{
"cells": [
{
"cell_type": "markdown",
"id": "696469fd",
"metadata": {},
"source": [
"# Notebook 2. Hippocampal Analysis"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d96f6119",
"metadata": {},
"outputs": [],
"source": [
"# Work from this notebook's folder (content/), also when the notebook is executed from the project root\n",
"# (myst build --execute, NeuroLibre), so the local modules (helpers, stats_test, ...) and ../data/ resolve.\n",
"import os\n",
"import sys\n",
"\n",
"if os.path.isdir(\"content\") and not os.path.isfile(\"helpers.py\"):\n",
" os.chdir(\"content\")\n",
"if os.getcwd() not in sys.path:\n",
" sys.path.insert(0, os.getcwd())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "27b5e363",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import numpy as np\n",
"\n",
"from statsmodels.stats.multitest import multipletests\n",
"\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"\n",
"# Setting text svg exports as exports\n",
"plt.rcParams['svg.fonttype'] = 'none'\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fa980633",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ All R dependencies are loaded and ready!\n"
]
}
],
"source": [
"# rpy2 configuration\n",
"import rpy2.robjects as ro\n",
"from rpy2.robjects.packages import importr\n",
"from rpy2.robjects import pandas2ri\n",
"from rpy2.robjects.conversion import localconverter\n",
"\n",
"try:\n",
" effectsize = importr('effectsize')\n",
" marginaleffects = importr('marginaleffects')\n",
" car = importr('car')\n",
" print(\"✅ All R dependencies are loaded and ready!\")\n",
"except Exception as e:\n",
" print(f\"❌ Missing R package: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "add3c5e6",
"metadata": {},
"outputs": [],
"source": [
"# Import auxiliary functions bundled in this notebook\n",
"import stats_test as st\n",
"import helpers"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b1a6ddb4",
"metadata": {},
"outputs": [],
"source": [
"# Static (matplotlib/seaborn) figure versions for the Word/PDF/LaTeX exports and the journal figures.\n",
"# Written to static/figures/ only when STATIC_FIGURE_FORMATS is set (pixi tasks static-figures-png/-pdf/-svg,\n",
"# export-docx, export-tex). A plain `pixi run run-notebooks` does not write them.\n",
"create_Static_figures = bool(helpers.STATIC_FIGURE_FORMATS)\n",
"\n",
"# This color pallet matches plotly figures with seaborn defaults.\n",
"color_palette = [\"#4C72B0\", \"#DD8452\", \"#55A868\", \"#C44E52\", \"#8172B2\", \"#937860\", \"#DA8BC3\", \"#8C8C8C\", \"#CCB974\", \"#64B5CD\"]"
]
},
{
"cell_type": "markdown",
"id": "47c87c01",
"metadata": {},
"source": [
"## Loading the data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e167dce2",
"metadata": {},
"outputs": [],
"source": [
"# read MRI metrics data: HippUnfold surface metrics averaged over the vertices of each participant, hemisphere and\n",
"# subfield (n_vertices = number of vertices averaged)\n",
"df = pd.read_csv(helpers.data_path(\"hyades_hippunfoldAD_surfmetrics.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7ad71d80",
"metadata": {},
"outputs": [],
"source": [
"# read volumetric data\n",
"df_vol = pd.read_csv(helpers.data_path(\"hyades_hippunfoldAD_volumes_normalized.tsv\"),sep='\\t')\n",
"\n",
"# Volumes are fractions of total intracranial volume (TIV). Express them as percentages of TIV: the smallest subfields (e.g. CA2)\n",
"# otherwise fall below the numerical tolerance of car::Anova (residual sum of squares treated as 0).\n",
"# F statistics, effect sizes and p-values are unaffected by this change of units.\n",
"volume_cols = ['Sub', 'CA1', 'CA2', 'CA3', 'CA4', 'DG', 'SRLM', 'Cyst']\n",
"df_vol[volume_cols] = df_vol[volume_cols] * 100"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "74fa6ea8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" participant_id reason\n",
"0 sub-021 HippUnfold segmentation with a small leak arti...\n",
"1 sub-024 HippUnfold segmentation slightly failed: SRLM ...\n",
"2 sub-035 HippUnfold segmentation failed with an artifac...\n",
"3 sub-045 HippUnfold segmentation slightly failed with s...\n",
"4 sub-049 HippUnfold segmentation failed with subiculum ...\n",
"5 sub-051 HippUnfold segmentation struggled with multipl...\n",
"6 sub-057 HippUnfold segmentation failed with small leak...\n"
]
}
],
"source": [
"# Exclusion list obtained from QC the segemntation and registration of the anatomical scans. Participants with a QC level of 0 were excluded from the analyses.\n",
"exclusion_list = pd.read_csv(helpers.data_path(\"exclusion_list.csv\")) \n",
"print(exclusion_list)\n",
"exclusion_list = exclusion_list[\"participant_id\"].to_list()"
]
},
{
"cell_type": "markdown",
"id": "16a57135",
"metadata": {},
"source": [
"## Wrangling"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "64c43b58",
"metadata": {},
"outputs": [],
"source": [
"# rename column from dataframe (just for stylec onsitency with paper)\n",
"df = df.rename(columns={'D_dki': 'MD'})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "66fb9890",
"metadata": {},
"outputs": [],
"source": [
"# Drop subjects with other diagnosis than dementia\n",
"nonDemGroups = ['PDD','PSP','VCI','PD','Conc','aMCI','TBD']\n",
"df = df.query(\"group != @nonDemGroups\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4441c0f5",
"metadata": {},
"outputs": [],
"source": [
"# Drop subjects in the exclusion list\n",
"df = df.query(\"participant_id != @exclusion_list\")\n",
"df_vol = df_vol.query(\"participant_id != @exclusion_list\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "71e7a9e9",
"metadata": {},
"outputs": [],
"source": [
"# Define the desired order of categories\n",
"group_order = ['HC', 'LBD', 'FTD', 'AD']\n",
"\n",
"# Convert to ordered categorical type (the data files code healthy controls as 'Control'; the paper uses HC)\n",
"df['group'] = df['group'].replace({'Control': 'HC'})\n",
"df['group'] = pd.Categorical(df['group'], categories=group_order, ordered=True)\n",
"\n",
"# harmonize color palette\n",
"group_colors = dict(zip(group_order, color_palette))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8d86ecc8",
"metadata": {},
"outputs": [],
"source": [
"# dropping srlm and cyst subfields\n",
"df = df.query(\"subfield != 'SRLM' and subfield != 'Cyst'\")\n",
"\n",
"# creating a subfield type to give a right order to values\n",
"subfield_category_order = ['Sub','CA1','CA2','CA3','CA4','DG']\n",
"# keep only the six subfields (drops unlabeled vertices, label '0') before ordering them\n",
"df = df[df['subfield'].isin(subfield_category_order)].copy()\n",
"df['subfield'] = pd.Categorical(df['subfield'], categories=subfield_category_order, ordered=True)"
]
},
{
"cell_type": "markdown",
"id": "13f1ee27",
"metadata": {},
"source": [
"## Extract features to study"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "20404522",
"metadata": {},
"outputs": [],
"source": [
"metadata = [\"participant_id\", \"group\", \"age\",'sex',\"hemisphere\",\"subfield\",'subfield_ID']\n",
"#metrics = [\"FA\",\"D_uFA\", \"uFA\", \"Ki\", \"Vol_mm3_norm\", \"MTsat\"]\n",
"metrics = [\"FA\",\"MD\", \"uFA\", \"Ki\", \"MTsat\", \"thickness\",\"curvature\",\"gyrification\"]\n",
"df = df[metadata + metrics + ['n_vertices']]"
]
},
{
"cell_type": "markdown",
"id": "83551598",
"metadata": {},
"source": [
"#### grouping"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1e9fb63c",
"metadata": {},
"outputs": [],
"source": [
"# group by subfields\n",
"df_Hemi_grouped = df.drop(columns=['n_vertices']).groupby(['participant_id','group','sex','age','subfield','subfield_ID','hemisphere'],observed=True).mean()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "54ebc0c9",
"metadata": {},
"outputs": [],
"source": [
"# group data by subject: whole hippocampus = mean over all vertices (weighted by the number of vertices)\n",
"def vertex_weighted_mean(x):\n",
" w = x['n_vertices']\n",
" return pd.Series({m: np.average(x[m], weights=w) if x[m].notna().all() else np.nan for m in metrics})\n",
"\n",
"df_Subject_grouped = df.groupby(['participant_id','group','sex','age'], observed=True).apply(vertex_weighted_mean)"
]
},
{
"cell_type": "markdown",
"id": "8a9faff9",
"metadata": {},
"source": [
"#### Add volume metrics to dataframes"
]
},
{
"cell_type": "markdown",
"id": "09b6f305",
"metadata": {},
"source": [
"Adding volumetric info to grouped dataset"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "69a62218",
"metadata": {},
"outputs": [],
"source": [
"# re-arrage data into same format as our dataframe\n",
"df_vol_melt = df_vol.drop(columns=['SRLM', 'Cyst'])\n",
"df_vol_melt = df_vol_melt.melt(id_vars=['participant_id','hemisphere'], value_vars=subfield_category_order, value_name='volume')\n",
"df_vol_melt = df_vol_melt.set_axis(['participant_id', 'hemisphere', 'subfield','volume'], axis=1)\n",
"df_vol_melt = df_vol_melt.set_index(['participant_id', 'hemisphere', 'subfield'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "9942dada",
"metadata": {},
"outputs": [],
"source": [
"# arrange index of original dataframe so we can merge them both\n",
"df_Hemi_grouped = df_Hemi_grouped.reset_index()\n",
"df_Hemi_grouped = df_Hemi_grouped.set_index(['participant_id', 'hemisphere', 'subfield'])\n",
"df_Hemi_grouped= pd.concat([df_Hemi_grouped,df_vol_melt], axis=1, ignore_index=False)\n",
"df_Hemi_grouped = df_Hemi_grouped.reset_index()\n",
"#df_Hemi_grouped"
]
},
{
"cell_type": "markdown",
"id": "6b9b5951",
"metadata": {},
"source": [
"Add volume to subject level dataframe"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "64f5e68d",
"metadata": {},
"outputs": [],
"source": [
"df_vol_melID = df_vol_melt.groupby('participant_id').sum()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "abf1c278",
"metadata": {},
"outputs": [],
"source": [
"df_Subject_grouped = df_Subject_grouped.reset_index()\n",
"df_Subject_grouped = df_Subject_grouped.set_index(['participant_id'])\n",
"df_Subject_grouped= pd.concat([df_Subject_grouped,df_vol_melID], axis=1, ignore_index=False)\n",
"df_Subject_grouped = df_Subject_grouped.reset_index()\n",
"#df_Subject_grouped"
]
},
{
"cell_type": "markdown",
"id": "91855423",
"metadata": {},
"source": [
"### Melting dataframes"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "84f9ae11",
"metadata": {},
"outputs": [],
"source": [
"df_Hemi_grouped_melted = df_Hemi_grouped.melt(id_vars=['participant_id','group','sex','age', 'hemisphere','subfield'], value_vars=metrics)\n",
"#df_Hemi_grouped_melted"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "9a579fb2",
"metadata": {},
"outputs": [],
"source": [
"df_Subject_grouped_melted = df_Subject_grouped.melt(id_vars=['participant_id','group','sex','age'], value_vars=metrics)\n",
"#df_Subject_grouped_melted"
]
},
{
"cell_type": "markdown",
"id": "a4cbe12c",
"metadata": {},
"source": [
"## Plots Grouped by subject (averaged all voxel within a subject)"
]
},
{
"cell_type": "markdown",
"id": "8e560163",
"metadata": {},
"source": [
"Boxplots"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "aa85f24d",
"metadata": {},
"outputs": [
{
"data": {
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"config": {
"plotlyServerURL": "https://plot.ly"
},
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"source": [
"#| label: notebook_fig_hippocampus_metrics\n",
"# whole hippocampus: one row per participant (metric means over subfields and hemispheres; volume = summed subfield volumes, % of TIV)\n",
"metrics_grid = [\n",
" [\"volume\", \"FA\", \"MD\"],\n",
" [\"uFA\", \"Ki\", \"MTsat\"],\n",
"]\n",
"\n",
"fig = make_subplots(\n",
" rows=2, cols=3,\n",
" subplot_titles=[st.metric_axis_label(m) for row in metrics_grid for m in row],\n",
" horizontal_spacing=0.05,\n",
" vertical_spacing=0.08,\n",
")\n",
"\n",
"for r, row_metrics in enumerate(metrics_grid, start=1):\n",
" for c, metric in enumerate(row_metrics, start=1):\n",
" for group in group_order:\n",
" data_group = df_Subject_grouped[df_Subject_grouped[\"group\"] == group]\n",
" fig.add_trace(\n",
" go.Box(\n",
" x=data_group[\"group\"],\n",
" y=data_group[metric],\n",
" name=group,\n",
" legendgroup=group,\n",
" marker_color=group_colors[group],\n",
" showlegend=(r == 1 and c == 1),\n",
" width=0.6, # wider boxes since each has its own x slot\n",
" ),\n",
" row=r, col=c,\n",
" )\n",
"\n",
"fig.update_xaxes(showticklabels=False)\n",
"\n",
"fig.update_layout(\n",
" width=1300,\n",
" height=800,\n",
" boxmode='group',\n",
" boxgap=0.15,\n",
" margin=dict(l=60, r=180, t=60, b=40),\n",
" legend=dict(\n",
" orientation='v',\n",
" yanchor='top', y=1,\n",
" xanchor='left', x=1.02,\n",
" ),\n",
")\n",
"\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ccad4454",
"metadata": {},
"outputs": [],
"source": [
"#| label: notebook_figStatic_hippocampus_metrics\n",
"# whole hippocampus: one row per participant (metric means over subfields and hemispheres; volume = summed subfield volumes, % of TIV)\n",
"if create_Static_figures:\n",
" helpers.static_theme()\n",
" metrics_grid = [\"volume\", \"FA\", \"MD\", \"uFA\", \"Ki\", \"MTsat\"]\n",
" fig, axes = plt.subplots(2, 3, figsize=(helpers.TEXT_WIDTH_IN, 3.8))\n",
" for ax, metric in zip(axes.flatten(), metrics_grid):\n",
" sns.boxplot(ax=ax, data=df_Subject_grouped, y=metric, hue='group', palette=group_colors)\n",
" ax.set_ylabel(st.metric_axis_label(metric))\n",
" if ax is not axes[0, 2]:\n",
" ax.legend_.remove()\n",
" sns.move_legend(axes[0, 2], \"upper left\", bbox_to_anchor=(1, 1), title=\"Group\", frameon=False)\n",
" sns.despine(fig)\n",
" fig.tight_layout()\n",
" helpers.save_static_figure(fig, \"fig_hippocampus_metrics\")\n",
" plt.close(fig)\n",
" sns.set_theme()"
]
},
{
"cell_type": "markdown",
"id": "5d2aabb4",
"metadata": {},
"source": [
"### Reg lines"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "c277c76e",
"metadata": {},
"outputs": [
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variable=uFA
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"legendgroup": "FTD",
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{
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Group=FTD
variable=Ki
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"legendgroup": "FTD",
"marker": {
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{
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"legendgroup": "FTD",
"marker": {
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},
"mode": "markers",
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{
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R2=0.540417
Group=FTD
variable=MTsat
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"legendgroup": "FTD",
"marker": {
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"source": [
"#| label: notebook_fig_hippocampus_ageMetrics\n",
"fig = px.scatter(\n",
" df_Subject_grouped_melted,\n",
" x='age',\n",
" y='value',\n",
" color='group',\n",
" facet_col='variable',\n",
" facet_col_wrap=2,\n",
" category_orders={\n",
" 'group': group_order,\n",
" },\n",
" color_discrete_map=group_colors,\n",
" trendline='ols',\n",
" labels={\n",
" 'age': 'Age',\n",
" 'value': 'Value',\n",
" 'group': 'Group',\n",
" },\n",
")\n",
"\n",
"# sharey=False equivalent: let each facet have its own y-axis range\n",
"fig.update_yaxes(matches=None, showticklabels=True)\n",
"\n",
"# px.scatter + trendline creates a duplicate legend entry per group\n",
"# (one for points, one for the \"...,trendline\" line) — collapse to one\n",
"seen = set()\n",
"for trace in fig.data:\n",
" base_name = trace.name.split(',')[0]\n",
" trace.legendgroup = base_name\n",
" if 'trendline' in trace.name:\n",
" trace.showlegend = False\n",
" elif base_name in seen:\n",
" trace.showlegend = False\n",
" else:\n",
" trace.name = base_name\n",
" seen.add(base_name)\n",
"\n",
"# clean up facet titles (\"structure=Hippocampus\" -> \"Hippocampus\")\n",
"fig.for_each_annotation(lambda a: a.update(text=st.metric_axis_label(a.text.split('=')[-1])))\n",
"\n",
"fig.update_layout(\n",
" width=1100,\n",
" height=1600,\n",
" legend=dict(\n",
" orientation='v',\n",
" yanchor='middle', y=0.5,\n",
" xanchor='left', x=1.02,\n",
" ),\n",
")\n",
"\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "c4de9fdd",
"metadata": {},
"outputs": [],
"source": [
"#| label: notebook_figStatic_hippocampus_ageMetrics\n",
"if create_Static_figures:\n",
" helpers.static_theme()\n",
" # FacetGrid + regplot (same as lmplot) so the bootstrapped confidence bands can use a fixed seed\n",
" p = sns.FacetGrid(df_Subject_grouped_melted, hue='group', palette=group_colors, col=\"variable\", col_wrap=4,\n",
" sharey=False, height=1.55, aspect=1.0)\n",
" p.map_dataframe(sns.regplot, x='age', y='value', seed=0, scatter_kws={\"s\": 6})\n",
" p.add_legend(title=\"Group\")\n",
" for name, ax in p.axes_dict.items():\n",
" ax.set_title(st.metric_axis_label(name))\n",
" p.set_axis_labels(\"Age (years)\", \"\")\n",
" helpers.save_static_figure(p.figure, \"fig_hippocampus_ageMetrics\")\n",
" plt.close(p.figure)\n",
" sns.set_theme()"
]
},
{
"cell_type": "markdown",
"id": "fc521943",
"metadata": {},
"source": [
"## Plots grouped by subfield- hemishpere"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "efe97415",
"metadata": {},
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"#| label: notebook_fig_micrometricsSubfield_dropdown\n",
"qMRI_metrics = [\"volume\", \"FA\", \"MD\", \"uFA\", \"Ki\", \"MTsat\"]\n",
"subfield_order = subfield_category_order\n",
"\n",
"default_metric = \"uFA\"\n",
"default_idx = qMRI_metrics.index(default_metric)\n",
"\n",
"fig = go.Figure()\n",
"\n",
"traces_per_group = 2 # band + line\n",
"traces_per_metric = len(group_order) * traces_per_group # 4 groups * 2 = 8\n",
"\n",
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" for group in group_order:\n",
" data_group = df_Hemi_grouped[df_Hemi_grouped[\"group\"] == group]\n",
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" stats = (\n",
" data_group.groupby(\"subfield\")[metric]\n",
" .agg(mean=\"mean\", sem=\"sem\")\n",
" .reindex(subfield_order)\n",
" )\n",
" ci95 = 1.96 * stats[\"sem\"]\n",
" upper = stats[\"mean\"] + ci95\n",
" lower = stats[\"mean\"] - ci95\n",
"\n",
" color = group_colors[group]\n",
" is_default = (i == default_idx)\n",
"\n",
" # band (always showlegend=False, only visibility toggles)\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=list(subfield_order) + list(subfield_order)[::-1],\n",
" y=list(upper) + list(lower)[::-1],\n",
" fill='toself',\n",
" fillcolor=color,\n",
" opacity=0.2,\n",
" line=dict(width=0),\n",
" legendgroup=group,\n",
" showlegend=False,\n",
" hoverinfo='skip',\n",
" visible=is_default,\n",
" )\n",
" )\n",
"\n",
" # mean line\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=subfield_order,\n",
" y=stats[\"mean\"],\n",
" mode='lines',\n",
" name=group,\n",
" legendgroup=group,\n",
" line=dict(color=color),\n",
" showlegend=is_default,\n",
" visible=is_default,\n",
" )\n",
" )\n",
"\n",
"fig.update_xaxes(categoryorder='array', categoryarray=subfield_order)\n",
"\n",
"# build dropdown: for each metric, only its 8 traces visible;\n",
"# legend only shown on the 4 \"line\" traces (odd positions within each metric's block)\n",
"buttons = []\n",
"n_total = len(qMRI_metrics) * traces_per_metric\n",
"for i, metric in enumerate(qMRI_metrics):\n",
" visible_mask = [False] * n_total\n",
" showlegend_mask = [False] * n_total\n",
" start = i * traces_per_metric\n",
" for j in range(traces_per_metric):\n",
" visible_mask[start + j] = True\n",
" if j % 2 == 1: # line traces only (band=0, line=1, band=2, line=3, ...)\n",
" showlegend_mask[start + j] = True\n",
"\n",
" buttons.append(dict(\n",
" label=st.metric_name(metric),\n",
" method='update',\n",
" args=[\n",
" {'visible': visible_mask, 'showlegend': showlegend_mask},\n",
" {'yaxis': {'title': st.metric_axis_label(metric)}, 'title': f\"{st.metric_name(metric)} across subfields\"},\n",
" ],\n",
" ))\n",
"\n",
"fig.update_layout(\n",
" width=750,\n",
" height=500,\n",
" title=f\"{st.metric_name(default_metric)} across subfields\",\n",
" yaxis_title=st.metric_axis_label(default_metric),\n",
" margin=dict(l=60, r=200, t=80, b=60),\n",
" legend=dict(\n",
" orientation='v',\n",
" yanchor='middle', y=0.5,\n",
" xanchor='left', x=1.02,\n",
" ),\n",
" updatemenus=[dict(\n",
" type='dropdown',\n",
" buttons=buttons,\n",
" direction='down',\n",
" x=1.02, xanchor='left',\n",
" y=1.15, yanchor='top',\n",
" showactive=True,\n",
" active=default_idx,\n",
" )],\n",
")\n",
"\n",
"fig"
]
},
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"execution_count": 28,
"id": "c53c72bd",
"metadata": {},
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"source": [
"#| label: notebook_figStatic_micrometricsSubfield\n",
"if create_Static_figures:\n",
" helpers.static_theme()\n",
" metrics_grid = [\"volume\", \"FA\", \"MD\", \"uFA\", \"Ki\", \"MTsat\"]\n",
" fig, axes = plt.subplots(2, 3, figsize=(helpers.TEXT_WIDTH_IN, 3.8))\n",
" for ax, metric in zip(axes.flatten(), metrics_grid):\n",
" # band: mean ± 1.96 × standard error, as in the interactive figure and the caption\n",
" sns.lineplot(ax=ax, data=df_Hemi_grouped, x=\"subfield\", y=metric, hue='group', palette=group_colors,\n",
" errorbar=(\"se\", 1.96))\n",
" ax.set_ylabel(st.metric_axis_label(metric))\n",
" ax.set_xlabel(\"Subfield\")\n",
" if ax is not axes[0, 2]:\n",
" ax.legend_.remove()\n",
" sns.move_legend(axes[0, 2], \"upper left\", bbox_to_anchor=(1, 1), title=\"Group\", frameon=False)\n",
" sns.despine(fig)\n",
" fig.tight_layout()\n",
" helpers.save_static_figure(fig, \"fig_micrometricsSubfield\")\n",
" plt.close(fig)\n",
" sns.set_theme()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c8c59c41",
"metadata": {},
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"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
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"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
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"ternary": {
"aaxis": {
"gridcolor": "white",
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},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
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},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
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}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
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"yaxis": {
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"ticks": "",
"title": {
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"zerolinecolor": "white",
"zerolinewidth": 2
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}
},
"width": 1750,
"xaxis": {
"anchor": "y",
"categoryarray": [
"Sub",
"CA1",
"CA2",
"CA3",
"CA4",
"DG"
],
"categoryorder": "array",
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0.0,
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},
"xaxis2": {
"anchor": "y2",
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"xaxis3": {
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},
"yaxis": {
"anchor": "x",
"domain": [
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},
"yaxis2": {
"anchor": "x2",
"domain": [
0.0,
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},
"yaxis3": {
"anchor": "x3",
"domain": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| label: notebook_fig_macrometricsSubfield\n",
"macro_metrics = [\"thickness\", \"curvature\", \"gyrification\"]\n",
"subfield_order = subfield_category_order\n",
"\n",
"fig = make_subplots(\n",
" rows=1, cols=3,\n",
" subplot_titles=[st.metric_axis_label(m) for m in macro_metrics],\n",
" horizontal_spacing=0.06,\n",
")\n",
"\n",
"for c, metric in enumerate(macro_metrics, start=1):\n",
" for group in group_order:\n",
" data_group = df_Hemi_grouped[df_Hemi_grouped[\"group\"] == group]\n",
"\n",
" stats = (\n",
" data_group.groupby(\"subfield\")[metric]\n",
" .agg(mean=\"mean\", sem=\"sem\")\n",
" .reindex(subfield_order)\n",
" )\n",
" ci95 = 1.96 * stats[\"sem\"]\n",
" upper = stats[\"mean\"] + ci95\n",
" lower = stats[\"mean\"] - ci95\n",
"\n",
" color = group_colors[group]\n",
" show_legend = (c == 1) # matches seaborn keeping only one legend, though placed on axes[2] there\n",
"\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=list(subfield_order) + list(subfield_order)[::-1],\n",
" y=list(upper) + list(lower)[::-1],\n",
" fill='toself',\n",
" fillcolor=color,\n",
" opacity=0.2,\n",
" line=dict(width=0),\n",
" legendgroup=group,\n",
" showlegend=False,\n",
" hoverinfo='skip',\n",
" ),\n",
" row=1, col=c,\n",
" )\n",
"\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=subfield_order,\n",
" y=stats[\"mean\"],\n",
" mode='lines',\n",
" name=group,\n",
" legendgroup=group,\n",
" line=dict(color=color),\n",
" showlegend=show_legend,\n",
" ),\n",
" row=1, col=c,\n",
" )\n",
"\n",
"fig.update_xaxes(categoryorder='array', categoryarray=subfield_order)\n",
"\n",
"fig.update_layout(\n",
" width=1750,\n",
" height=500,\n",
" margin=dict(l=60, r=180, t=60, b=40),\n",
" legend=dict(\n",
" orientation='v',\n",
" yanchor='top', y=1,\n",
" xanchor='left', x=1.02,\n",
" ),\n",
")\n",
"\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "b2bcedcd",
"metadata": {},
"outputs": [],
"source": [
"#| label: notebook_figStatic_macrometricsSubfield\n",
"if create_Static_figures:\n",
" helpers.static_theme()\n",
" macro_metrics = [\"thickness\", \"curvature\", \"gyrification\"]\n",
" fig, axes = plt.subplots(1, 3, figsize=(helpers.TEXT_WIDTH_IN, 2.0))\n",
" for ax, metric in zip(axes, macro_metrics):\n",
" # band: mean ± 1.96 × standard error, as in the interactive figure and the caption\n",
" sns.lineplot(ax=ax, data=df_Hemi_grouped, x=\"subfield\", y=metric, hue='group', palette=group_colors,\n",
" errorbar=(\"se\", 1.96))\n",
" ax.set_ylabel(st.metric_axis_label(metric))\n",
" ax.set_xlabel(\"Subfield\")\n",
" if ax is not axes[2]:\n",
" ax.legend_.remove()\n",
" sns.move_legend(axes[2], \"upper left\", bbox_to_anchor=(1, 1), title=\"Group\", frameon=False)\n",
" sns.despine(fig)\n",
" fig.tight_layout()\n",
" helpers.save_static_figure(fig, \"fig_macrometricsSubfield\")\n",
" plt.close(fig)\n",
" sns.set_theme()"
]
},
{
"cell_type": "markdown",
"id": "e93e96d9",
"metadata": {},
"source": [
"## Stats"
]
},
{
"cell_type": "markdown",
"id": "067abfa0",
"metadata": {},
"source": [
"Stats follow same logical structure as the previous test. Do and Ancova (controling age + sex + hemisphere) for each combinaiton of metric and structure (subfield). \n",
"\n",
"Also corrected for multiple comparisons.\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "00edfbe8",
"metadata": {},
"outputs": [],
"source": [
"# Define general variables\n",
"subfields = df_Hemi_grouped[\"subfield\"].unique()\n",
"mc_alpha = 0.05\n",
"metrics_stats = metrics + ['volume']"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "397ed6a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total results collected: 270\n"
]
}
],
"source": [
"df_Hemi_grouped_ancova_subfield = st.ancova_tests_subfield(df_Hemi_grouped, metrics_stats, subfields, covar='age + hemisphere + sex')\n",
"\n",
"df_Hemi_grouped_ancova_subfield_Pcorrected = st.post_ancova_correctMultipleComparison(df_Hemi_grouped_ancova_subfield, mc_method='fdr_by', mc_alpha=mc_alpha, structure_col = 'subfield')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "71805ddb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" | Subfield | \n",
" Metric | \n",
" F(3, df_err) | \n",
" df_err | \n",
" ω²ₚ | \n",
" 95% CI lower | \n",
" p_adj | \n",
"
\n",
" \n",
" \n",
" \n",
" | CA1 | \n",
" FA | \n",
" 4.99 | \n",
" 93 | \n",
" 0.11 | \n",
" 0.01 | \n",
" 0.0387 | \n",
"
\n",
" \n",
" | CA1 | \n",
" MD | \n",
" 10.35 | \n",
" 93 | \n",
" 0.25 | \n",
" 0.12 | \n",
" 0.0001 | \n",
"
\n",
" \n",
" | CA1 | \n",
" MTsat | \n",
" 6.44 | \n",
" 87 | \n",
" 0.21 | \n",
" 0.08 | \n",
" 0.0084 | \n",
"
\n",
" \n",
" | CA1 | \n",
" Curvature | \n",
" 5.30 | \n",
" 93 | \n",
" 0.13 | \n",
" 0.03 | \n",
" 0.0279 | \n",
"
\n",
" \n",
" | CA1 | \n",
" µFA | \n",
" 10.29 | \n",
" 93 | \n",
" 0.24 | \n",
" 0.11 | \n",
" 0.0001 | \n",
"
\n",
" \n",
" | CA2 | \n",
" FA | \n",
" 4.87 | \n",
" 93 | \n",
" 0.11 | \n",
" 0.02 | \n",
" 0.0404 | \n",
"
\n",
" \n",
" | CA2 | \n",
" MD | \n",
" 14.14 | \n",
" 93 | \n",
" 0.30 | \n",
" 0.16 | \n",
" 4.0e-06 | \n",
"
\n",
" \n",
" | CA2 | \n",
" MTsat | \n",
" 11.07 | \n",
" 87 | \n",
" 0.28 | \n",
" 0.14 | \n",
" 7.8e-05 | \n",
"
\n",
" \n",
" | CA2 | \n",
" µFA | \n",
" 17.11 | \n",
" 93 | \n",
" 0.34 | \n",
" 0.21 | \n",
" 7.8e-07 | \n",
"
\n",
" \n",
" | CA3 | \n",
" MD | \n",
" 16.64 | \n",
" 93 | \n",
" 0.34 | \n",
" 0.20 | \n",
" 8.1e-07 | \n",
"
\n",
" \n",
" | CA3 | \n",
" MTsat | \n",
" 9.66 | \n",
" 87 | \n",
" 0.26 | \n",
" 0.12 | \n",
" 0.0003 | \n",
"
\n",
" \n",
" | CA3 | \n",
" µFA | \n",
" 15.85 | \n",
" 93 | \n",
" 0.33 | \n",
" 0.19 | \n",
" 1.3e-06 | \n",
"
\n",
" \n",
" | CA4 | \n",
" MD | \n",
" 14.68 | \n",
" 93 | \n",
" 0.32 | \n",
" 0.19 | \n",
" 2.7e-06 | \n",
"
\n",
" \n",
" | CA4 | \n",
" MTsat | \n",
" 5.72 | \n",
" 87 | \n",
" 0.18 | \n",
" 0.06 | \n",
" 0.0186 | \n",
"
\n",
" \n",
" | CA4 | \n",
" µFA | \n",
" 12.59 | \n",
" 93 | \n",
" 0.29 | \n",
" 0.16 | \n",
" 1.7e-05 | \n",
"
\n",
" \n",
" | DG | \n",
" MD | \n",
" 17.25 | \n",
" 93 | \n",
" 0.35 | \n",
" 0.21 | \n",
" 7.8e-07 | \n",
"
\n",
" \n",
" | DG | \n",
" µFA | \n",
" 9.29 | \n",
" 93 | \n",
" 0.24 | \n",
" 0.11 | \n",
" 0.0003 | \n",
"
\n",
" \n",
" | Sub | \n",
" MD | \n",
" 12.39 | \n",
" 93 | \n",
" 0.28 | \n",
" 0.15 | \n",
" 1.9e-05 | \n",
"
\n",
" \n",
" | Sub | \n",
" MTsat | \n",
" 8.55 | \n",
" 87 | \n",
" 0.28 | \n",
" 0.14 | \n",
" 0.0008 | \n",
"
\n",
" \n",
" | Sub | \n",
" Gyrification | \n",
" 4.90 | \n",
" 93 | \n",
" 0.08 | \n",
" 0.00 | \n",
" 0.0404 | \n",
"
\n",
" \n",
" | Sub | \n",
" µFA | \n",
" 15.36 | \n",
" 93 | \n",
" 0.33 | \n",
" 0.20 | \n",
" 1.7e-06 | \n",
"
\n",
" \n",
"
"
],
"text/markdown": [
"| Subfield | Metric | F(3, df_err) | df_err | ω²ₚ | 95% CI lower | p_adj |\n",
"|:-----------|:-------------|---------------:|---------:|------:|---------------:|--------:|\n",
"| CA1 | FA | 4.99 | 93 | 0.11 | 0.01 | 0.0387 |\n",
"| CA1 | MD | 10.35 | 93 | 0.25 | 0.12 | 0.0001 |\n",
"| CA1 | MTsat | 6.44 | 87 | 0.21 | 0.08 | 0.0084 |\n",
"| CA1 | Curvature | 5.3 | 93 | 0.13 | 0.03 | 0.0279 |\n",
"| CA1 | µFA | 10.29 | 93 | 0.24 | 0.11 | 0.0001 |\n",
"| CA2 | FA | 4.87 | 93 | 0.11 | 0.02 | 0.0404 |\n",
"| CA2 | MD | 14.14 | 93 | 0.3 | 0.16 | 4e-06 |\n",
"| CA2 | MTsat | 11.07 | 87 | 0.28 | 0.14 | 7.8e-05 |\n",
"| CA2 | µFA | 17.11 | 93 | 0.34 | 0.21 | 7.8e-07 |\n",
"| CA3 | MD | 16.64 | 93 | 0.34 | 0.2 | 8.1e-07 |\n",
"| CA3 | MTsat | 9.66 | 87 | 0.26 | 0.12 | 0.0003 |\n",
"| CA3 | µFA | 15.85 | 93 | 0.33 | 0.19 | 1.3e-06 |\n",
"| CA4 | MD | 14.68 | 93 | 0.32 | 0.19 | 2.7e-06 |\n",
"| CA4 | MTsat | 5.72 | 87 | 0.18 | 0.06 | 0.0186 |\n",
"| CA4 | µFA | 12.59 | 93 | 0.29 | 0.16 | 1.7e-05 |\n",
"| DG | MD | 17.25 | 93 | 0.35 | 0.21 | 7.8e-07 |\n",
"| DG | µFA | 9.29 | 93 | 0.24 | 0.11 | 0.0003 |\n",
"| Sub | MD | 12.39 | 93 | 0.28 | 0.15 | 1.9e-05 |\n",
"| Sub | MTsat | 8.55 | 87 | 0.28 | 0.14 | 0.0008 |\n",
"| Sub | Gyrification | 4.9 | 93 | 0.08 | 0 | 0.0404 |\n",
"| Sub | µFA | 15.36 | 93 | 0.33 | 0.2 | 1.7e-06 |"
],
"text/plain": [
"Subfield Metric F(3, df_err) df_err ω²ₚ 95% CI lower p_adj\n",
" CA1 FA 4.99 93 0.11 0.01 0.0387\n",
" CA1 MD 10.35 93 0.25 0.12 0.0001\n",
" CA1 MTsat 6.44 87 0.21 0.08 0.0084\n",
" CA1 Curvature 5.30 93 0.13 0.03 0.0279\n",
" CA1 µFA 10.29 93 0.24 0.11 0.0001\n",
" CA2 FA 4.87 93 0.11 0.02 0.0404\n",
" CA2 MD 14.14 93 0.30 0.16 4.0e-06\n",
" CA2 MTsat 11.07 87 0.28 0.14 7.8e-05\n",
" CA2 µFA 17.11 93 0.34 0.21 7.8e-07\n",
" CA3 MD 16.64 93 0.34 0.20 8.1e-07\n",
" CA3 MTsat 9.66 87 0.26 0.12 0.0003\n",
" CA3 µFA 15.85 93 0.33 0.19 1.3e-06\n",
" CA4 MD 14.68 93 0.32 0.19 2.7e-06\n",
" CA4 MTsat 5.72 87 0.18 0.06 0.0186\n",
" CA4 µFA 12.59 93 0.29 0.16 1.7e-05\n",
" DG MD 17.25 93 0.35 0.21 7.8e-07\n",
" DG µFA 9.29 93 0.24 0.11 0.0003\n",
" Sub MD 12.39 93 0.28 0.15 1.9e-05\n",
" Sub MTsat 8.55 87 0.28 0.14 0.0008\n",
" Sub Gyrification 4.90 93 0.08 0.00 0.0404\n",
" Sub µFA 15.36 93 0.33 0.20 1.7e-06"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#| label: notebook_table_hippocampus_ancovaResults_significant\n",
"cols = [\"subfield\", \"metric\", \"F value\", \"Df_residual\",\n",
" \"Omega2_partial\", \"CI_low.y\", \"pvals_adj\", \"level\"]\n",
"\n",
"df_sig = (\n",
" df_Hemi_grouped_ancova_subfield_Pcorrected\n",
" .query(\"pvals_adj < @mc_alpha\")\n",
" [cols]\n",
" .rename(columns={\n",
" \"F value\": \"F(3, df_err)\",\n",
" \"Df_residual\": \"df_err\",\n",
" \"Omega2_partial\": \"ω²ₚ\",\n",
" \"CI_low.y\": \"95% CI lower\",\n",
" \"pvals_adj\": \"p_adj\"\n",
" })\n",
" .sort_values([\"subfield\", \"metric\"])\n",
").reset_index(drop=True)\n",
"\n",
"st.display_table(st.format_table(df_sig, two_decimals=[\"F(3, df_err)\", \"ω²ₚ\", \"95% CI lower\"], p_values=[\"p_adj\"]))"
]
}
],
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"kernelspec": {
"display_name": "default",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
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