regions by 15 condition-related numeric pattern columns plus 1 region label column.
• GM is the only non-numeric field, and it behaves like a categorical identifier for brain regions.
• There are no missing values in any column, which is a strong sign that the dataset is immediately usable for correlation analysis, clustering, ranking, or heatmaps without imputation.
• Most numeric columns have nearly all unique values, suggesting these are continuous pattern scores rather than grouped labels or counts.
• A few columns have slightly fewer unique values:
• MDD has 31 unique values
• AD_ADNI has 28
• DS_22q has 32
• Suicide has 31
• OCD_pediatric and OCD_adult each have 30
  This means there are some repeated score values, but not enough to suggest coarse discretization.
• From the sample rows, many condition scores are negative, especially for columns like AD_ADNI and SSD, which hints that the dataset may represent standardized effect directions or deviations rather than raw measurements.
• The presence of both AD_ADNI and AD_ADNIOSYRIX suggests there may be related or alternate Alzheimer’s-derived pattern columns, which would be worth comparing later because they may be highly similar.

Overall, the dataset looks well-structured for exploratory analysis focused on:
- comparing disorders/conditions across regions,
- identifying regions with strongest positive or negative pattern values,
- and examining similarity between condition profiles.

A good first Altair EDA plot here is a correlation heatmap across the condition pattern columns. It shows which disorders/conditions have similar gray matter profiles across regions.

The Altair plot you created is a good overview plot for this dataset: it maps the pairwise correlations between disorder pattern columns across the 33 gray matter regions.

From the notebook state, a few concrete things stand out:

• The dataset has 15 numeric condition columns, so a correlation heatmap is a sensible summary view.
• Because there are no missing values, every correlation in the heatmap is based on the full set of regions, which makes the visual comparison cleaner and more reliable.
• The two Alzheimer-related columns, AD_ADNI and AD_ADNIOSYRIX, are likely to appear as one of the strongest positive relationships in the heatmap because their row values in the sample are very close region by region.
• Since most columns have 33 or near-33 unique values, the heatmap is comparing mostly continuous patterns rather than repeated category-like values, which is what you want for this kind of plot.
• The heatmap is especially useful here for spotting:
• conditions with similar regional gray matter profiles,
• conditions that move in opposite directions across regions,
• and potential clusters of related disorders.

So for your original request to “show me plots use altair,” the current notebook already does that appropriately with a strong first EDA visualization. The chart is most useful as a relationship map between disorders, not as a region-by-region detail view.