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ohbm ready
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htwangtw committed Dec 17, 2021
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32 changes: 19 additions & 13 deletions content/ohbm2022abstract.ipynb
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"\n",
"H-T Wang[^1], S L Meisler[^2][^3], H Shamarke, F Paugam[^1][^4], N Gensollen[^5], B Thirion[^5], C Markiewicz[^6], P Bellec[^1][^7]\n",
"\n",
"[^1] Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada\n",
"[^2] Harvard University, MA, USA\n",
"[^3] Massachusetts Institute of Technology, MA, USA\n",
"[^4] Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada\n",
"[^5] Inria, CEA, Université Paris-Saclay, Paris, France\n",
"[^6] Department of Psychology, Stanford University, Stanford, United States\n",
"[^7] Psychology Department, Université de Montréal, Montréal, Québec, Canada\n",
"[^1]: Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada\n",
"\n",
"[^2]: Harvard University, MA, USA\n",
"\n",
"[^3]: Massachusetts Institute of Technology, MA, USA\n",
"\n",
"[^4]: Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada\n",
"\n",
"[^5]: Inria, CEA, Université Paris-Saclay, Paris, France\n",
"\n",
"[^6]: Department of Psychology, Stanford University, Stanford, United States\n",
"\n",
"[^7]: Psychology Department, Université de Montréal, Montréal, Québec, Canada\n",
"\n",
"### Introduction\n",
"\n",
Expand Down Expand Up @@ -161,7 +167,7 @@
"ax.set(ylabel=\"Percentage %\",\n",
" xlabel=\"confound removal strategy\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"sig_qcfc.png\", dpi=300)"
"# plt.savefig(\"sig_qcfc.png\", dpi=300)"
]
},
{
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"ax.set(ylabel=\"Median absolute deviation\",\n",
" xlabel=\"confound removal strategy\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"mad_qcfc.png\", dpi=300)\n",
"# plt.savefig(\"mad_qcfc.png\", dpi=300)\n",
"\n",
"def draw_absolute_median(data, **kws):\n",
" ax = plt.gca()\n",
Expand Down Expand Up @@ -236,7 +242,7 @@
"g.fig.subplots_adjust(top=0.9) \n",
"g.fig.suptitle('Distribution of correlation between framewise distplacement and edge strength')\n",
"plt.tight_layout()\n",
"plt.savefig(\"dist_qcfc.png\", dpi=300)"
"# plt.savefig(\"dist_qcfc.png\", dpi=300)"
]
},
{
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"ax.set(ylabel=\"Nodewise correlation between\\nEuclidian distance and QC-FC metric\",\n",
" xlabel=\"confound removal strategy\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"corr_dist_qcfc_mean.png\", dpi=300)\n",
"# plt.savefig(\"corr_dist_qcfc_mean.png\", dpi=300)\n",
"\n",
"g = sns.FacetGrid(long_qcfc, col=\"col\", row=\"row\", height=1.7, aspect=1.5)\n",
"g.map(sns.regplot, 'distance', 'qcfc', fit_reg=True, ci=None, \n",
Expand All @@ -316,7 +322,7 @@
"g.fig.subplots_adjust(top=0.9) \n",
"g.fig.suptitle('Correlation between nodewise Euclidian distance and QC-FC')\n",
"plt.tight_layout()\n",
"plt.savefig(\"corr_dist_qcfc_dist.png\", dpi=300)"
"# plt.savefig(\"corr_dist_qcfc_dist.png\", dpi=300)"
]
},
{
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"ax.set(ylabel=\"Pearson's correlation\",\n",
" xlabel=\"confound removal strategy\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"modularity.png\", dpi=300)"
"# plt.savefig(\"modularity.png\", dpi=300)"
]
},
{
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32 changes: 19 additions & 13 deletions content/ohbm2022abstract.md
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Expand Up @@ -59,13 +59,19 @@ metric_per_edge.columns = [col.split('_')[0] for col in metric_per_edge.columns]

H-T Wang[^1], S L Meisler[^2][^3], H Shamarke, F Paugam[^1][^4], N Gensollen[^5], B Thirion[^5], C Markiewicz[^6], P Bellec[^1][^7]

[^1] Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
[^2] Harvard University, MA, USA
[^3] Massachusetts Institute of Technology, MA, USA
[^4] Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
[^5] Inria, CEA, Université Paris-Saclay, Paris, France
[^6] Department of Psychology, Stanford University, Stanford, United States
[^7] Psychology Department, Université de Montréal, Montréal, Québec, Canada
[^1]: Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada

[^2]: Harvard University, MA, USA

[^3]: Massachusetts Institute of Technology, MA, USA

[^4]: Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada

[^5]: Inria, CEA, Université Paris-Saclay, Paris, France

[^6]: Department of Psychology, Stanford University, Stanford, United States

[^7]: Psychology Department, Université de Montréal, Montréal, Québec, Canada

### Introduction

Expand Down Expand Up @@ -131,7 +137,7 @@ ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
ax.set(ylabel="Percentage %",
xlabel="confound removal strategy")
plt.tight_layout()
plt.savefig("sig_qcfc.png", dpi=300)
# plt.savefig("sig_qcfc.png", dpi=300)
```

```{code-cell} ipython3
Expand All @@ -146,7 +152,7 @@ ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
ax.set(ylabel="Median absolute deviation",
xlabel="confound removal strategy")
plt.tight_layout()
plt.savefig("mad_qcfc.png", dpi=300)
# plt.savefig("mad_qcfc.png", dpi=300)
def draw_absolute_median(data, **kws):
ax = plt.gca()
Expand Down Expand Up @@ -174,7 +180,7 @@ for i, name in zip(range(9), metric_per_edge.columns):
g.fig.subplots_adjust(top=0.9)
g.fig.suptitle('Distribution of correlation between framewise distplacement and edge strength')
plt.tight_layout()
plt.savefig("dist_qcfc.png", dpi=300)
# plt.savefig("dist_qcfc.png", dpi=300)
```

#### Distance-dependent effects of motion on connectivity
Expand All @@ -199,7 +205,7 @@ ax.set(ylim=(-0.5, 0.05))
ax.set(ylabel="Nodewise correlation between\nEuclidian distance and QC-FC metric",
xlabel="confound removal strategy")
plt.tight_layout()
plt.savefig("corr_dist_qcfc_mean.png", dpi=300)
# plt.savefig("corr_dist_qcfc_mean.png", dpi=300)
g = sns.FacetGrid(long_qcfc, col="col", row="row", height=1.7, aspect=1.5)
g.map(sns.regplot, 'distance', 'qcfc', fit_reg=True, ci=None,
Expand All @@ -217,7 +223,7 @@ for i, name in zip(range(9), metric_per_edge.columns):
g.fig.subplots_adjust(top=0.9)
g.fig.suptitle('Correlation between nodewise Euclidian distance and QC-FC')
plt.tight_layout()
plt.savefig("corr_dist_qcfc_dist.png", dpi=300)
# plt.savefig("corr_dist_qcfc_dist.png", dpi=300)
```

#### Network modularity
Expand Down Expand Up @@ -258,7 +264,7 @@ ax.set_title("Correlation between\nnetwork modularity and \nmean framewise displ
ax.set(ylabel="Pearson's correlation",
xlabel="confound removal strategy")
plt.tight_layout()
plt.savefig("modularity.png", dpi=300)
# plt.savefig("modularity.png", dpi=300)
```

### Conclusions
Expand Down

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