{ "cells": [ { "cell_type": "markdown", "id": "c5d5061e", "metadata": {}, "source": [ "# Notebook 1. Whole Brain Analysis" ] }, { "cell_type": "code", "execution_count": 1, "id": "13d0dea6", "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": "4b9cb7c8", "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", "from scipy.spatial import distance\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": "11fc112f", "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": "f8de88fb", "metadata": {}, "outputs": [], "source": [ "# Import auxiliary functions bundled in this notebook\n", "import ancova_assumptions as aa\n", "import stats_test as st\n", "import helpers\n" ] }, { "cell_type": "markdown", "id": "e33b9ac4", "metadata": {}, "source": [ "The next flag creates static figures that were used as a base for paper static version. Saved in static folder. If you want to create them again, set this variable to True. Otherwise, set it to False." ] }, { "cell_type": "code", "execution_count": 5, "id": "e0757c8c", "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": "e7b98450", "metadata": {}, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 6, "id": "7b876785-8c16-4b4e-804b-07802ebf1100", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(helpers.data_path(\"hyades_wholeBrain_ROIs.csv\"))" ] }, { "cell_type": "code", "execution_count": 7, "id": "bb75fa6f", "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": "0aaa03f6", "metadata": {}, "source": [ "## Wrangling" ] }, { "cell_type": "code", "execution_count": 8, "id": "d7b9d957", "metadata": {}, "outputs": [], "source": [ "# rename column from dataframe (just for stylec onsitency with paper)\n", "df = df.rename(columns={'D_dki': 'MD'})\n", "\n", "# normalized_volume is the ROI volume as a fraction of total intracranial volume (TIV); express it as % of TIV\n", "# (same units as the hippocampal subfield volumes; F statistics, effect sizes and p-values are unaffected)\n", "df['normalized_volume'] = df['normalized_volume'] * 100" ] }, { "cell_type": "code", "execution_count": 9, "id": "b29fb36f-cdc0-4b75-bfe3-b5849c50d43f", "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": 10, "id": "5d791bf9", "metadata": {}, "outputs": [], "source": [ "# Drop subjects in the exclusion list\n", "df = df.query(\"participant_id != @exclusion_list\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "745fd1cb", "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": 12, "id": "27a588d3", "metadata": {}, "outputs": [], "source": [ "# Drop \"unkown\" label structre \n", "df = df.query(\"structure != 'unknown'\")" ] }, { "cell_type": "markdown", "id": "1be3111a", "metadata": {}, "source": [ "## Extract features to study" ] }, { "cell_type": "code", "execution_count": 13, "id": "190359f8", "metadata": {}, "outputs": [], "source": [ "metadata = [\"participant_id\", \"group\", \"age\",'sex',\"structure\"]\n", "#metrics = [\"FA\",\"D_uFA\", \"uFA\", \"Ki\", \"Vol_mm3_norm\", \"MTsat\"]\n", "metrics = [\"FA\",\"MD\", \"uFA\", \"Ki\", \"MTsat\", \"normalized_volume\"]\n", "df_brain_grouped = df[metadata + metrics]" ] }, { "cell_type": "code", "execution_count": 14, "id": "660e63c5", "metadata": {}, "outputs": [], "source": [ "# Define the order of the structures for plotting\n", "structure_order = [\"Parietal\", \"Frontal\", \"Temporal\", \"Occipital\", \"Cingulate\", \"Insula\", \"Hippocampus\", \"Amygdala\"];" ] }, { "cell_type": "markdown", "id": "856dcd33", "metadata": {}, "source": [ "## " ] }, { "cell_type": "markdown", "id": "28448063", "metadata": {}, "source": [ "## Mahallanobis distance by metric " ] }, { "cell_type": "code", "execution_count": 15, "id": "2a0de217", "metadata": {}, "outputs": [], "source": [ "def mahalanobis_distance_byMetrics(df, group2compare = \"HC\", metrics=None):\n", " \"\"\"\n", " Calculates the Mahalanobis distance of each subject to the distribution of the 'group2compare' group\n", " for each unique structure, using the columns in the metrics list.\n", "\n", " Parameters\n", " ----------\n", " df : pd.DataFrame\n", " DataFrame containing participant data.\n", " group2compare : str\n", " Column name for the group to compare with.\n", " metrics : str\n", " List of metrics to use to calculate the distance.\n", "\n", " Returns\n", " -------\n", " pd.DataFrame\n", " DataFrame with original data and the Mahalanobis distance for each structure to the group2compare group.\n", " \"\"\"\n", " \n", " group_col='group'\n", " structure_col='structure'\n", "\n", " results = []\n", " \n", " for structure in df[structure_col].unique():\n", " df_struct = df[df[structure_col] == structure].copy()\n", " group2compare_df = df_struct[df_struct[group_col] == group2compare][metrics]\n", " if len(group2compare_df) < 2:\n", " continue # Skip if not enough subjects for covariance\n", " group2compare_mean = group2compare_df.mean().values\n", " group2compare_cov = np.cov(group2compare_df, rowvar=False)\n", " group2compare_cov_inv = np.linalg.pinv(group2compare_cov)\n", " def compute_md(row):\n", " return distance.mahalanobis(row[metrics], group2compare_mean, group2compare_cov_inv)\n", " df_struct['mahalanobis_distance'] = df_struct.apply(compute_md, axis=1)\n", " \n", " results.append(df_struct)\n", " return pd.concat(results, ignore_index=True)\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "b1416e92", "metadata": {}, "outputs": [], "source": [ "df_brain_grouped_mahallanobis = mahalanobis_distance_byMetrics(df_brain_grouped, group2compare = \"HC\", metrics=metrics)" ] }, { "cell_type": "code", "execution_count": 17, "id": "42e71f81", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "Group=HC
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"Insula", "Hippocampus", "Amygdala" ], "categoryorder": "array", "domain": [ 0.0, 1.0 ], "title": { "text": "ROI" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Mahalanobis distance" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#| label: notebook_fig_brainRois_MhD_boxplots\n", "fig = px.box(\n", " df_brain_grouped_mahallanobis,\n", " x='structure',\n", " y='mahalanobis_distance',\n", " color='group',\n", " category_orders={\n", " 'structure': structure_order,\n", " 'group': group_order,\n", " },\n", " color_discrete_map=group_colors,\n", " labels={\n", " 'structure': 'ROI',\n", " 'mahalanobis_distance': 'Mahalanobis distance',\n", " 'group': 'Group'\n", " },\n", ")\n", "\n", "# fig.update_layout(\n", "# width=2000,\n", "# height=600,\n", " \n", "# legend=dict(\n", "# orientation='v',\n", "# yanchor='middle', y=0.5,\n", "# xanchor='left', x=1.02,\n", "# ),\n", "# boxmode='group',\n", "# )\n", "\n", "fig" ] }, { "cell_type": "code", "execution_count": 18, "id": "efd12ce0", "metadata": {}, "outputs": [], "source": [ "#| label: notebook_figStatic_brainRois_MhD_boxplots\n", "if create_Static_figures:\n", " helpers.static_theme()\n", " fig, ax = plt.subplots(figsize=(helpers.TEXT_WIDTH_IN, 2.8))\n", " sns.boxplot(data=df_brain_grouped_mahallanobis, x='structure', y=\"mahalanobis_distance\", hue=\"group\",\n", " order=structure_order, palette=group_colors, ax=ax)\n", " sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 1), title=\"Group\", frameon=False)\n", " ax.set_xlabel(\"ROI\")\n", " ax.set_ylabel(\"Mahalanobis distance\")\n", " sns.despine(fig)\n", " fig.tight_layout()\n", " helpers.save_static_figure(fig, \"fig_brainRois_MhD_boxplots\")\n", " plt.close(fig)\n", " sns.set_theme()" ] }, { "cell_type": "markdown", "id": "f1619a51", "metadata": {}, "source": [ "### Plotting maha vs age" ] }, { "cell_type": "code", "execution_count": 19, "id": "bf6b0ca6", "metadata": {}, "outputs": [], "source": [ "df_brain_grouped_mahallanobis_melted = pd.melt(df_brain_grouped_mahallanobis, id_vars=['participant_id','group','age','sex','structure'], value_vars='mahalanobis_distance',\n", " var_name='metric', value_name='value')\n", "\n", "#df_brain_grouped_mahallanobis_melted" ] }, { "cell_type": "code", "execution_count": 20, "id": "ce657365", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Group=HC
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structure=Frontal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "WBhCClk+A0DITKtZviwDQKi1ffiICQNA+FK55rjUAkBohyI2HsMCQNi7i4WDsQJAuCReJE6OAkAIwpkSflkCQAjCmRJ+WQJA6SpssUg2AkDJkz5QExMCQDnIp594AQJA2gIffNiXAUA=", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "Group=LBD
structure=Frontal
Age=%{x}
Value=%{y}", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "markers", "name": "LBD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "Qk1MRUtNSEpHSA==", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "FO17FZkLEkAAAAAAAAD4f8TXI/z0MQZAaxBLcMMDGUCTGIJtM8ADQDPD0o3nyQ5APcY4ZFphGEC2eax8RooSQJEstfCE2hVAfmfE1d0pBEA=", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "OLS trendline
value = -0.218055 * age + 20.0825
R2=0.265373

Group=LBD
structure=Frontal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "kHLLFHfDFkCyW7PHmSUUQMih+D4GZxJA1ESberyHEUDURJt6vIcRQNgVweNRkg9A8FsGW77TDUAAokvSKhUMQBjokEmXVgpA", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "Group=FTD
structure=Frontal
Age=%{x}
Value=%{y}", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "markers", "name": "FTD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "RT9JPDBGRktRQUFCQg==", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "OugyREk4HUAZKDyIKRMhQLqzzeJAsBNAlnIazQqy/D/tMZWavSQJQAAAAAAAAPh/iqLwDekfIECuAsTYHvsPQJYXoz+jVxpAK5BB56SWEkAtKFymagMXQAgzKezS3QhAr+/VBPU9EkA=", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "OLS trendline
value = 0.0983851 * age + -1.3659
R2=0.148731

Group=FTD
structure=Frontal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "qjnUDEvaCkDsgQpkGiYSQDGbcptXVBNAC6xiwNUdFEALrGLA1R0UQHe02tKUghRAd7Ta0pSCFEC9zUIK0rAVQCnWuhyRFRZAb+8iVM5DF0BIABN5TA0YQNQy4+fGaRpA", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "Group=AD
structure=Frontal
Age=%{x}
Value=%{y}", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "markers", "name": "AD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "SDxFOExWUDVIR0ZSST8=", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "TWlPG3gxIkAAAAAAAAD4f2VZZJ5KZRVAX8usa8f8HUBGENY6Fr0dQLPn0WwKdCBAcG7Hr6bYGUAyyLJ/h8oYQKZdqvIzhhhAkNH6FOKXGUB8c+FJsYcWQMvJgk35/BRAbYifP5BxBkD/fMMdWqEgQA==", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "OLS trendline
value = -0.0089045 * age + 7.15472
R2=0.002632

Group=AD
structure=Frontal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x8", "y": { "bdata": "eEt6aSy7GkC/TLuh0Z8aQLlPqM/9XxpARlIqQEgpGkCzUpX9KSAaQCBTALsLFxpAjVNreO0NGkCNU2t47Q0aQPpT1jXPBBpAQVUXbnTpGUD0VsNj+8QZQM5Xmd6+shlAgllF1EWOGUA=", "dtype": "f8" }, "yaxis": "y8" }, { "hovertemplate": "Group=HC
structure=Temporal
Age=%{x}
Value=%{y}", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "markers", "name": "HC", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "P0RGTzhJPkFESDo3PQ==", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "vDb86X/Y8z94msDKgJkEQFsrN4MbGPA/spHzGklGBEAD6bm8s8YEQJXJ/Q0X7Ps/Gev1QQdV/j/tZm+35qD+P0gttFx9wQFA6a5xM7UTCEDZEjUcG8YDQIHev26ULAhA9Rr3o0Z0CUA=", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "OLS trendline
value = -0.0216373 * age + 3.67721
R2=0.052263

Group=HC
structure=Temporal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "+jXe87XlA0Cw363IZbkDQBwzTXLFYANAPjC88NTbAkD02YvFhK8CQKqDW5o0gwJAFtf6Q5QqAkA41GnCo6UBQDjUacKjpQFApCcJbANNAUAQe6gVY/QAQMYkeOoSyABAFj6szmN8/z8=", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "Group=LBD
structure=Temporal
Age=%{x}
Value=%{y}", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "markers", "name": "LBD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "Qk1MRUtNSEpHSA==", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "BDmzZctMD0AAAAAAAAD4f6XSH9ohLQpAbEN2juutJUAJfKezr4kTQO3sqy42IQlAz4VabF3cDUDBLNn9Q5ALQEXGqwgFDBpAbUe11re4A0A=", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "OLS trendline
value = -0.314847 * age + 27.4984
R2=0.181639

Group=LBD
structure=Temporal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "1NXujrDfGkDorBB7ehgXQEg8J8OrkxRA+IMyZ0RRE0D4gzJnRFETQFwTSa91zBBAGLaophwUD0B4Rb/uTY8MQNjU1TZ/CgpA", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "Group=FTD
structure=Temporal
Age=%{x}
Value=%{y}", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "markers", "name": "FTD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "RT9JPDBGRktRQUFCQg==", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "MlTf4MTNFEDgAznNI/EbQKN6LWtd/hdANAVGl++mFEBW+YXjZm8PQAAAAAAAAPh/EfDi+ZSwFkAzyuv9+SQRQBuTF52RWxNAynRhZs06DkBQAjRcR9wTQM1rsIoQsRdAb1uw9033F0A=", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "OLS trendline
value = 0.0174539 * age + 4.06274
R2=0.023031

Group=FTD
structure=Temporal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "rnzrNiSaE0Ck6vSVnXAUQCFGt+07phRAH4Pj0vrJFEAfg+PS+skUQJ6heUXa2xRAnqF5RdrbFEAc/TudeBEVQJob0g9YIxVAGHeUZ/ZYFUAWtMBMtXwVQBBrRfzx5xVA", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "Group=AD
structure=Temporal
Age=%{x}
Value=%{y}", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "markers", "name": "AD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "SDxFOExWUDVIR0ZSST8=", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "L+bFO50nJUAAAAAAAAD4f+8Lr+GRxRxAswHN+aipGkCVucb7krwXQBXiERNRxyNAgx5aRmhhIECZfi3bRowVQKUCyeobHxtAwAA2nKIaI0A9gwYGSLoZQIG1TOr/hhpAMqdKMPpEI0BOMrr5Pe8gQA==", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "OLS trendline
value = 0.0704394 * age + 2.79379
R2=0.155437

Group=AD
structure=Temporal
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x5", "y": { "bdata": "nZ1VTbobGkBpbBcQHvQaQPBOhtYG7RxAiOwJXM6dHkB2MfWc7+UeQGZ24N0QLh9AVLvLHjJ2H0BUu8seMnYfQEIAt19Tvh9Ah2c8kVtLIEBl8RITntsgQFM2/lO/IyFAMcDU1QG0IUA=", "dtype": "f8" }, "yaxis": "y5" }, { "hovertemplate": "Group=HC
structure=Occipital
Age=%{x}
Value=%{y}", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "markers", "name": "HC", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "P0RGTzhJPkFESDo3PQ==", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "QV6eFpP8AEAbQtg/W1gGQLZzM/RpHvk/Dd0pAxpqB0AKBuWKI68IQNqJr0LjjPs/NKOzNgX0AkDzyYzaqIUAQACpxzCWJfQ/J8h6094zBEC//wap6YoBQMluTGfcgQdApb0LMcOwAUA=", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "OLS trendline
value = -0.0192475 * age + 3.54942
R2=0.059830

Group=HC
structure=Occipital
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "DRXAFCztA0CmDxHfwMUDQNoEs3PqdgNApvSl0qgAA0A/7/acPdkCQNjpR2fSsQJADN/p+/tiAkDYztxauuwBQNjO3Fq67AFACsR+7+OdAUA+uSCEDU8BQNezcU6iJwFAcJNXDB87AEA=", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "Group=LBD
structure=Occipital
Age=%{x}
Value=%{y}", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "markers", "name": "LBD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "Qk1MRUtNSEpHSA==", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "8YFmAnhpBEAAAAAAAAD4f6kZ76TZ3AxAE2Wg+bJJFEClXcQE1aMKQIDDe+wPoxdAzEFOrYp4BUBiexFjsYoKQJm6xRRCUA9AR+ZPtMVnAUA=", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "OLS trendline
value = 0.119035 * age + -5.00544
R2=0.118799

Group=LBD
structure=Occipital
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "FiEWm5vOBkASrPuq9akJQLYI6gqHkQtACjfhuk+FDEAKN+G6T4UMQLKTzxrhbA5ABsLGyqlgD0At+F49OSoQQFePWpUdpBBA", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "Group=FTD
structure=Occipital
Age=%{x}
Value=%{y}", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "markers", "name": "FTD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "RT9JPDBGRktRQUFCQg==", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "tAuoXgIZGEA8mnmlqqUVQEDKDcs8LhdAK43xCIxODkD12u/UG4AOQAAAAAAAAPh/kWMriKDsFkBvPm5C3BAUQAnEGQ87pgNAh9LsZxEtAEB7P0JF6W0DQPrRxYDuSglAlnyRxERjBEA=", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "OLS trendline
value = 0.0206446 * age + 2.63837
R2=0.012690

Group=FTD
structure=Occipital
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "XBqx89EIDUCwa3d3LgQPQAYAaZgFgw9APri0A5XXD0A+uLQDldcPQC1KrVzuABBALUqtXO4AEEBYFCbtWUAQQGYC+cd9VRBAkMxxWOmUEECtqBcOMb8QQAI9CS8IPhFA", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "Group=AD
structure=Occipital
Age=%{x}
Value=%{y}", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "markers", "name": "AD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "SDxFOExWUDVIR0ZSST8=", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "86XcrDVVFEAAAAAAAAD4fya5RXubBBRA9+nBqoCR+z+XSkwZwv8SQCAB0PE54SJAFAunpgz9CUDlg+6/gdYQQCiggAuLfwlAtqYrBgmaKEC6Ug8i+yQJQGxdxEt+fhRAWraGAabn/T9u7xF6/RgYQA==", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "OLS trendline
value = 0.092926 * age + -1.58955
R2=0.088393

Group=AD
structure=Occipital
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x6", "y": { "bdata": "FhmeHyuvCkCuCm8pG+oMQN5JK6AlDxFAdzv8qRVKE0BlDp+rPakTQFThQa1lCBRAQ7Tkro1nFEBDtOSujWcUQDKHh7C1xhRA/v9vtS3kFUC5S/u7zWAXQJbxQL8dHxhAUT3Mxb2bGUA=", "dtype": "f8" }, "yaxis": "y6" }, { "hovertemplate": "Group=HC
structure=Cingulate
Age=%{x}
Value=%{y}", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "markers", "name": "HC", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "P0RGTzhJPkFESDo3PQ==", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "Y+W4fVZE8D9/7mUFXEEFQIAo6WT2K/k/EbqVherMBkCpqvOpRz4HQNd/S26GqPk/VrX/Qw5Q8D+aaRP6yJUCQLV7J9sXFgVAMM6CXEk3+z8HZ8w5mEUFQFiQYPqenQhAwBfz4BOiCEA=", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "OLS trendline
value = -0.0258605 * age + 3.92938
R2=0.059390

Group=HC
structure=Cingulate
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "Ibox5HAOBEDC8mOFetkDQAJkyMeNbwNA5A1fq6rQAkCERpFMtJsCQCR/w+29ZgJAZvAnMNH8AUBHmr4T7l0BQEeavhPuXQFAiAsjVgH0AEDIfIeYFIoAQGm1uTkeVQBAWBLOAbAu/j8=", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "Group=LBD
structure=Cingulate
Age=%{x}
Value=%{y}", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "markers", "name": "LBD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "Qk1MRUtNSEpHSA==", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "eh6wFbtvAEAAAAAAAAD4f9xTOjx5ZwRABwnLgx6pE0CiS3v7pD0HQGmSE+/TghRAhEhOY6WrCUBxfozH7mP/P0NBgh0kSQpA+29cIKHuBkA=", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "OLS trendline
value = 0.0538339 * age + -0.691521
R2=0.028101

Group=LBD
structure=Cingulate
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "9J6cAGTkBkAelfx1JS8IQDs5526mCwlASYtc6+Z5CUBJi1zr5nkJQGYvR+RnVgpAdIG8YKjECkCC0zHd6DILQJAlp1kpoQtA", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "Group=FTD
structure=Cingulate
Age=%{x}
Value=%{y}", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "markers", "name": "FTD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "RT9JPDBGRktRQUFCQg==", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "cWR7ACeXHED6TeEh2twRQJ48GgKYZQxAj2iUXgzXBUBC3WYO6wwPQAAAAAAAAPh/i1nRPJOtGUCtxW8EIu8bQLc8hJ5UbhhAzmxR5Pc6BUDM0CtdTbYHQBP/EZb2HgRAb+xODdKeCUA=", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "OLS trendline
value = 0.109791 * age + -2.94354
R2=0.255597

Group=FTD
structure=Cingulate
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "qmL7IZGcAkD6wD4szSYNQI6Yzy5cyQ9AIxSYbYjFEEAjFJhtiMUQQBE4sJj1NRFAETiwmPU1EUDbo/gZPYcSQMnHEEWq9xJAkzNZxvFIFEBve4kczCkVQANTGh9bzBdA", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "Group=AD
structure=Cingulate
Age=%{x}
Value=%{y}", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "markers", "name": "AD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "SDxFOExWUDVIR0ZSST8=", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "878UJ3nfEUAAAAAAAAD4f/uF3Iyk4QRAw5ojvJOJB0Cd6QrR/gkZQHFiYZgJzCBA77PPSh8XEUBV/I/Q3rAUQDG7js3q4RNAKlAUQcFFEkDAtazYqTcDQFoOeRrWgRRAcIF0uC2BAEBf8EQIZRUYQA==", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "OLS trendline
value = 0.0675261 * age + -0.23467
R2=0.129032

Group=AD
structure=Cingulate
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x3", "y": { "bdata": "/uI89/HACkAZjv9J018MQK2OjYXwExBAyTlQ2NGyEUBOVhtm9/cRQNJy5vMcPRJAV4+xgUKCEkBXj7GBQoISQNurfA9oxxJAaQHeuNiWE0B8cwrwbqsUQIWsoAu6NRVAlx7NQlBKFkA=", "dtype": "f8" }, "yaxis": "y3" }, { "hovertemplate": "Group=HC
structure=Insula
Age=%{x}
Value=%{y}", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "markers", "name": "HC", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "P0RGTzhJPkFESDo3PQ==", "dtype": "i1" }, "xaxis": "x4", "y": { "bdata": "EXhiZbuD/D+d33zMrWQAQB71z/PmDQFAKGTtHaj7AkBfDEQtwoQGQPWEGfW6YQBAqmClh4qOA0Bsd85ZMqoCQIX+qgeUSgFAsMhph79cCEAjDmlHlxT/P1DQBoD/rQhANEvZC2uv/j8=", "dtype": "f8" }, "yaxis": "y4" }, { "hovertemplate": "OLS trendline
value = -0.00940468 * age + 2.93248
R2=0.024757

Group=HC
structure=Insula
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x4", "y": { "bdata": "GAuU019SA0C43V4QHT8DQPiC9ImXGANA2PpUQM/eAkB4zR99jMsCQBig6rlJuAJAWEWAM8SRAkA4veDp+1cCQDi94On7VwJAeGJ2Y3YxAkC4Bwzd8AoCQFja1hmu9wFAGMqXhh2EAUA=", "dtype": "f8" }, "yaxis": "y4" }, { "hovertemplate": "Group=LBD
structure=Insula
Age=%{x}
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value = 0.201218 * age + -10.4425
R2=0.171662

Group=LBD
structure=Insula
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x4", "y": { "bdata": "nJc7T/mzBkAo9+9VQogLQIThEgVzwA5AWitSrkUuEEBaK1KuRS4QQIig4wVeyhFAIFusMWqYEkC2FXVddmYTQE7QPYmCNBRA", "dtype": "f8" }, "yaxis": "y4" }, { "hovertemplate": "Group=FTD
structure=Insula
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value = 0.0548787 * age + 1.52315
R2=0.070824

Group=FTD
structure=Insula
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x4", "y": { "bdata": "694zzBqhEEDcKk1KdEMTQNh906kK7BNA1F+C6W5cFEDUX4LpblwUQNTQWQmhlBRA1NBZCaGUFEDQI+BoNz0VQM6Ut4hpdRVAyuc96P8dFkDHyewnZI4WQL9v+eaQ3xdA", "dtype": "f8" }, "yaxis": "y4" }, { "hovertemplate": "Group=AD
structure=Insula
Age=%{x}
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value = 0.126026 * age + -3.72927
R2=0.242088

Group=AD
structure=Insula
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x4", "y": { "bdata": "E3wLVsaZB0B9V/qJE6AKQLwr6dZj1xBAKAfYCrHdE0C6K9XovV4UQEpQ0sbK3xRA3HTPpNdgFUDcdM+k12AVQG6ZzILk4RVAJAfEHAtlF0BsmbiUPmkZQJDislBYaxpA1nSnyItvHEA=", "dtype": "f8" }, "yaxis": "y4" }, { "hovertemplate": "Group=HC
structure=Hippocampus
Age=%{x}
Value=%{y}", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "markers", "name": "HC", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "P0RGTzhJPkFESDo3PQ==", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "cXMxBb2H+D9Jfdfj+00HQNQtH+PqRgRATf+ydd7sCUDqJEVg1wwBQF/hCHqBfP8/+8y2eB9O9D84U3jEcSgBQGgcrRIMuQJAYBI0oh0tCEC/f7gfyAD/P7sFVyQOeQZAIGLl289P/T8=", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "OLS trendline
value = 0.0398119 * age + -0.320344
R2=0.226717

Group=HC
structure=Hippocampus
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "HKgmT7Po/T9Yl0YkxYv+P9B1hs7o0f8/wyHzJo/dAEBhGYMRGC8BQP8QE/yggAFAOwAz0bIjAkAV5+KQTRgDQBXn4pBNGANAUtYCZl+7A0COxSI7cV4EQCy9siX6rwRA4IoSpS+ZBkA=", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "Group=LBD
structure=Hippocampus
Age=%{x}
Value=%{y}", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "markers", "name": "LBD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "Qk1MRUtNSEpHSA==", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "I6pXjdjvD0AAAAAAAAD4fxKpSxQ5HwJAhm0XuCgFHkCWU/OoEUMGQK5FTpYakxxAS3xWcMwdFUCkTT1XHM0GQLqBFFSq+AlA/hNSuz+5AEA=", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "OLS trendline
value = -0.0753363 * age + 9.58602
R2=0.016550

Group=LBD
structure=Hippocampus
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "z2M6no50EkAZY1TGH40RQKBiEOHV8hBAY2Ju7rClEEBjYm7usKUQQOlhKglnCxBAWsMQLYR8D0DgwsxHOuIOQGbCiGLwRw5A", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "Group=FTD
structure=Hippocampus
Age=%{x}
Value=%{y}", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "markers", "name": "FTD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "RT9JPDBGRktRQUFCQg==", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "nkMkr45ZF0AVeB0P9F0RQFX/v1U2twpAizEOcSCkDUDCD/4hHQsEQAAAAAAAAPh/dCPq+zKmF0BnxL+3qDwTQJBsAMc0ABVAKxuFxJaUBkAH0HX7/Wj6P0qqaHSedQlA5SHSEFg8AEA=", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "OLS trendline
value = 0.0909741 * age + -2.29035
R2=0.267661

Group=FTD
structure=Hippocampus
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "PlWHq3ucAEDokcgPQ1gJQBLh2Og0hwtA2hU5JNb7DEDaFTkk1vsMQD4w6cEmtg1APjDpwSa2DUBof/maGOUPQObMVJy0TxBAe/TciC1nEUDeDo0mfiESQAlenf9vUBRA", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "Group=AD
structure=Hippocampus
Age=%{x}
Value=%{y}", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "markers", "name": "AD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "SDxFOExWUDVIR0ZSST8=", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "i28d8YcyCUAAAAAAAAD4fwc2jhx/2xdAxrEC/knsBUCeSRJYfA8WQDtyVsBaCBlA9/LND+wpFkCRtvIewLIJQKFNnTijNQ5AgrhrUSoMCEBnygJO2PIDQO0FOe9l/h1AtZ/N/dZyBEDYM3sLa6oTQA==", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "OLS trendline
value = 0.105164 * age + -3.11071
R2=0.356478

Group=AD
structure=Hippocampus
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x", "y": { "bdata": "yNGlQTy0A0DC4ZaQXToGQLSxHp4AHgxA1GgAniGVEEDTQNPV0QARQNIYpg2CbBFA0fB4RTLYEUDR8HhFMtgRQNDIS33iQxJAzVDEJPOGE0DJsA8EtDUVQMdgtXMUDRZAw8AAU9W7F0A=", "dtype": "f8" }, "yaxis": "y" }, { "hovertemplate": "Group=HC
structure=Amygdala
Age=%{x}
Value=%{y}", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "markers", "name": "HC", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "P0RGTzhJPkFESDo3PQ==", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "0f/UadKb/D8vx+9dZG4DQLv0mNRjnwVASaorb0ZBB0BA6ViarjUAQIdVsToyIPE/QvNPi0OhAUBXw3i8cSMDQKXX81p9zf8/ZOFGds1TB0CWltKhd90BQAb6LkdRsAVAQ41JYjpwBEA=", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "OLS trendline
value = 0.00581315 * age + 1.92159
R2=0.006577

Group=HC
structure=Amygdala
Age=%{x}
Value=%{y} (trend)", "legendgroup": "HC", "marker": { "color": "#4C72B0", "symbol": "circle" }, "mode": "lines", "name": "HC", "showlegend": false, "type": "scatter", "x": { "bdata": "Nzg6PT4/QURERkhJTw==", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "qGUdDjfuAUBhjXvSHvoBQNPcN1vuEQJA/lNSqKU1AkC3e7BsjUECQHCjDjF1TQJA4fLKuURlAkAMauUG/IgCQAxq5Qb8iAJAfrmhj8ugAkDvCF4Ym7gCQKgwvNyCxAJA/h7xdvELA0A=", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "Group=LBD
structure=Amygdala
Age=%{x}
Value=%{y}", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "markers", "name": "LBD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "Qk1MRUtNSEpHSA==", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "lyvROIo7EEAAAAAAAAD4f6+xDwpMWA1AzsHjimYZG0A2LF7IoxgIQO0ov2QffhBAnXHoMUgP/D9sHs7ljbgQQOGZstDK6wBApBtcqDMICUA=", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "OLS trendline
value = -0.0884498 * age + 10.0537
R2=0.045026

Group=LBD
structure=Amygdala
Age=%{x}
Value=%{y} (trend)", "legendgroup": "LBD", "marker": { "color": "#DD8452", "symbol": "circle" }, "mode": "lines", "name": "LBD", "showlegend": false, "type": "scatter", "x": { "bdata": "QkVHSEhKS0xN", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "a+beHjzdEECeKb29CJsPQHpnZ2i+MA5AaIa8PZl7DUBohrw9mXsNQETEZuhOEQxAMOO7vSlcC0AeAhGTBKcKQAwhZmjf8QlA", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "Group=FTD
structure=Amygdala
Age=%{x}
Value=%{y}", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "markers", "name": "FTD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "RT9JPDBGRktRQUFCQg==", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "1Wyzt1vuFkATUXD0RvwhQGPX51azQhRAOmqaxumHDkCglcdaH1EOQAAAAAAAAPh/UAKpmqVBEkAAuPknjSUGQPfrldgZ2CJAx8n2TtnTCkB3zUxNAUYFQFcMzmfX+AlApDHTD85Q/T8=", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "OLS trendline
value = 0.0962351 * age + -1.82015
R2=0.108566

Group=FTD
structure=Amygdala
Age=%{x}
Value=%{y} (trend)", "legendgroup": "FTD", "marker": { "color": "#55A868", "symbol": "circle" }, "mode": "lines", "name": "FTD", "showlegend": false, "type": "scatter", "x": { "bdata": "MDw/QUFCQkVGSUtR", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "xJnRkZ9kBkAg78VlsqEPQDyCYY17+BBAWIlgdJK9EUBYiWB0kr0RQOcM4OcdIBJA5wzg5x0gEkCSl15CwEcTQCEb3rVLqhNAzKVcEO7RFEDqrFv3BJcVQEDCWKxJ5hdA", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "Group=AD
structure=Amygdala
Age=%{x}
Value=%{y}", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "markers", "name": "AD", "orientation": "v", "showlegend": false, "type": "scatter", "x": { "bdata": "SDxFOExWUDVIR0ZSST8=", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "c7besFbvA0AAAAAAAAD4f+C4JzEPLxpAlTM+eIv8EEBbCmW3c9EbQBcmdhrG0CNA4S6tEgfSGEAe6ElGNfkDQFW68SAjqwJAcjRG6kk1DEAUtkDF4EkQQOCQEKxj0CVAykcDzNx1CkB3jfflU6sXQA==", "dtype": "f8" }, "yaxis": "y2" }, { "hovertemplate": "OLS trendline
value = 0.18356 * age + -7.73182
R2=0.392208

Group=AD
structure=Amygdala
Age=%{x}
Value=%{y} (trend)", "legendgroup": "AD", "marker": { "color": "#C44E52", "symbol": "circle" }, "mode": "lines", "name": "AD", "showlegend": false, "type": "scatter", "x": { "bdata": "NTg/RUZHSEhJTFBSVg==", "dtype": "i1" }, "xaxis": "x2", "y": { "bdata": "sAcorR7z/z842j8hWmEEQMQjJvrdqA5AROi+xzm8E0CoNobpMHgUQA6FTQsoNBVAdNMULR/wFUB00xQtH/AVQNoh3E4WrBZACg0ytPvfGECgRk872M8bQGzj3X7GRx1AgI79gtEbIEA=", "dtype": "f8" }, "yaxis": "y2" } ], "layout": { "annotations": [ { "font": {}, "showarrow": false, "text": "Hippocampus", "x": 0.245, "xanchor": "center", "xref": "paper", "y": 0.1975, "yanchor": "bottom", "yref": "paper" }, { "font": {}, "showarrow": false, "text": "Amygdala", "x": 0.755, "xanchor": "center", "xref": "paper", "y": 0.1975, "yanchor": "bottom", "yref": "paper" }, { "font": {}, "showarrow": false, "text": "Cingulate", "x": 0.245, "xanchor": "center", "xref": "paper", "y": 0.465, "yanchor": "bottom", "yref": "paper" }, { "font": {}, "showarrow": false, "text": "Insula", "x": 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\"...,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=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": 21, "id": "2646b515", "metadata": {}, "outputs": [], "source": [ "#| label: notebook_figStatic_wholeBrain_ageMhD\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_brain_grouped_mahallanobis_melted, hue='group', palette=group_colors, col=\"structure\",\n", " col_wrap=4, col_order=structure_order, 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", " p.set_titles(\"{col_name}\")\n", " p.set_axis_labels(\"Age (years)\", \"Mahalanobis distance\")\n", " helpers.save_static_figure(p.figure, \"fig_wholeBrain_ageMhD\")\n", " plt.close(p.figure)\n", " sns.set_theme()" ] }, { "cell_type": "markdown", "id": "0355af64", "metadata": {}, "source": [ "## Visualization of metrics by region" ] }, { "cell_type": "code", "execution_count": 22, "id": "123c6695", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "legendgroup": "HC", "marker": { "color": "#4C72B0" }, "name": "HC", 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group]\n", " fig.add_trace(\n", " go.Box(\n", " x=data_group[\"structure\"],\n", " y=data_group[metric],\n", " name=group,\n", " legendgroup=group,\n", " offsetgroup=group,\n", " marker_color=group_colors[group],\n", " visible=(i == default_idx),\n", " showlegend=(i == default_idx),\n", " )\n", " )\n", "\n", "fig.update_xaxes(\n", " categoryorder='array',\n", " categoryarray=structure_order,\n", " #tickangle=-90,\n", ")\n", "\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", " start = i * traces_per_metric\n", " for j in range(traces_per_metric):\n", " visible_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': visible_mask},\n", " {'yaxis': {'title': st.metric_axis_label(metric)}, 'title': f\"Boxplot — {st.metric_name(metric)}\"},\n", " ],\n", " ))\n", "\n", "fig.update_layout(\n", " width=750,\n", " height=500,\n", " boxmode='group',\n", " boxgap=0.15,\n", " boxgroupgap=0.0,\n", " title=f\"Boxplot — {st.metric_name(default_metric)}\",\n", " yaxis_title=st.metric_axis_label(default_metric),\n", " yaxis_automargin=True,\n", " margin=dict(l=20, r=100, 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=.7, xanchor='left',\n", " y=1.15, yanchor='top',\n", " showactive=True,\n", " active=default_idx,\n", " )],\n", ")\n", "\n", "fig" ] }, { "cell_type": "code", "execution_count": 23, "id": "4e626c12", "metadata": {}, "outputs": [], "source": [ "#| label: notebook_figStatic_brainRois_metrics_boxplots\n", "if create_Static_figures:\n", " from matplotlib.patches import Patch\n", "\n", " helpers.static_theme()\n", " # A: quantitative MRI metrics; B: normalized volume, split into larger and smaller ROIs (own y scales)\n", " fig = plt.figure(figsize=(helpers.TEXT_WIDTH_IN, 7.0))\n", " subfig_a, subfig_b = fig.subfigures(2, 1, height_ratios=[3, 1.25])\n", " axes = subfig_a.subplots(nrows=3, ncols=2).flatten()\n", "\n", " qMRI_metrics = [\"FA\", \"MD\", \"uFA\", \"Ki\", \"MTsat\"]\n", " for i, metric in enumerate(qMRI_metrics):\n", " data_plot = df_brain_grouped[df_brain_grouped[\"MTsat\"].notna()] if metric == 'MTsat' else df_brain_grouped\n", " sns.boxplot(data=data_plot, x=\"structure\", y=metric, hue=\"group\", palette=group_colors, ax=axes[i],\n", " order=structure_order)\n", " axes[i].set_title(st.metric_axis_label(metric))\n", " axes[i].set_ylabel(\"\")\n", " axes[i].set_xlabel(\"\")\n", " # ROI names only on the lowest panel of each column (MTsat, Ki)\n", " if metric in (\"MTsat\", \"Ki\"):\n", " axes[i].tick_params(axis='x', rotation=45)\n", " else:\n", " axes[i].tick_params(axis='x', labelbottom=False)\n", " axes[i].get_legend().remove()\n", " # sixth slot: legend\n", " handles = [Patch(facecolor=group_colors[g], edgecolor=\"0.25\", label=g) for g in group_order]\n", " axes[5].axis(\"off\")\n", " axes[5].legend(handles=handles, title=\"Group\", loc=\"center\", frameon=False)\n", "\n", " metric = \"normalized_volume\"\n", " structures_plot = [['Parietal', 'Frontal', 'Temporal', 'Occipital'], ['Insula', 'Cingulate', 'Hippocampus', 'Amygdala']]\n", " axes_b = subfig_b.subplots(nrows=1, ncols=2)\n", " for ax, structures in zip(axes_b, structures_plot):\n", " data_plot = df_brain_grouped.query(\"structure in @structures\")\n", " sns.boxplot(data=data_plot, x=\"structure\", y=metric, hue=\"group\", palette=group_colors, ax=ax,\n", " order=structures, legend=False)\n", " ax.set_xlabel(\"\")\n", " ax.set_ylabel(\"\")\n", " axes_b[0].set_ylabel(st.metric_axis_label(metric))\n", "\n", " for subfig, letter in ((subfig_a, \"A\"), (subfig_b, \"B\")):\n", " subfig.text(0, 1, letter, fontsize=10, fontweight=\"bold\", va=\"top\", ha=\"left\")\n", " sns.despine(fig)\n", " subfig_a.subplots_adjust(left=0.08, right=0.97, top=0.95, bottom=0.12, hspace=0.35, wspace=0.18)\n", " subfig_b.subplots_adjust(left=0.08, right=0.97, top=0.88, bottom=0.15, wspace=0.18)\n", " helpers.save_static_figure(fig, \"fig_brainRois_metrics_boxplots\")\n", " plt.close(fig)\n", " sns.set_theme()" ] }, { "cell_type": "markdown", "id": "59fa87e0", "metadata": {}, "source": [ "## Stadistics" ] }, { "cell_type": "code", "execution_count": 25, "id": "9a7fc3be", "metadata": {}, "outputs": [], "source": [ "# Define general variables\n", "structures = df_brain_grouped[\"structure\"].unique()\n", "mc_alpha = 0.05" ] }, { "cell_type": "markdown", "id": "61152298", "metadata": {}, "source": [ "### Maha" ] }, { "cell_type": "code", "execution_count": 26, "id": "aa5187db", "metadata": {}, "outputs": [], "source": [ "# MhD transformation to stabilize variance in data for simple stats\n", "df_brain_grouped_mahallanobis['mahalanobis_distance_WilHil'] = df_brain_grouped_mahallanobis['mahalanobis_distance'].pow(1/3)\n", "df_brain_grouped_mahallanobis['mahalanobis_distance_log'] = np.log(df_brain_grouped_mahallanobis['mahalanobis_distance'])" ] }, { "cell_type": "code", "execution_count": 27, "id": "785d4ca6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 48 rows across 8 structure/metric combinations\n", "Total results collected: 8\n", "Total results collected: 16\n", "Total assumption checks run: 40 | Violations flagged: 1\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
structuremetrictest_nametermp_valueflag_violated
20Cingulatemahalanobis_distance_lognormality_of_residualsshapiro_wilk0.019164True
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" ], "text/plain": [ " structure metric test_name term \\\n", "20 Cingulate mahalanobis_distance_log normality_of_residuals shapiro_wilk \n", "\n", " p_value flag_violated \n", "20 0.019164 True " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#summary_ancovaAssumptions, raw = aa.ancova_assumption_tests(df_brain_grouped_mahallanobis, ['mahalanobis_distance'], structures, covar='age + sex')\n", "#summary_ancovaAssumptions, raw = aa.ancova_assumption_tests(df_brain_grouped_mahallanobis, ['mahalanobis_distance_WilHil'], structures, covar='age + sex')\n", "summary_ancovaAssumptions, raw = aa.ancova_assumption_tests(df_brain_grouped_mahallanobis, ['mahalanobis_distance_log'], structures, covar='age + sex')\n", "display(summary_ancovaAssumptions[summary_ancovaAssumptions[\"flag_violated\"] == True])" ] }, { "cell_type": "code", "execution_count": 28, "id": "b98e3b27", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.reset_defaults()\n", "plt.figure(figsize=(18, 8)) \n", "sns.set_theme(style=\"white\", font_scale=2)\n", "plt.rcParams['svg.fonttype'] = 'none'\n", "ax = sns.boxplot(data=df_brain_grouped_mahallanobis, x='structure', y=\"mahalanobis_distance_log\", hue=\"group\", order=structure_order)\n", "sns.move_legend(ax, 'center left' , bbox_to_anchor=(1, 0.5))\n", "# Code to adjust layout and show the plot\n", "ax.set_xticks(ax.get_xticks())\n", "ax.set_xticklabels(ax.get_xticklabels(), rotation=90) \n", "\n", "plt.tight_layout()\n", "#plt.savefig('../static/fig_boxplotMahaMetrics.svg')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "id": "730068c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 32\n" ] } ], "source": [ "# Run ANCOVA test for each structure and MhD, controlling for age + sex\n", "df_brain_grouped_ancova = st.ancova_tests(df_brain_grouped_mahallanobis, ['mahalanobis_distance_log'], structures, summary_ancovaAssumptions, covar='age + sex')\n", "# Perform multiple-comparison correction on the group variable results of the ANCOVA tests\n", "df_brain_grouped_ancova_Pcorrected = st.post_ancova_correctMultipleComparison(df_brain_grouped_ancova, mc_method='fdr_by', mc_alpha=mc_alpha)" ] }, { "cell_type": "code", "execution_count": 30, "id": "e095d9b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ROIMetricF(3, df_err)df_errω²ₚ95% CI lowerp_adjInteraction included
Temporallog MhD35.76410.700.563.5e-10—
Frontallog MhD18.58410.550.369.9e-07—
Parietallog MhD17.83410.540.341.1e-06—
Insulalog MhD10.99410.430.220.0001—
Cingulatelog MhD7.92410.340.120.0012—
Amygdalalog MhD6.29410.300.090.0047—
Hippocampuslog MhD4.72410.300.090.0174—
Occipitallog MhD5.36410.260.050.0103—
" ], "text/markdown": [ "| ROI | Metric | F(3, df_err) | df_err | ω²ₚ | 95% CI lower | p_adj | Interaction included |\n", "|:------------|:---------|---------------:|---------:|------:|---------------:|--------:|:-----------------------|\n", "| Temporal | log MhD | 35.76 | 41 | 0.7 | 0.56 | 3.5e-10 | — |\n", "| Frontal | log MhD | 18.58 | 41 | 0.55 | 0.36 | 9.9e-07 | — |\n", "| Parietal | log MhD | 17.83 | 41 | 0.54 | 0.34 | 1.1e-06 | — |\n", "| Insula | log MhD | 10.99 | 41 | 0.43 | 0.22 | 0.0001 | — |\n", "| Cingulate | log MhD | 7.92 | 41 | 0.34 | 0.12 | 0.0012 | — |\n", "| Amygdala | log MhD | 6.29 | 41 | 0.3 | 0.09 | 0.0047 | — |\n", "| Hippocampus | log MhD | 4.72 | 41 | 0.3 | 0.09 | 0.0174 | — |\n", "| Occipital | log MhD | 5.36 | 41 | 0.26 | 0.05 | 0.0103 | — |" ], "text/plain": [ " ROI Metric F(3, df_err) df_err ω²ₚ 95% CI lower p_adj Interaction included\n", " Temporal log MhD 35.76 41 0.70 0.56 3.5e-10 —\n", " Frontal log MhD 18.58 41 0.55 0.36 9.9e-07 —\n", " Parietal log MhD 17.83 41 0.54 0.34 1.1e-06 —\n", " Insula log MhD 10.99 41 0.43 0.22 0.0001 —\n", " Cingulate log MhD 7.92 41 0.34 0.12 0.0012 —\n", " Amygdala log MhD 6.29 41 0.30 0.09 0.0047 —\n", "Hippocampus log MhD 4.72 41 0.30 0.09 0.0174 —\n", " Occipital log MhD 5.36 41 0.26 0.05 0.0103 —" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#| label: notebook_table_MhD_ancovaResults_significant\n", "\n", "cols = [\"structure\", \"metric\", \"F value\", \"Df_residual\",\n", " \"Omega2_partial\", \"CI_low.y\", \"pvals_adj\", \"level\",\n", " \"violated_terms\"\n", " ]\n", "\n", "df_sig_maha = (\n", " df_brain_grouped_ancova_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", " \"violated_terms\": \"Interaction included\"\n", " })\n", " .sort_values([\"structure\", \"metric\"])\n", " #.round(4)\n", " .copy()\n", ").reset_index(drop=True)\n", "\n", "df_sig_maha[\"Interaction included\"] = df_sig_maha[\"Interaction included\"].replace(\"\", \"—\")\n", "\n", "st.display_table(st.format_table(df_sig_maha.sort_values(by='ω²ₚ', ascending=False), two_decimals=[\"F(3, df_err)\", \"ω²ₚ\", \"95% CI lower\"], p_values=[\"p_adj\"]))" ] }, { "cell_type": "markdown", "id": "9bea4b02", "metadata": {}, "source": [ "### ROIs" ] }, { "cell_type": "code", "execution_count": 31, "id": "c0cbd15b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 192\n" ] } ], "source": [ "# Run ANCOVA test for each structure and metric, controlling for age + sex\n", "df_brain_grouped_ancova = st.ancova_tests(df_brain_grouped, metrics, structures, summary_ancovaAssumptions, covar='age + sex')\n", "# Perform multiple-comparison correction on the group variable results of the ANCOVA tests\n", "df_brain_grouped_ancova_Pcorrected = st.post_ancova_correctMultipleComparison(df_brain_grouped_ancova, mc_method='fdr_by', mc_alpha=mc_alpha)" ] }, { "cell_type": "markdown", "id": "3d9803df", "metadata": {}, "source": [ "Check homogeneity of regression slopes to asses Ancova assumpsions" ] }, { "cell_type": "code", "execution_count": 32, "id": "cde6d116", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 288 rows across 48 structure/metric combinations\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 48\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 96\n", "Total assumption checks run: 240 | Violations flagged: 20\n" ] } ], "source": [ "summary_ancovaAssumptions, raw = aa.ancova_assumption_tests(df_brain_grouped, metrics, structures, covar='age + sex')" ] }, { "cell_type": "code", "execution_count": 33, "id": "002136bf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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ROIMetricAssumptionTestp
FrontalFAHomogeneity of regression slopesgroup × age interaction0.0444
FrontalMDHomogeneity of regression slopesgroup × age interaction0.0035
FrontalµFAHomogeneity of regression slopesgroup × age interaction0.0044
FrontalKiHomogeneity of regression slopesgroup × age interaction0.0152
TemporalµFAHomogeneity of regression slopesgroup × age interaction0.0278
TemporalKiHomogeneity of regression slopesgroup × age interaction0.0098
OccipitalKiHomogeneity of regression slopesgroup × age interaction0.0229
OccipitalMTsatHomogeneity of regression slopesgroup × sex interaction0.0482
CingulateMDHomogeneity of regression slopesgroup × age interaction0.0014
CingulateµFAHomogeneity of regression slopesgroup × age interaction0.0040
InsulaFAHomogeneity of regression slopesgroup × age interaction0.0289
HippocampusFAHomogeneity of regression slopesgroup × age interaction0.0198
HippocampusKiHomogeneity of regression slopesgroup × age interaction0.0175
AmygdalaFAHomogeneity of regression slopesgroup × age interaction0.0240
ParietalMDNormality of residualsShapiro–Wilk0.0456
ParietalKiNormality of residualsShapiro–Wilk0.0107
FrontalKiNormality of residualsShapiro–Wilk0.0088
OccipitalMDNormality of residualsShapiro–Wilk0.0005
AmygdalaMDNormality of residualsShapiro–Wilk0.0061
TemporalNormalized volumeHomogeneity of varianceLevene0.0116
" ], "text/markdown": [ "| ROI | Metric | Assumption | Test | p |\n", "|:------------|:------------------|:---------------------------------|:------------------------|-------:|\n", "| Frontal | FA | Homogeneity of regression slopes | group × age interaction | 0.0444 |\n", "| Frontal | MD | Homogeneity of regression slopes | group × age interaction | 0.0035 |\n", "| Frontal | µFA | Homogeneity of regression slopes | group × age interaction | 0.0044 |\n", "| Frontal | Ki | Homogeneity of regression slopes | group × age interaction | 0.0152 |\n", "| Temporal | µFA | Homogeneity of regression slopes | group × age interaction | 0.0278 |\n", "| Temporal | Ki | Homogeneity of regression slopes | group × age interaction | 0.0098 |\n", "| Occipital | Ki | Homogeneity of regression slopes | group × age interaction | 0.0229 |\n", "| Occipital | MTsat | Homogeneity of regression slopes | group × sex interaction | 0.0482 |\n", "| Cingulate | MD | Homogeneity of regression slopes | group × age interaction | 0.0014 |\n", "| Cingulate | µFA | Homogeneity of regression slopes | group × age interaction | 0.004 |\n", "| Insula | FA | Homogeneity of regression slopes | group × age interaction | 0.0289 |\n", "| Hippocampus | FA | Homogeneity of regression slopes | group × age interaction | 0.0198 |\n", "| Hippocampus | Ki | Homogeneity of regression slopes | group × age interaction | 0.0175 |\n", "| Amygdala | FA | Homogeneity of regression slopes | group × age interaction | 0.024 |\n", "| Parietal | MD | Normality of residuals | Shapiro–Wilk | 0.0456 |\n", "| Parietal | Ki | Normality of residuals | Shapiro–Wilk | 0.0107 |\n", "| Frontal | Ki | Normality of residuals | Shapiro–Wilk | 0.0088 |\n", "| Occipital | MD | Normality of residuals | Shapiro–Wilk | 0.0005 |\n", "| Amygdala | MD | Normality of residuals | Shapiro–Wilk | 0.0061 |\n", "| Temporal | Normalized volume | Homogeneity of variance | Levene | 0.0116 |" ], "text/plain": [ " ROI Metric Assumption Test p\n", " Frontal FA Homogeneity of regression slopes group × age interaction 0.0444\n", " Frontal MD Homogeneity of regression slopes group × age interaction 0.0035\n", " Frontal µFA Homogeneity of regression slopes group × age interaction 0.0044\n", " Frontal Ki Homogeneity of regression slopes group × age interaction 0.0152\n", " Temporal µFA Homogeneity of regression slopes group × age interaction 0.0278\n", " Temporal Ki Homogeneity of regression slopes group × age interaction 0.0098\n", " Occipital Ki Homogeneity of regression slopes group × age interaction 0.0229\n", " Occipital MTsat Homogeneity of regression slopes group × sex interaction 0.0482\n", " Cingulate MD Homogeneity of regression slopes group × age interaction 0.0014\n", " Cingulate µFA Homogeneity of regression slopes group × age interaction 0.0040\n", " Insula FA Homogeneity of regression slopes group × age interaction 0.0289\n", "Hippocampus FA Homogeneity of regression slopes group × age interaction 0.0198\n", "Hippocampus Ki Homogeneity of regression slopes group × age interaction 0.0175\n", " Amygdala FA Homogeneity of regression slopes group × age interaction 0.0240\n", " Parietal MD Normality of residuals Shapiro–Wilk 0.0456\n", " Parietal Ki Normality of residuals Shapiro–Wilk 0.0107\n", " Frontal Ki Normality of residuals Shapiro–Wilk 0.0088\n", " Occipital MD Normality of residuals Shapiro–Wilk 0.0005\n", " Amygdala MD Normality of residuals Shapiro–Wilk 0.0061\n", " Temporal Normalized volume Homogeneity of variance Levene 0.0116" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#| label: notebook_table_ancova_violations\n", "assumption_names = {\n", " \"homogeneity_of_slopes\": \"Homogeneity of regression slopes\",\n", " \"normality_of_residuals\": \"Normality of residuals\",\n", " \"homogeneity_of_variance\": \"Homogeneity of variance\",\n", "}\n", "test_names = {\n", " \"group:age\": \"group × age interaction\",\n", " \"group:sex\": \"group × sex interaction\",\n", " \"shapiro_wilk\": \"Shapiro–Wilk\",\n", " \"group\": \"Levene\",\n", "}\n", "\n", "df_violations = (\n", " summary_ancovaAssumptions\n", " .query(\"flag_violated == True\")\n", " .drop(columns=\"flag_violated\")\n", " .assign(test_name=lambda d: d[\"test_name\"].replace(assumption_names),\n", " term=lambda d: d[\"term\"].replace(test_names))\n", " .reset_index(drop=True)\n", ")\n", "\n", "st.display_table(st.format_table(df_violations, p_values=[\"p_value\"]))" ] }, { "cell_type": "code", "execution_count": 34, "id": "62e58700", "metadata": {}, "outputs": [], "source": [ "# Uncomment to visually inspect the normality assumption for violated cases\n", "#fig = aa.plot_normality_diagnostics(df, raw['normality'])\n", "#fig" ] }, { "cell_type": "code", "execution_count": 35, "id": "d8c9e66f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total results collected: 206\n" ] } ], "source": [ "# Run ANCOVA test for each structure and metric, controlling for age + sex\n", "df_brain_grouped_ancova = st.ancova_tests(df_brain_grouped_mahallanobis, metrics, structures, summary_ancovaAssumptions, covar='age + sex')\n", "# Perform multiple-comparison correction on the group variable results of the ANCOVA tests\n", "df_brain_grouped_ancova_Pcorrected = st.post_ancova_correctMultipleComparison(df_brain_grouped_ancova, mc_method='fdr_by', mc_alpha=mc_alpha)" ] }, { "cell_type": "code", "execution_count": 36, "id": "307a8d3f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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ROIMetricF(3, df_err)df_errω²ₚ95% CI lowerp_adjInteraction included
AmygdalaMD5.36440.240.050.0374—
AmygdalaMTsat6.24410.360.140.0263—
AmygdalaNormalized volume5.41440.230.040.0374—
CingulateFA5.68440.300.100.0342—
CingulateMD8.30410.340.120.0085group:age
FrontalMD9.92410.350.140.0035group:age
FrontalµFA5.41410.200.020.0374group:age
HippocampusMD5.60440.260.070.0346—
HippocampusMTsat5.90410.340.120.0316—
HippocampusµFA7.47440.340.130.0102—
InsulaMD7.89440.320.120.0091—
ParietalMD7.50440.280.080.0102—
ParietalMTsat6.16410.330.110.0263—
ParietalNormalized volume9.05440.330.130.0047—
TemporalKi6.46410.250.050.0262group:age
TemporalMD13.71440.460.260.0004—
TemporalNormalized volume10.80440.380.170.0021—
TemporalµFA6.17410.250.050.0263group:age
" ], "text/markdown": [ "| ROI | Metric | F(3, df_err) | df_err | ω²ₚ | 95% CI lower | p_adj | Interaction included |\n", "|:------------|:------------------|---------------:|---------:|------:|---------------:|--------:|:-----------------------|\n", "| Amygdala | MD | 5.36 | 44 | 0.24 | 0.05 | 0.0374 | — |\n", "| Amygdala | MTsat | 6.24 | 41 | 0.36 | 0.14 | 0.0263 | — |\n", "| Amygdala | Normalized volume | 5.41 | 44 | 0.23 | 0.04 | 0.0374 | — |\n", "| Cingulate | FA | 5.68 | 44 | 0.3 | 0.1 | 0.0342 | — |\n", "| Cingulate | MD | 8.3 | 41 | 0.34 | 0.12 | 0.0085 | group:age |\n", "| Frontal | MD | 9.92 | 41 | 0.35 | 0.14 | 0.0035 | group:age |\n", "| Frontal | µFA | 5.41 | 41 | 0.2 | 0.02 | 0.0374 | group:age |\n", "| Hippocampus | MD | 5.6 | 44 | 0.26 | 0.07 | 0.0346 | — |\n", "| Hippocampus | MTsat | 5.9 | 41 | 0.34 | 0.12 | 0.0316 | — |\n", "| Hippocampus | µFA | 7.47 | 44 | 0.34 | 0.13 | 0.0102 | — |\n", "| Insula | MD | 7.89 | 44 | 0.32 | 0.12 | 0.0091 | — |\n", "| Parietal | MD | 7.5 | 44 | 0.28 | 0.08 | 0.0102 | — |\n", "| Parietal | MTsat | 6.16 | 41 | 0.33 | 0.11 | 0.0263 | — |\n", "| Parietal | Normalized volume | 9.05 | 44 | 0.33 | 0.13 | 0.0047 | — |\n", "| Temporal | Ki | 6.46 | 41 | 0.25 | 0.05 | 0.0262 | group:age |\n", "| Temporal | MD | 13.71 | 44 | 0.46 | 0.26 | 0.0004 | — |\n", "| Temporal | Normalized volume | 10.8 | 44 | 0.38 | 0.17 | 0.0021 | — |\n", "| Temporal | µFA | 6.17 | 41 | 0.25 | 0.05 | 0.0263 | group:age |" ], "text/plain": [ " ROI Metric F(3, df_err) df_err ω²ₚ 95% CI lower p_adj Interaction included\n", " Amygdala MD 5.36 44 0.24 0.05 0.0374 —\n", " Amygdala MTsat 6.24 41 0.36 0.14 0.0263 —\n", " Amygdala Normalized volume 5.41 44 0.23 0.04 0.0374 —\n", " Cingulate FA 5.68 44 0.30 0.10 0.0342 —\n", " Cingulate MD 8.30 41 0.34 0.12 0.0085 group:age\n", " Frontal MD 9.92 41 0.35 0.14 0.0035 group:age\n", " Frontal µFA 5.41 41 0.20 0.02 0.0374 group:age\n", "Hippocampus MD 5.60 44 0.26 0.07 0.0346 —\n", "Hippocampus MTsat 5.90 41 0.34 0.12 0.0316 —\n", "Hippocampus µFA 7.47 44 0.34 0.13 0.0102 —\n", " Insula MD 7.89 44 0.32 0.12 0.0091 —\n", " Parietal MD 7.50 44 0.28 0.08 0.0102 —\n", " Parietal MTsat 6.16 41 0.33 0.11 0.0263 —\n", " Parietal Normalized volume 9.05 44 0.33 0.13 0.0047 —\n", " Temporal Ki 6.46 41 0.25 0.05 0.0262 group:age\n", " Temporal MD 13.71 44 0.46 0.26 0.0004 —\n", " Temporal Normalized volume 10.80 44 0.38 0.17 0.0021 —\n", " Temporal µFA 6.17 41 0.25 0.05 0.0263 group:age" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#| label: notebook_table_ancovaResults_significant\n", "\n", "cols = [\"structure\", \"metric\", \"F value\", \"Df_residual\",\n", " \"Omega2_partial\", \"CI_low.y\", \"pvals_adj\", \"level\",\n", " \"violated_terms\"\n", " ]\n", "\n", "df_sig = (\n", " df_brain_grouped_ancova_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", " \"violated_terms\": \"Interaction included\"\n", " })\n", " .sort_values([\"structure\", \"metric\"])\n", ").reset_index(drop=True)\n", "\n", "df_sig[\"Interaction included\"] = df_sig[\"Interaction included\"].replace(\"\", \"—\")\n", "\n", "st.display_table(st.format_table(df_sig, two_decimals=[\"F(3, df_err)\", \"ω²ₚ\", \"95% CI lower\"], p_values=[\"p_adj\"]))" ] }, { "cell_type": "code", "execution_count": 37, "id": "8c82fcb8", "metadata": {}, "outputs": [], "source": [ "df_brain_grouped_ancova_posthoc = st.posthoc_eem_tests(df_brain_grouped, df_brain_grouped_ancova_Pcorrected, summary_ancovaAssumptions, mc_alpha=0.05, covar = 'age + sex')" ] }, { "cell_type": "code", "execution_count": 38, "id": "436f9dca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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ROIMetricContrastdf_errtgg CI lowg CI highp (Holm)
AmygdalaMDAD - HC443.351.310.522.090.0017
AmygdalaMDAD - LBD442.491.050.201.910.0167
AmygdalaMDFTD - HC442.821.110.321.900.0071
AmygdalaMTsatAD - HC41-3.72-1.49-2.30-0.680.0006
AmygdalaMTsatAD - LBD41-2.89-1.29-2.19-0.390.0061
AmygdalaMTsatFTD - HC41-2.72-1.08-1.89-0.280.0095
AmygdalaNormalized volumeAD - HC44-3.24-1.27-2.05-0.480.0023
AmygdalaNormalized volumeFTD - HC44-3.35-1.31-2.10-0.520.0017
CingulateFAAD - FTD443.961.560.772.350.0003
CingulateFAAD - HC442.921.140.351.930.0055
CingulateFAAD - LBD442.330.990.131.840.0245
CingulateMDAD - HC414.341.760.942.579.2e-05
CingulateMDFTD - HC414.301.770.942.610.0001
FrontalMDAD - HC414.351.760.942.588.9e-05
FrontalMDFTD - HC414.491.851.022.695.7e-05
FrontalµFAFTD - HC41-3.45-1.42-2.26-0.590.0013
FrontalµFAFTD - LBD41-2.91-1.68-2.84-0.510.0058
HippocampusMDAD - HC443.681.430.652.220.0006
HippocampusMDAD - LBD443.001.270.422.120.0044
HippocampusMTsatAD - FTD41-2.13-0.87-1.70-0.050.0391
HippocampusMTsatAD - HC41-3.91-1.57-2.38-0.760.0003
HippocampusMTsatAD - LBD41-2.98-1.33-2.23-0.430.0048
HippocampusµFAAD - HC44-4.12-1.61-2.40-0.820.0002
HippocampusµFAAD - LBD44-3.25-1.37-2.23-0.520.0022
HippocampusµFAFTD - HC44-2.85-1.12-1.91-0.330.0066
HippocampusµFAFTD - LBD44-2.06-0.88-1.75-0.020.0458
InsulaMDAD - HC444.401.720.932.506.8e-05
InsulaMDAD - LBD442.731.160.302.010.0090
InsulaMDFTD - HC443.381.320.532.110.0015
ParietalMDAD - HC444.571.781.002.574.0e-05
ParietalMDAD - LBD442.080.880.031.730.0431
ParietalMDFTD - HC443.231.260.472.050.0024
ParietalMDLBD - HC442.020.900.001.800.0490
ParietalMTsatAD - HC41-4.15-1.66-2.47-0.850.0002
ParietalMTsatFTD - HC41-2.87-1.14-1.95-0.340.0065
ParietalNormalized volumeAD - HC44-4.95-1.93-2.72-1.141.2e-05
ParietalNormalized volumeAD - LBD44-2.88-1.22-2.07-0.370.0061
ParietalNormalized volumeFTD - HC44-3.28-1.29-2.08-0.500.0020
TemporalKiAD - HC412.440.990.171.810.0189
TemporalKiAD - LBD412.301.360.162.550.0268
TemporalKiFTD - HC413.361.390.552.220.0017
TemporalKiFTD - LBD413.051.760.592.920.0040
TemporalMDAD - HC445.792.261.473.056.8e-07
TemporalMDAD - LBD443.511.480.632.340.0011
TemporalMDFTD - HC444.531.780.992.574.5e-05
TemporalMDFTD - LBD442.331.000.131.860.0245
TemporalNormalized volumeAD - HC44-4.40-1.72-2.50-0.936.9e-05
TemporalNormalized volumeAD - LBD44-2.34-0.99-1.84-0.140.0240
TemporalNormalized volumeFTD - HC44-4.95-1.94-2.73-1.151.1e-05
TemporalNormalized volumeFTD - LBD44-2.83-1.21-2.08-0.350.0070
TemporalµFAAD - HC41-2.29-0.93-1.75-0.110.0271
TemporalµFAAD - LBD41-2.10-1.24-2.43-0.040.0423
TemporalµFAFTD - HC41-3.14-1.30-2.13-0.460.0031
TemporalµFAFTD - LBD41-2.79-1.61-2.77-0.440.0080
" ], "text/markdown": [ "| ROI | Metric | Contrast | df_err | t | g | g CI low | g CI high | p (Holm) |\n", "|:------------|:------------------|:-----------|---------:|------:|------:|-----------:|------------:|-----------:|\n", "| Amygdala | MD | AD - HC | 44 | 3.35 | 1.31 | 0.52 | 2.09 | 0.0017 |\n", "| Amygdala | MD | AD - LBD | 44 | 2.49 | 1.05 | 0.2 | 1.91 | 0.0167 |\n", "| Amygdala | MD | FTD - HC | 44 | 2.82 | 1.11 | 0.32 | 1.9 | 0.0071 |\n", "| Amygdala | MTsat | AD - HC | 41 | -3.72 | -1.49 | -2.3 | -0.68 | 0.0006 |\n", "| Amygdala | MTsat | AD - LBD | 41 | -2.89 | -1.29 | -2.19 | -0.39 | 0.0061 |\n", "| Amygdala | MTsat | FTD - HC | 41 | -2.72 | -1.08 | -1.89 | -0.28 | 0.0095 |\n", "| Amygdala | Normalized volume | AD - HC | 44 | -3.24 | -1.27 | -2.05 | -0.48 | 0.0023 |\n", "| Amygdala | Normalized volume | FTD - HC | 44 | -3.35 | -1.31 | -2.1 | -0.52 | 0.0017 |\n", "| Cingulate | FA | AD - FTD | 44 | 3.96 | 1.56 | 0.77 | 2.35 | 0.0003 |\n", "| Cingulate | FA | AD - HC | 44 | 2.92 | 1.14 | 0.35 | 1.93 | 0.0055 |\n", "| Cingulate | FA | AD - LBD | 44 | 2.33 | 0.99 | 0.13 | 1.84 | 0.0245 |\n", "| Cingulate | MD | AD - HC | 41 | 4.34 | 1.76 | 0.94 | 2.57 | 9.2e-05 |\n", "| Cingulate | MD | FTD - HC | 41 | 4.3 | 1.77 | 0.94 | 2.61 | 0.0001 |\n", "| Frontal | MD | AD - HC | 41 | 4.35 | 1.76 | 0.94 | 2.58 | 8.9e-05 |\n", "| Frontal | MD | FTD - HC | 41 | 4.49 | 1.85 | 1.02 | 2.69 | 5.7e-05 |\n", "| Frontal | µFA | FTD - HC | 41 | -3.45 | -1.42 | -2.26 | -0.59 | 0.0013 |\n", "| Frontal | µFA | FTD - LBD | 41 | -2.91 | -1.68 | -2.84 | -0.51 | 0.0058 |\n", "| Hippocampus | MD | AD - HC | 44 | 3.68 | 1.43 | 0.65 | 2.22 | 0.0006 |\n", "| Hippocampus | MD | AD - LBD | 44 | 3 | 1.27 | 0.42 | 2.12 | 0.0044 |\n", "| Hippocampus | MTsat | AD - FTD | 41 | -2.13 | -0.87 | -1.7 | -0.05 | 0.0391 |\n", "| Hippocampus | MTsat | AD - HC | 41 | -3.91 | -1.57 | -2.38 | -0.76 | 0.0003 |\n", "| Hippocampus | MTsat | AD - LBD | 41 | -2.98 | -1.33 | -2.23 | -0.43 | 0.0048 |\n", "| Hippocampus | µFA | AD - HC | 44 | -4.12 | -1.61 | -2.4 | -0.82 | 0.0002 |\n", "| Hippocampus | µFA | AD - LBD | 44 | -3.25 | -1.37 | -2.23 | -0.52 | 0.0022 |\n", "| Hippocampus | µFA | FTD - HC | 44 | -2.85 | -1.12 | -1.91 | -0.33 | 0.0066 |\n", "| Hippocampus | µFA | FTD - LBD | 44 | -2.06 | -0.88 | -1.75 | -0.02 | 0.0458 |\n", "| Insula | MD | AD - HC | 44 | 4.4 | 1.72 | 0.93 | 2.5 | 6.8e-05 |\n", "| Insula | MD | AD - LBD | 44 | 2.73 | 1.16 | 0.3 | 2.01 | 0.009 |\n", "| Insula | MD | FTD - HC | 44 | 3.38 | 1.32 | 0.53 | 2.11 | 0.0015 |\n", "| Parietal | MD | AD - HC | 44 | 4.57 | 1.78 | 1 | 2.57 | 4e-05 |\n", "| Parietal | MD | AD - LBD | 44 | 2.08 | 0.88 | 0.03 | 1.73 | 0.0431 |\n", "| Parietal | MD | FTD - HC | 44 | 3.23 | 1.26 | 0.47 | 2.05 | 0.0024 |\n", "| Parietal | MD | LBD - HC | 44 | 2.02 | 0.9 | 0 | 1.8 | 0.049 |\n", "| Parietal | MTsat | AD - HC | 41 | -4.15 | -1.66 | -2.47 | -0.85 | 0.0002 |\n", "| Parietal | MTsat | FTD - HC | 41 | -2.87 | -1.14 | -1.95 | -0.34 | 0.0065 |\n", "| Parietal | Normalized volume | AD - HC | 44 | -4.95 | -1.93 | -2.72 | -1.14 | 1.2e-05 |\n", "| Parietal | Normalized volume | AD - LBD | 44 | -2.88 | -1.22 | -2.07 | -0.37 | 0.0061 |\n", "| Parietal | Normalized volume | FTD - HC | 44 | -3.28 | -1.29 | -2.08 | -0.5 | 0.002 |\n", "| Temporal | Ki | AD - HC | 41 | 2.44 | 0.99 | 0.17 | 1.81 | 0.0189 |\n", "| Temporal | Ki | AD - LBD | 41 | 2.3 | 1.36 | 0.16 | 2.55 | 0.0268 |\n", "| Temporal | Ki | FTD - HC | 41 | 3.36 | 1.39 | 0.55 | 2.22 | 0.0017 |\n", "| Temporal | Ki | FTD - LBD | 41 | 3.05 | 1.76 | 0.59 | 2.92 | 0.004 |\n", "| Temporal | MD | AD - HC | 44 | 5.79 | 2.26 | 1.47 | 3.05 | 6.8e-07 |\n", "| Temporal | MD | AD - LBD | 44 | 3.51 | 1.48 | 0.63 | 2.34 | 0.0011 |\n", "| Temporal | MD | FTD - HC | 44 | 4.53 | 1.78 | 0.99 | 2.57 | 4.5e-05 |\n", "| Temporal | MD | FTD - LBD | 44 | 2.33 | 1 | 0.13 | 1.86 | 0.0245 |\n", "| Temporal | Normalized volume | AD - HC | 44 | -4.4 | -1.72 | -2.5 | -0.93 | 6.9e-05 |\n", "| Temporal | Normalized volume | AD - LBD | 44 | -2.34 | -0.99 | -1.84 | -0.14 | 0.024 |\n", "| Temporal | Normalized volume | FTD - HC | 44 | -4.95 | -1.94 | -2.73 | -1.15 | 1.1e-05 |\n", "| Temporal | Normalized volume | FTD - LBD | 44 | -2.83 | -1.21 | -2.08 | -0.35 | 0.007 |\n", "| Temporal | µFA | AD - HC | 41 | -2.29 | -0.93 | -1.75 | -0.11 | 0.0271 |\n", "| Temporal | µFA | AD - LBD | 41 | -2.1 | -1.24 | -2.43 | -0.04 | 0.0423 |\n", "| Temporal | µFA | FTD - HC | 41 | -3.14 | -1.3 | -2.13 | -0.46 | 0.0031 |\n", "| Temporal | µFA | FTD - LBD | 41 | -2.79 | -1.61 | -2.77 | -0.44 | 0.008 |" ], "text/plain": [ " ROI Metric Contrast df_err t g g CI low g CI high p (Holm)\n", " Amygdala MD AD - HC 44 3.35 1.31 0.52 2.09 0.0017\n", " Amygdala MD AD - LBD 44 2.49 1.05 0.20 1.91 0.0167\n", " Amygdala MD FTD - HC 44 2.82 1.11 0.32 1.90 0.0071\n", " Amygdala MTsat AD - HC 41 -3.72 -1.49 -2.30 -0.68 0.0006\n", " Amygdala MTsat AD - LBD 41 -2.89 -1.29 -2.19 -0.39 0.0061\n", " Amygdala MTsat FTD - HC 41 -2.72 -1.08 -1.89 -0.28 0.0095\n", " Amygdala Normalized volume AD - HC 44 -3.24 -1.27 -2.05 -0.48 0.0023\n", " Amygdala Normalized volume FTD - HC 44 -3.35 -1.31 -2.10 -0.52 0.0017\n", " Cingulate FA AD - FTD 44 3.96 1.56 0.77 2.35 0.0003\n", " Cingulate FA AD - HC 44 2.92 1.14 0.35 1.93 0.0055\n", " Cingulate FA AD - LBD 44 2.33 0.99 0.13 1.84 0.0245\n", " Cingulate MD AD - HC 41 4.34 1.76 0.94 2.57 9.2e-05\n", " Cingulate MD FTD - HC 41 4.30 1.77 0.94 2.61 0.0001\n", " Frontal MD AD - HC 41 4.35 1.76 0.94 2.58 8.9e-05\n", " Frontal MD FTD - HC 41 4.49 1.85 1.02 2.69 5.7e-05\n", " Frontal µFA FTD - HC 41 -3.45 -1.42 -2.26 -0.59 0.0013\n", " Frontal µFA FTD - LBD 41 -2.91 -1.68 -2.84 -0.51 0.0058\n", "Hippocampus MD AD - HC 44 3.68 1.43 0.65 2.22 0.0006\n", "Hippocampus MD AD - LBD 44 3.00 1.27 0.42 2.12 0.0044\n", "Hippocampus MTsat AD - FTD 41 -2.13 -0.87 -1.70 -0.05 0.0391\n", "Hippocampus MTsat AD - HC 41 -3.91 -1.57 -2.38 -0.76 0.0003\n", "Hippocampus MTsat AD - LBD 41 -2.98 -1.33 -2.23 -0.43 0.0048\n", "Hippocampus µFA AD - HC 44 -4.12 -1.61 -2.40 -0.82 0.0002\n", "Hippocampus µFA AD - LBD 44 -3.25 -1.37 -2.23 -0.52 0.0022\n", "Hippocampus µFA FTD - HC 44 -2.85 -1.12 -1.91 -0.33 0.0066\n", "Hippocampus µFA FTD - LBD 44 -2.06 -0.88 -1.75 -0.02 0.0458\n", " Insula MD AD - HC 44 4.40 1.72 0.93 2.50 6.8e-05\n", " Insula MD AD - LBD 44 2.73 1.16 0.30 2.01 0.0090\n", " Insula MD FTD - HC 44 3.38 1.32 0.53 2.11 0.0015\n", " Parietal MD AD - HC 44 4.57 1.78 1.00 2.57 4.0e-05\n", " Parietal MD AD - LBD 44 2.08 0.88 0.03 1.73 0.0431\n", " Parietal MD FTD - HC 44 3.23 1.26 0.47 2.05 0.0024\n", " Parietal MD LBD - HC 44 2.02 0.90 0.00 1.80 0.0490\n", " Parietal MTsat AD - HC 41 -4.15 -1.66 -2.47 -0.85 0.0002\n", " Parietal MTsat FTD - HC 41 -2.87 -1.14 -1.95 -0.34 0.0065\n", " Parietal Normalized volume AD - HC 44 -4.95 -1.93 -2.72 -1.14 1.2e-05\n", " Parietal Normalized volume AD - LBD 44 -2.88 -1.22 -2.07 -0.37 0.0061\n", " Parietal Normalized volume FTD - HC 44 -3.28 -1.29 -2.08 -0.50 0.0020\n", " Temporal Ki AD - HC 41 2.44 0.99 0.17 1.81 0.0189\n", " Temporal Ki AD - LBD 41 2.30 1.36 0.16 2.55 0.0268\n", " Temporal Ki FTD - HC 41 3.36 1.39 0.55 2.22 0.0017\n", " Temporal Ki FTD - LBD 41 3.05 1.76 0.59 2.92 0.0040\n", " Temporal MD AD - HC 44 5.79 2.26 1.47 3.05 6.8e-07\n", " Temporal MD AD - LBD 44 3.51 1.48 0.63 2.34 0.0011\n", " Temporal MD FTD - HC 44 4.53 1.78 0.99 2.57 4.5e-05\n", " Temporal MD FTD - LBD 44 2.33 1.00 0.13 1.86 0.0245\n", " Temporal Normalized volume AD - HC 44 -4.40 -1.72 -2.50 -0.93 6.9e-05\n", " Temporal Normalized volume AD - LBD 44 -2.34 -0.99 -1.84 -0.14 0.0240\n", " Temporal Normalized volume FTD - HC 44 -4.95 -1.94 -2.73 -1.15 1.1e-05\n", " Temporal Normalized volume FTD - LBD 44 -2.83 -1.21 -2.08 -0.35 0.0070\n", " Temporal µFA AD - HC 41 -2.29 -0.93 -1.75 -0.11 0.0271\n", " Temporal µFA AD - LBD 41 -2.10 -1.24 -2.43 -0.04 0.0423\n", " Temporal µFA FTD - HC 41 -3.14 -1.30 -2.13 -0.46 0.0031\n", " Temporal µFA FTD - LBD 41 -2.79 -1.61 -2.77 -0.44 0.0080" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#| label: notebook_table_posthocResults_significant\n", "cols = [\n", " \"structure\", \"metric\", \"contrast\",\n", " \"df\", \"t_value\",\n", " \"hedges_g\", \"hedges_g_low\", \"hedges_g_high\",\n", " \"p_holm\", \"level\"\n", "]\n", "df_brain_grouped_ancova_posthoc_sig = (\n", " df_brain_grouped_ancova_posthoc\n", " .query(\"p_holm < @mc_alpha\")\n", " [cols]\n", " .rename(columns={\n", " \"df\": \"df_err\",\n", " \"t_value\": \"t\",\n", " \"hedges_g\": \"g\",\n", " \"hedges_g_low\": \"g CI low\",\n", " \"hedges_g_high\": \"g CI high\",\n", " \"p_holm\": \"p (Holm)\"\n", " })\n", " .sort_values([\"structure\", \"metric\", \"contrast\"])\n", " .reset_index(drop=True)\n", ")\n", "\n", "st.display_table(st.format_table(df_brain_grouped_ancova_posthoc_sig, two_decimals=[\"t\", \"g\", \"g CI low\", \"g CI high\"], p_values=[\"p (Holm)\"]))" ] }, { "cell_type": "code", "execution_count": 39, "id": "558f7ee2", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "%{y}
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"white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Pairwise group differences" }, "width": 700, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ] }, "yaxis": { "anchor": "x", "autorange": "reversed", "domain": [ 0.0, 1.0 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#| label: notebook_fig_posthocResults_significant\n", "\n", "pivot_order = ['AD - HC', 'FTD - HC', 'LBD - HC', 'AD - LBD', 'FTD - LBD', 'AD - FTD'] \n", "\n", "pivot = (\n", " df_brain_grouped_ancova_posthoc_sig\n", " .pivot_table(index=['structure', 'metric'],\n", " columns='contrast',\n", " values=\"g\",\n", " aggfunc='first')\n", " .reindex(columns=pivot_order)\n", ")\n", "# contrasts that did not survive Holm correction stay NaN: drawn as blank cells, not as g = 0\n", "\n", "# flatten the multiindex row labels into a single string per row for display\n", "row_labels = [f\"{s} — {st.metric_name(m)}\" for s, m in pivot.index]\n", "\n", "fig = px.imshow(\n", " pivot.values,\n", " x=pivot.columns.tolist(),\n", " y=row_labels,\n", " color_continuous_scale='RdBu_r',\n", " color_continuous_midpoint=0,\n", " aspect='auto',\n", " labels=dict(color=\"Hedges' g\"),\n", ")\n", "\n", "fig.update_traces(\n", " text=[[\"\" if np.isnan(v) else f\"{v:.2f}\" for v in row] for row in pivot.values],\n", " texttemplate=\"%{text}\",\n", " hovertemplate=\"%{y}
%{x}
Hedges' g = %{z:.2f}\",\n", ")\n", "\n", "fig.update_layout(\n", " plot_bgcolor=\"white\",\n", " title=\"Pairwise group differences\",\n", " width=700,\n", " height=900,\n", " margin=dict(l=200, r=60, t=60, b=60), # extra left margin for long row labels\n", ")\n", "\n", "fig" ] }, { "cell_type": "code", "execution_count": 40, "id": "d595d281", "metadata": {}, "outputs": [], "source": [ "#| label: notebook_figStatic_posthocResults_significant\n", "if create_Static_figures:\n", " helpers.static_theme(style=\"white\")\n", " pivot = (\n", " df_brain_grouped_ancova_posthoc_sig\n", " .pivot_table(index=['structure', 'metric'], columns='contrast', values=\"g\", aggfunc='first')\n", " .reindex(columns=pivot_order)\n", " )\n", " pivot.index = [f\"{s} — {st.metric_name(m)}\" for s, m in pivot.index]\n", "\n", " fig, ax = plt.subplots(figsize=(helpers.TEXT_WIDTH_IN, 5.2))\n", " # contrasts that did not survive Holm correction (NaN) are left blank, as in the interactive figure\n", " sns.heatmap(pivot, mask=pivot.isna(), annot=True, fmt='.2f', annot_kws={\"size\": 7}, center=0, cmap='RdBu_r',\n", " linewidths=0.5, linecolor=\"white\", ax=ax, cbar_kws={'label': \"Hedges' g\"})\n", " ax.set_xlabel(\"Contrast\")\n", " ax.set_ylabel(\"ROI — metric\")\n", " ax.tick_params(length=0)\n", " fig.tight_layout()\n", " helpers.save_static_figure(fig, \"fig_posthocResults_significant\")\n", " plt.close(fig)\n", " sns.set_theme()" ] } ], "metadata": { "kernelspec": { "display_name": "default", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.15" } }, "nbformat": 4, "nbformat_minor": 5 }