{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "0zPY_FV0glOV" }, "source": [ "# fMRI as Graph" ] }, { "cell_type": "markdown", "metadata": { "id": "lCbZROxBwXUd" }, "source": [ "**Prerequisite**:\n", "1. ผู้เรียนควรศึกษาเนื้อหา Introduction to fMRI Preprocessing มาแล้ว โดยเฉพาะการเข้าใจว่า\n", " \n", " 1.1 fMRI เป็นข้อมูลที่มีทั้ง spatial dimension และ time dimension ซึ่งการ visualize ข้อมูลก็ทำได้หลายวิธี เช่น การดูภาพสมองที่เวลาใดเวลาหนึ่ง หรือ การดูข้อมูล time series ที่ voxel ใด voxel หนึ่ง\n", "\n", " 1.2 การทำ registration จะช่วยให้เราสามารถแปลงข้อมูล fMRI ที่เราเก็บมา ให้มีตำแหน่งของอวัยวะต่าง ๆ ตรงกับ template บางอย่าง (เช่น MRI Atlas ซึ่งมีข้อมูลเกี่ยวกับการแบ่งส่วนต่าง ๆ ของสมอง (brain parcellation)) ซึ่งจะช่วยให้เราสามารถนำเอาข้อมูลไปใช้ทำการวิเคราะห์ต่อได้ง่ายมากยิ่งขึ้น เช่น เราสามารถดึงเอา voxel จากบริเวณสมองที่เราสนใจออกมาทำการวิเคราะห์ต่อได้\n", "\n", "2. ผู้เรียนควรผ่านการศึกษาเนื้อหา Brain Building Blocks โดยเฉพาะบทเรียน [Functional Area & Network](https://youtu.be/DPiVUyxK3oM?si=JJwSyYKPbadxc9hG) และเนื้อหาที่เกี่ยวข้องกับ fMRI ทั้งหมด\n", "\n", "3. ผู้เรียนควรศึกษาเนื้อหา Signal Processing ของ Brain Code Camp มาแล้ว\n", "\n", "4. ผู้เรียนควรมีความรู้ความเข้าใจทางสถิติเบื้องต้น เช่น\n", " \n", " 4.1 ค่าเฉลี่ย (mean)\n", " \n", " 4.2 standard deviation/variance\n", " \n", " 4.3 correlation\n", "\n", "
\n", "\n", "**Main Source**: บทเรียนนี้ได้นำเอาโค้ดจากบทเรียน [Functional connectivity with nilearn](https://main-educational.github.io/intro_nilearn/functional-connectivity-with-nilearn.html), [Default mode network extraction of ADHD dataset](https://nilearn.github.io/stable/auto_examples/04_glm_first_level/plot_adhd_dmn.html) และ [Comparing connectomes on different reference atlases](https://nilearn.github.io/stable/auto_examples/03_connectivity/plot_atlas_comparison.html#sphx-glr-auto-examples-03-connectivity-plot-atlas-comparison-py) ของ [`nilearn`](https://nilearn.github.io/) มาปรับแก้และต่อยอด เพื่อให้มีสอดคล้องกับหลักสูตรของ Brain Code Camp มากยิ่งขึ้น" ] }, { "cell_type": "markdown", "metadata": { "id": "_2ulN2kKNAtU" }, "source": [ "## Overview\n", "\n", "เราได้เห็นแล้วว่าการทำ preprocessing ทั้งหมดในบทเรียนก่อนหน้า ใช้เวลาค่อนข้างนาน ดังนั้นในบทเรียนนี้ เราจะ load เอาข้อมูลที่ผ่านการทำ registration แล้วมาใช้สำหรับเรียนรู้กัน\n", "\n", "ขั้นตอนการวิเคราะห์ในบทเรียนนี้ประกอบด้วย\n", "1. **fMRI Data Loading and Simple Exploration** การโหลดข้อมูล fMRI ที่ผ่านการ register ไปยัง template เรียบร้อยแล้ว โดยการเรียกใช้ `nilearn.datasets.fetch_developement_fmri`\n", "2. **fMRI as Graph** การมอง fMRI เป็นข้อมูลประเภท graph\n", "3. **Graph Visualization**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ETsZUDPr3TUr", "outputId": "7d09e32b-4805-4b51-ff80-f25e4d714b06" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: nilearn in /usr/local/lib/python3.13/dist-packages (0.14.1)\n", "Requirement already satisfied: jinja2>=3.1.6 in /usr/local/lib/python3.13/dist-packages (from nilearn) (3.1.6)\n", "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (1.5.3)\n", "Requirement already satisfied: nibabel>=5.2.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (5.4.2)\n", "Requirement already satisfied: numpy>=1.26.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (2.1.3)\n", "Requirement already satisfied: packaging>=26.0.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (26.3)\n", "Requirement already satisfied: pandas>=2.3.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (3.0.5)\n", "Requirement already satisfied: requests>=2.33.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (2.34.2)\n", "Requirement already satisfied: scikit-learn!=1.7.0,!=1.9.0,>=1.5.0 in /usr/local/lib/python3.13/dist-packages (from nilearn) (1.6.1)\n", "Requirement already satisfied: scipy>=1.11.1 in /usr/local/lib/python3.13/dist-packages (from nilearn) (1.16.3)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.13/dist-packages (from jinja2>=3.1.6->nilearn) (3.0.3)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.13/dist-packages (from pandas>=2.3.0->nilearn) (2.9.0.post0)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.13/dist-packages (from requests>=2.33.0->nilearn) (3.4.9)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.13/dist-packages (from requests>=2.33.0->nilearn) (3.19)\n", "Requirement already satisfied: urllib3<3,>=1.26 in /usr/local/lib/python3.13/dist-packages (from requests>=2.33.0->nilearn) (2.5.0)\n", "Requirement already satisfied: certifi>=2023.5.7 in /usr/local/lib/python3.13/dist-packages (from requests>=2.33.0->nilearn) (2026.7.22)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.13/dist-packages (from scikit-learn!=1.7.0,!=1.9.0,>=1.5.0->nilearn) (3.6.0)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.13/dist-packages (from python-dateutil>=2.8.2->pandas>=2.3.0->nilearn) (1.17.0)\n" ] } ], "source": [ "!pip install nilearn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uvYGpRHqARlV" }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import nilearn\n", "from nilearn import datasets, plotting, maskers\n", "from nilearn import image as ni_image\n", "from nilearn.connectome import ConnectivityMeasure" ] }, { "cell_type": "markdown", "metadata": { "id": "ACVF7f_LNF9q" }, "source": [ "## fMRI Data Loading and Simple Exploration" ] }, { "cell_type": "markdown", "metadata": { "id": "svjBpru7F9m9" }, "source": [ "### Data Loading" ] }, { "cell_type": "markdown", "metadata": { "id": "z1LlaOarPfzh" }, "source": [ "โหลดข้อมูล fMRI มาจากคน 2 คน แล้ว save ไว้ใน folder ที่ระบุไว้ใน `data_dir` ผ่านการเรียกใช้ `nilearn.datasets.fetch_adhd`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 33 }, "id": "uoifjj9EEmJS", "outputId": "09afa902-c773-405b-e834-15c7eb3811a0" }, "outputs": [ { "data": { "text/html": [ "
[fetch_adhd] Dataset directory found: nilearn_data/adhd\n",
              "
\n" ], "text/plain": [ "\u001b[1;34m[\u001b[0m\u001b[34mfetch_adhd\u001b[0m\u001b[1;34m]\u001b[0m Dataset directory found: nilearn_data/adhd\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_dir = './nilearn_data'\n", "num_subjects = 2\n", "fmri_multi_subjects = datasets.fetch_adhd(n_subjects=num_subjects, data_dir=data_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "jS1eDxQfGGSW" }, "source": [ "### Simple Exploration" ] }, { "cell_type": "markdown", "metadata": { "id": "h2TUzYv7Pbox" }, "source": [ "สำรวจข้อมูลดูว่าสิ่งที่เราโหลดมา มีข้อมูลอะไรอยู่บ้าง" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Va51-GolEmgp", "outputId": "6f72601d-ebe4-4ab9-b525-d82b25513695" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['func', 'confounds', 'phenotypic', 'description', 't_r'])\n" ] } ], "source": [ "print(fmri_multi_subjects.keys())" ] }, { "cell_type": "markdown", "metadata": { "id": "K46v-cnQGcu1" }, "source": [ "หากต้องการทราบว่า key ไหนเป็นข้อมูลประเภทอะไร เราสามารถเข้าไปหาข้อมูลใน documentation ของ [`nilearn.datasets.fetch_adhd`](https://nilearn.github.io/dev/modules/description/adhd.html#adhd-dataset) ซึ่งเราจะพบว่าข้อมูลชุดนี้ ถูกนำมาจาก [The ADHD-200 Sample](https://fcon_1000.projects.nitrc.org/indi/adhd200/index.html) อีกที\n", "\n", "\n", "เรามาลองเริ่มต้นด้วยการ print ตัว `func` ออกมาดู" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pYRMjAVEF-WP", "outputId": "5f427b9d-dfa1-499a-90bb-4b5252937d5a" }, "outputs": [ { "data": { "text/plain": [ "['nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz',\n", " 'nilearn_data/adhd/data/0010064/0010064_rest_tshift_RPI_voreg_mni.nii.gz']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fmri_multi_subjects.func" ] }, { "cell_type": "markdown", "metadata": { "id": "7pqfbdQ-H2Gw" }, "source": [ "เราจะเห็นว่า key ที่มีชื่อว่า `func` เป็น Python list ที่มีแต่ละ element เป็น `str` ที่เก็บชื่อไฟล์ข้อมูล fMRI\n", "\n", "สิ่งที่เราสังเกตได้จากชื่อไฟล์ทั้งหมดก็คือ\n", "\n", "1. ไฟล์ทั้งหมดลงท้ายด้วย `.nii.gz` โดย `nii` คือ ไฟล์ชนิด Neuroimaging Informatics Technology Initiative (NIfTI) ซึ่งเป็น format ที่เป็นที่นิยมใช้เก็บข้อมูล MRI และ `.gz` แสดงให้เห็นว่าไฟล์นี้ได้ถูกบีบอัด (compression) เพื่อลดขนาด\n", "\n", "2. มีหลักการการตั้งชื่อที่ค่อนข้างมีระบบ เช่น\n", " \n", " 1.1 เริ่มต้นชื่อไฟล์ด้วยรหัสที่เฉพาะเจาะจงกับ fMRI ไฟล์นั้น\n", "\n", " 1.2 เราพอจะบอกข้อมูลเกี่ยวกับการทดลองจากชื่อไฟล์ได้ในระดับหนึ่ง เช่น `rest`ที่แสดงให้เห็นว่า ข้อมูลนี้เป็นข้อมูลประเภท resting-state fMRI, `rpi` ที่แสดงถึงทิศทางการเก็บข้อมูลแบบ Right-Posterior-Inferior, `voreg_mni` ที่บ่งบอกว่าข้อมูลนี้ผ่านการทำ volume registration ไปยัง MNI template แล้ว\n", "\n", "เราสามารถดูข้อมูลเพิ่มเติมได้จาก key อื่น ๆ เช่น `phenotypic`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_acgMGStHkwH", "outputId": "13b40057-1295-40c2-f4a1-0f4ea5575fb4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Unnamed: 0', 'Subject', 'Rest.Scan', 'MeanFD',\n", " 'NumFD_greater_than_0.20', 'rootMeanSquareFD', 'FDquartile.top1.4thFD.',\n", " 'PercentFD_greater_than_0.20', 'MeanDVARS', 'MeanFD_Jenkinson', 'site',\n", " 'sibling_id', 'data_set', 'age', 'sex', 'handedness', 'full_2_iq',\n", " 'full_4_iq', 'viq', 'piq', 'iq_measure', 'tdc', 'adhd',\n", " 'adhd_inattentive', 'adhd_combined', 'adhd_subthreshold',\n", " 'diagnosis_using_cdis', 'notes', 'sess_1_anat_2', 'oppositional',\n", " 'cog_inatt', 'hyperac', 'anxious_shy', 'perfectionism',\n", " 'social_problems', 'psychosomatic', 'conn_adhd', 'restless_impulsive',\n", " 'emot_lability', 'conn_gi_tot', 'dsm_iv_inatt', 'dsm_iv_h_i',\n", " 'dsm_iv_tot', 'study', 'sess_1_rest_1', 'sess_1_rest_1_eyes',\n", " 'sess_1_rest_2', 'sess_1_rest_2_eyes', 'sess_1_rest_3',\n", " 'sess_1_rest_3_eyes', 'sess_1_rest_4', 'sess_1_rest_4_eyes',\n", " 'sess_1_rest_5', 'sess_1_rest_5_eyes', 'sess_1_rest_6',\n", " 'sess_1_rest_6_eyes', 'sess_1_anat_1', 'sess_1_which_anat',\n", " 'sess_2_rest_1', 'sess_2_rest_1_eyes', 'sess_2_rest_2',\n", " 'sess_2_rest_2_eyes', 'sess_2_anat_1', 'defacing_ok', 'defacing_notes'],\n", " dtype='str')\n" ] } ], "source": [ "print(fmri_multi_subjects.phenotypic.columns)" ] }, { "cell_type": "markdown", "metadata": { "id": "S6qfkn10PJ1C" }, "source": [ "ถ้าเราไม่แน่ใจว่าตัวแปรต่าง ๆ มันสื่อถึงอะไร หรือ ถ้าต้องการทราบข้อมูลเชิงลึก เราควรเข้าไปศึกษาข้อมูลเพิ่มเติมจาก official documentation ด้วย เช่น เราสามารถดูว่าข้อมูลชุดนี้ผ่านการ preprocess อะไรมาแล้วบ้างได้[ที่นี่](http://preprocessed-connectomes-project.org/adhd200/)" ] }, { "cell_type": "markdown", "metadata": { "id": "wysA9z-zQ5Td" }, "source": [ "หลังจากที่เราได้เห็นภาพรวมของข้อมูลชุดนี้แล้ว เราก็จะมาลองโหลดข้อมูลจาก subject 1 คนมาดูกัน โดยใช้ `nilearn.ni_image.load_img`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "S2Dn_IJuG5qM", "outputId": "725cd5f7-6410-44a8-c690-0012ef11a298" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape (x, y, z, t) = (61, 73, 61, 176)\n", "\n", " object, endian='<'\n", "sizeof_hdr : 348\n", "data_type : b''\n", "db_name : b''\n", "extents : 0\n", "session_error : 0\n", "regular : b'r'\n", "dim_info : 48\n", "dim : [ 4 61 73 61 176 1 1 1]\n", "intent_p1 : 0.0\n", "intent_p2 : 0.0\n", "intent_p3 : 0.0\n", "intent_code : none\n", "datatype : float32\n", "bitpix : 32\n", "slice_start : 0\n", "pixdim : [-1. 3. 3. 3. 2. 0. 0. 0.]\n", "vox_offset : 0.0\n", "scl_slope : nan\n", "scl_inter : nan\n", "slice_end : 60\n", "slice_code : unknown\n", "xyzt_units : 10\n", "cal_max : 0.0\n", "cal_min : 0.0\n", "slice_duration : 0.0\n", "toffset : 0.0\n", "glmax : 0\n", "glmin : 0\n", "descrip : b''\n", "aux_file : b''\n", "qform_code : scanner\n", "sform_code : scanner\n", "quatern_b : -0.0\n", "quatern_c : 1.0\n", "quatern_d : 0.0\n", "qoffset_x : 90.0\n", "qoffset_y : -126.0\n", "qoffset_z : -72.0\n", "srow_x : [-3. -0. -0. 90.]\n", "srow_y : [ -0. 3. -0. -126.]\n", "srow_z : [ 0. 0. 3. -72.]\n", "intent_name : b''\n", "magic : b'n+1'\n" ] } ], "source": [ "idx_subject = 0\n", "fmri_data_one_subject = ni_image.load_img(fmri_multi_subjects.func[idx_subject])\n", "\n", "# ดูว่าข้อมูลของเรามี shape เป็นอย่างไร (อ้างอิงจากรูปแบบการจัดเรียงข้อมูลของ NIfTI)\n", "print(f\"Shape (x, y, z, t) = {fmri_data_one_subject.shape}\\n\")\n", "\n", "# ดูข้อมูลเพิ่มเติมได้จากการดู header\n", "print(fmri_data_one_subject.header)" ] }, { "cell_type": "markdown", "metadata": { "id": "19AmAziJRwSi" }, "source": [ "เราจะเห็นจาก output ว่า `Shape (x, y, z, t) = (61, 73, 61, 176)` ซึ่งเราสามารถมองข้อมูลนี้ได้ในหลายแบบ เช่น" ] }, { "cell_type": "markdown", "metadata": { "id": "wX9J6HqFVnlP" }, "source": [ "**แบบที่ 1** ข้อมูล fMRI นี้เป็นข้อมูลภาพ MRI (ขนาด 61 x 73 x 61 voxels) ที่เวลาต่าง ๆ กัน (176 จุดในแกนเวลา) หรือเป็น video ของ MRI ที่มี 176 frames นั่นเอง" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 445 }, "id": "ZeEusSuYVEld", "outputId": "c536ebda-9556-4717-87bd-5c044c9ef7b2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape: (61, 73, 61) at time index 100\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.13/dist-packages/numpy/_core/fromnumeric.py:870: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", " a.partition(kth, axis=axis, kind=kind, order=order)\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx_time_point = 100\n", "\n", "# เลือกข้อมูลจาก time point ที่ต้องการ\n", "fmri_one_subject_one_time_pt = fmri_data_one_subject.slicer[:,:,:,idx_time_point]\n", "\n", "print(f\"Shape: {fmri_one_subject_one_time_pt.shape} at time index {idx_time_point}\\n\")\n", "plotting.view_img(fmri_one_subject_one_time_pt)" ] }, { "cell_type": "markdown", "metadata": { "id": "fPbC_gtGWoQD" }, "source": [ "**แบบที่ 2** ข้อมูล fMRI นี้เป็นข้อมูล time signal (หรือ time series) ที่มาจากแต่ละ voxel เนื่องจาก 1 voxel มี time signal 1 เส้น ส่งผลเรามี time signal ทั้งหมด 61 x 73 x 61 = 271,633 เส้น" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 416 }, "id": "77Qrr4K5XaJl", "outputId": "b2391229-f3a0-4272-bf20-f2c9f8dc8c3a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape: (176,)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_location, y_location, z_location = 30, 35, 31\n", "\n", "# เลือกข้อมูลจาก time point ที่ต้องการ\n", "time_signal_one_location = fmri_data_one_subject.slicer[x_location:x_location+1,y_location:y_location+1,z_location:z_location+1,:].get_fdata().squeeze()\n", "\n", "print(f\"Shape: {time_signal_one_location.shape}\")\n", "\n", "plt.figure(figsize=(10, 4))\n", "plt.plot(time_signal_one_location, color=\"#1f77b4\", linewidth=1.5)\n", "plt.title(f\"fMRI time signal at voxel ({x_location}, {y_location}, {z_location})\")\n", "plt.xlabel(\"time point\")\n", "plt.ylabel(\"signal intensity\")\n", "plt.grid(True, linestyle=\"--\", alpha=0.6)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "oRGBq755aRM1" }, "source": [ "## fMRI as Graph" ] }, { "cell_type": "markdown", "metadata": { "id": "nXjQ0RUyacO1" }, "source": [ "ใน section ก่อนหน้า เรามอง fMRI เป็นรูปภาพที่เวลาต่าง ๆ และ time signal ที่ voxel ต่าง ๆ ใน section นี้ เราจะลองมอง fMRI เป็นข้อมูลชนิด Graph กัน" ] }, { "cell_type": "markdown", "metadata": { "id": "SMrJG29WbZ7V" }, "source": [ "### What is a Graph\n", "\n", "เวลาเราพูดถึง graph เรามักจะนึกถึงกราฟเส้น หรือ กราฟแท่งที่เราใช้กันบ่อย ๆ แต่ในบทเรียนนี้ เราจะมาลองทำความรู้จัก graph ในอีกบริบทกัน\n", "\n", "Graph มีโครงสร้างข้อมูลที่ประกอบด้วยองค์ประกอบหลัก 2 อย่าง คือ\n", "\n", "1. **Node (หรือ vertex)** มักถูกแสดงด้วยวงกลม\n", "\n", "2. **Edge** แสดงด้วยเส้นเชื่อมที่แสดงถึงความสัมพันธ์ระหว่าง node ต่าง ๆ ในกราฟ\n", "\n", "![Graph](https://raw.githubusercontent.com/braincodecamp/brain-code-camp-2026-lectures/2a86483294ce6b60b3a8d209cb88681431066fa3/Figures/graph_node_edge.jpeg)\n", "\n", "ในภาพด้านบนจะเห็นว่ามี graph 2 ประเภท (undirected graph และ directed graph) โดยที่แต่ละประเภทมี node จำนวน 4 node และ มี edge จำนวน 4 edge ความแตกต่างระหว่างทั้ง 2 graph ก็คือ ใน directed graph จะแสดง edge ด้วยลูกศร ที่แสดงทิศทาง ในขณะที่ใน undirected graph ไม่มีการบ่งบอกทิศทางใน edge เลย\n", "\n", "
\n", "\n", "**ตัวอย่างข้อมูล graph ในชีวิตจริง** (ซึ่งในหลาย ๆ สาขาจะใช้คำว่า **network** แทนคำว่า graph)\n", "1. **ความเป็นเพื่อนใน Facebook** ซึ่งเราอาจจะลองใช้ undirected graph มาแสดงข้อมูลนี้ โดยที่\n", " - **node**: user แต่ละคน\n", " - **edge**: การเป็นเพื่อนกันบน Facebook โดยคู่ node ที่มี edge เชื่อมกันแสดงว่าเป็นเพื่อนกันใน Facebook ในขณะที่คู่ node ที่ไม่มี edge เชื่อมกันแสดงถึงการไม่ได้เป็นเพื่อนกันใน Facebook\n", "2. **การติดตามกันบน social media** ซึ่งเราอาจจะลองใช้ directed graph มาแสดงข้อมูลนี้ โดยที่\n", " - **node**: account แต่ละอัน\n", " - **edge**: ถ้า account ที่แสดงด้วย node 1 ได้ทำการ follow ตัว account ที่แสดงด้วย node 2 ใน graph ก็จะมีลูกศรวิ่งจาก node 1 ไปยัง node 2\n", "3. **Molecular structure** ซึ่งเราอาจจะแสดงได้ด้วย undirected graph โดยที่\n", " - **node**: atom แต่ละตัวใน molecule\n", " - **edge**: การมีอยู่ของพันธะเคมีที่เชื่อมระหว่างคู่อะตอม" ] }, { "cell_type": "markdown", "metadata": { "id": "jUIUDqrJiK9r" }, "source": [ "\n", "\n", "[Slides: Graph Neural Network](https://github.com/braincodecamp/brain-code-camp-2026-lectures/blob/main/ExtraResources/GraphNeuralNetwork.pdf)" ] }, { "cell_type": "markdown", "metadata": { "id": "xYsOTImpiNhS" }, "source": [ "หนึ่งในวิธีการ represent ข้อมูลประเภท graph ก็คือการใช้ adjacency matrix ซึ่งเป็น matrix ที่บ่งบอกว่า node ไหน เชื่อมกันบ้าง\n", "\n", "- ในตัวอย่างด้านซ้ายซึ่งเป็น undirected graph เราจะเห็นว่า\n", " - node 2 และ node 4 มี edge เชื่อมกัน ดังนั้น element ใน adjacency matrix ที่ตรงกับ node 2 และ node 4 จะมีค่าที่ไม่เป็น 0 (ในที่นี้ใช้ค่า 1) ดังแสดงด้วยสีชมพูในภาพ\n", " - node 2 และ node 3 ไม่มี edge เชื่อมกันโดยตรง ดังนั้น element ใน adjacency matrix ที่ตรงกับ node 2 และ node 3 จะมีค่าเป็น 0\n", "- ในตัวอย่างด้านขวาซึ่งเป็น directed graph เราจะเห็นว่า\n", " - มีลูกศรชี้จาก node 4 ไปที่ node 3 ดังนั้น element ใน adjacency matrix ที่มี source เป็น node 4 และมี destination เป็น node 3 จะมีค่าไม่เท่ากับ 0 (ในที่นี้ใช้ค่า 1) ดังแสดงด้วยสีเขียว\n", " - มีลูกศรชี้จาก node 4 ไปที่ node 2 ดังนั้น element ใน adjacency matrix ที่มี source เป็น node 4 และมี destination เป็น node 2 จะมีค่าไม่เท่ากับ 0 (ในที่นี้ใช้ค่า 1) ดังแสดงด้วยสีชมพู\n", " - ไม่มีลูกศรชี้จาก node 2 ไปยัง node 4 ดังนั้น element ใน adjacency matrix ที่มี source เป็น node 2 และมี destination เป็น node 4 จะมีค่าเป็น 0\n", "\n", "ในตัวอย่างก่อนหน้านี้ adjacency matrix ของเรามีแค่เลข 0 (ไม่มีเส้นเชื่อม หรือ edge) กับ 1 (มีเส้นเชื่อม) ซึ่งบอกเพียงแค่*การมีอยู่*ของความสัมพันธ์ระหว่างคู่ node แต่ในชีวิตจริง ความสัมพันธ์ระหว่างคู่ node มักจะมีขนาดหรือปริมาณที่ต่างกันด้วย เราจึงใช้ Edge Weight แทนค่าปริมาณเหล่านั้น โดยนำ edge weight ไปใส่ใน adjacency matrix (และ/หรือใส่ weight กำกับไว้ตรง edge ในภาพ graph) แทนเลข 1\n", "\n", "
\n", "\n", "**ตัวอย่างของ graph ที่มี edge weight**\n", "\n", "1. **ระยะทางหรือเวลาเดินทางระหว่างเมือง**\n", " - **node**: เมืองต่าง ๆ\n", " - **edge weight**: ระยะทาง (กิโลเมตร) หรือ เวลาที่ใช้ในการเดินทางระหว่าง 2 เมือง\n", "2. **ความสนิทสนมใน social media**\n", " - **node**: User แต่ละคน\n", " - **edge weight**: ตัวเลขที่แปรผันกับจำนวนครั้งที่มีปฏิสัมพันธ์กัน (จำนวน chat, like, หรือ comment)" ] }, { "cell_type": "markdown", "metadata": { "id": "6B8DV8vlrXZj" }, "source": [ "หากเราต้องการที่จะมองข้อมูล fMRI เป็น graph เราควรจะนิยาม node และ edge อย่างไร" ] }, { "cell_type": "markdown", "metadata": { "id": "InHFDYZYbPsz" }, "source": [ "### Defining Nodes\n", "\n", "\n", "หนึ่งในวิธีที่ตรงไปตรงมาที่สุดก็คือการให้แต่ละ voxel ในข้อมูล fMRI เป็น node แต่ละ node โดยที่มี feature เป็น time signal ของ voxel นั้น ส่งผลให้เรามีจำนวน node ใน graph ของเราเท่ากับจำนวน voxel ที่เรามี\n", "\n", "ข้อมูล fMRI เรามีทั้งหมด 61 x 73 x 61 = 271,633 voxels\n", " - มีจำนวน node เท่ากับ 271,633 node\n", " - adjacency matrix จะมีขนาด 271,633 x 271,633 ซึ่งนับเป็น matrix ที่มีขนาดใหญ่ค่อนข้างมาก ซึ่งหากเรานำเอาข้อมูลนี้ไปประมวลผลต่อ จะกินทรัพยากรมาก\n", "\n", "หากเราต้องการลดจำนวน node ลง เราสามารถทำอย่างไรได้บ้าง" ] }, { "cell_type": "markdown", "metadata": { "id": "ULz9Mb_oiWHV" }, "source": [ "#### Brain Atlas Utilization\n", "\n", "จากเนื้อหา [Functional Area & Network](https://youtu.be/DPiVUyxK3oM?si=dHBS3gm5edPl9j-E) ของ Brain Building Blocks เราได้เรียนรู้ว่าสมองของเราไม่ได้ทำงานเป็นเหมือนก้อนเต้าหู้ที่ทุกบริเวณของสมองทำงานเหมือนกันหมด (aggregated field)\n", "แต่สมองนั้นถูกแบ่งออกเป็นหลายบริเวณ โดยแต่ละบริเวณจะมีฟังก์ชันการทำงานที่เจาะจงบางอย่าง (functional area) และ แต่ละ functional area ก็ยังมีการเชื่อมโยงกันอีกด้วย\n", "\n", "หากเรานำเอาแนวคิดนี้มาต่อยอดและประยุกต์ใช้ในการจัด voxel ใน fMRI ออกเป็นกลุ่ม ๆ เพื่อรวมเอา voxel ที่มีความเกี่ยวข้องกันในบางแง่มุมมารวมกัน ก็จะทำให้เราสามารถลดปริมาณ node และ ความซ้ำซ้อนของข้อมูลได้ ซึ่ง *ความเกี่ยวข้องกัน* อาจจะเป็นได้ในหลายรูปแบบ เช่น ความเกี่ยวข้องกันในทางฟังก์ชันการทำงาน ความเกี่ยวข้องกันในทางตำแหน่ง หรือ ความเกี่ยวข้องกันในรูปแบบอื่น ๆ ที่ถูกค้นพบด้วยการใช้เทคนิคทาง machine learning (เช่น การทำ clustering หรือการใช้ community detection algorithm)\n", "\n", "หนึ่งในวิธีที่ได้รับความนิยมในการจับกลุ่ม voxel ก็คือการใช้ brain atlas ตามที่เราได้เห็นมาแล้วจากเนื้อหา Introduction to fMRI Preprocessing ของ Brain Code Camp ในวิธีนี้เราจับกลุ่ม voxel มาโดยการทำ 2 ขั้นตอนต่อกัน\n", "1. นำเอาข้อมูล fMRI ของเรามาทำ registration ไปยัง standard template (เช่น MNI152 ที่ถูกใช้เป็นตัวอย่างในบทเรียนก่อนหน้า)\n", "2. ใช้ brain atlas ที่สอดคล้องกับ template ที่เราเลือกใช้ในการทำ registration มาช่วยในการดึงข้อมูลจากแต่ละบริเวณที่ระบุไว้ใน atlas ที่เราเลือกใช้ได้\n", "\n", "
\n", "\n", "ในบทเรียนนี้ เราจะลองเรียกใช้ `nilearn.datasets.fetch_atlas_yeo_2011` ในการดึงข้อมูลจากแต่ละบริเวณออกมา\n", "\n", "
\n", "\n", "**MNI152 References**\n", "\n", "- VS Fonov, AC Evans, K Botteron, CR Almli, RC McKinstry, DL Collins and BDCG, Unbiased average age-appropriate atlases for pediatric studies, NeuroImage,Volume 54, Issue 1, January 2011, ISSN 1053–8119, DOI: 10.1016/j.neuroimage.2010.07.033\n", "\n", "- VS Fonov, AC Evans, RC McKinstry, CR Almli and DL Collins, Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, NeuroImage, Volume 47, Supplement 1, July 2009, Page S102 Organization for Human Brain Mapping 2009 Annual Meeting, DOI: http://dx.doi.org/10.1016/S1053-8119(09)70884-5\n", "\n", "**Atlas References**\n", "- Cortical parcellation estimated by intrinsic functional connectivity. https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_Yeo2011. Accessed: 2021-05-19.\n", "\n", "- B. T. Thomas Yeo, Fenna M. Krienen, Jorge Sepulcre, Mert R. Sabuncu, Danial Lashkari, Marisa Hollinshead, Joshua L. Roffman, Jordan W. Smoller, Lilla Zöllei, Jonathan R. Polimeni, Bruce Fischl, Hesheng Liu, and Randy L. Buckner. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3):1125–1165, 2011. PMID: 21653723. doi:10.1152/jn.00338.2011." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 33 }, "id": "BMI35ureqy0Q", "outputId": "ea210c9e-9c1f-4c8d-e4f5-2eb1bf624246" }, "outputs": [ { "data": { "text/html": [ "
[fetch_atlas_yeo_2011] Dataset directory found: nilearn_data/yeo_2011\n",
              "
\n" ], "text/plain": [ "\u001b[1;34m[\u001b[0m\u001b[34mfetch_atlas_yeo_2011\u001b[0m\u001b[1;34m]\u001b[0m Dataset directory found: nilearn_data/yeo_2011\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yeo7_atlas = datasets.fetch_atlas_yeo_2011(data_dir=data_dir, verbose=1, n_networks=7)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 474 }, "id": "rCmxMbUTrfrq", "outputId": "0dbfc1fe-2e01-477b-dd1a-466747bcb2b3" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_4780/1865255007.py:1: RuntimeWarning: \n", "The image maps contains a single image.\n", "No color map needed.\n", " plotting.plot_prob_atlas(yeo7_atlas.maps)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotting.plot_prob_atlas(yeo7_atlas.maps)" ] }, { "cell_type": "markdown", "metadata": { "id": "mlJuy02Qsc5U" }, "source": [ "ในช่วงต้นของบทเรียน เราได้ทำการตรวจสอบแล้วว่าข้อมูล fMRI ที่เราโหลดมาได้ถูกทำการ preprocess ที่มีขั้นตอนของการ register ไปยัง MNI152 template เรียบร้อยแล้ว ทำให้เราสามารถใช้ atlas ที่เราโหลดมา มาดึงข้อมูลจากแต่ละบริเวณผ่านการเรียกใช้ `nilearn.maskers.NiftiMapsMasker` ได้เลย" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCDe3E3VuQ_w", "outputId": "028ff1a7-7ded-4d28-c540-8259288f0004" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape: (# time points, # ROIs) = (176, 7)\n" ] } ], "source": [ "masker = maskers.NiftiLabelsMasker(labels_img=yeo7_atlas.maps, standardize=\"zscore_sample\")\n", "roi_time_series = masker.fit_transform(fmri_data_one_subject)\n", "print(f\"Shape: (# time points, # ROIs) = {roi_time_series.shape}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "FemhUog2wVUT" }, "source": [ "จะเห็นได้ว่าผลลัพธ์ที่ได้คือ time signal ที่มีความยาว 176 จุด ทั้งหมด 7 เส้น ซึ่งแต่ละเส้นจะเป็นตัวแทนของบริเวณสมองตามที่ระบุไว้ใน atlas ทั้ง 7 บริเวณ\n", "\n", "บริเวณใดบริเวณหนึ่งของ atlas มักประกอบไปด้วย voxel จำนวนมาก (ยิ่งถ้าข้อมูล fMRI มี resolution สูง จำนวน voxel มักจะยิ่งเยอะ) ในขั้นตอนนี้ เราจึงนำเอา time signal จากทุก ๆ voxel ที่อยู่ในบริเวณเดียวกัน มารวมกันจนเกิดเป็น time signal เพียงตัวเดียว เพื่อใช้เป็นตัวแทนของบริเวณนั้น ซึ่งวิธีที่ง่ายที่สุดที่ใช้ในการรวมก็คือการนำเอา time signal ทั้งหมดในบริเวณเดียวกันมาเฉลี่ยรวมกัน (signal averaging)\n", "\n", "
\n", "\n", "การเปลี่ยนจากเดิมที่มีจำนวน node มากถึง 271,633 node (ตามจำนวน voxel) ให้เหลือเพียง 7 nodes (ตาม atlas) ช่วยให้เราสามารถ\n", "\n", "1. **ลดทรัพยากรการคำนวณ** ผ่านการลดขนาดของ adjacency matrix ทำให้นำไปใช้ประมวลผลด้วยโมเดลทางคณิตศาสตร์ สถิติ และ ปัญญาประดิษฐ์ได้อย่างรวดเร็วมากยิ่งขึ้น (เนื่องจากจำนวน parameters ที่ลดลง) และไม่ติดขัดเรื่องหน่วยความจำ (เนื่องจากขนาดของข้อมูลที่เล็กลง)\n", "\n", "2. **ลดสัญญาณรบกวน** โดยอาศัยหลักการ signal averaging ที่เราเคยเรียนกันไปแล้วในบทเรียน Signal Processing การหาค่าเฉลี่ยของ BOLD signal จากหลาย ๆ voxel ในบริเวณเดียวกัน จะช่วยลดสัญญาณรบกวนได้\n", "\n", "3. **ลดปัญหาตำแหน่งสมองไม่ตรงกันระหว่างบุคคล (Inter-Subject Alignment)** เนื่องจากโครงสร้างกายภาพของสมองแต่ละคนมีความแตกต่างกัน การอ้างอิงเป็นบริเวณกว้างตาม Atlas แทนการอ้างอิง voxel แบบจุดต่อจุด จะช่วยให้เราเปรียบเทียบข้อมูลสมองของต่างบุคคลในตำแหน่งหน้าที่เดียวกันได้ง่ายมากยิ่งขึ้น\n" ] }, { "cell_type": "markdown", "metadata": { "id": "M481aw0jboRw" }, "source": [ "### Defining Edges\n", "\n", "edge ใน graph แสดงถึงความสัมพันธ์ระหว่าง node แต่ละอัน ซึ่งในบริบทนี้ก็คือ functional connectivity ระหว่างพื้นที่สมองทั้ง 7 บริเวณตาม atlas\n", "\n", "ในการวัดความสัมพันธ์นี้ วิธีที่นิยมใช้อย่างแพร่หลายที่สุดคือการคำนวณ correlation matrix ซึ่งเป็นการวัด correlation ระหว่างคู่ node คือการนำเอา time signal ที่เป็นตัวแทนของคู่ node มาคำนวณหาค่า Pearson correlation coefficient เพื่อดูว่ามีรูปแบบการเปลี่ยนแปลงของ time signal จากทั้ง 2 บริเวณนั้นเป็นไปในทิศทางเดียวกันและพร้อมกันหรือไม่\n", "\n", "
\n", "\n", "ในบทเรียนนี้เราจะใช้ correlation เป็นตัวอย่างในการสร้าง edge ผ่านการใช้ `nilearn.connectome.ConnectivityMeasure(kind='correlation')`\n", "\n", "
\n", "\n", "อย่างไรก็ตาม ค่า correlation นั้นบอกเพียงความสัมพันธ์แบบไม่ระบุทิศทาง (edge ใน undirected graph) ทำให้เราทราบแค่ว่าสมองสองบริเวณนี้ทำงานสอดคล้องกัน แต่ไม่สามารถบอกได้ว่าบริเวณใดเป็นตัวกระตุ้นหรือส่งอิทธิพลไปยังอีกบริเวณหนึ่ง\n", "\n", "หากเราต้องการตอบคำถามเชิงลึกในทาง neuroscience ที่แม่นยำยิ่งขึ้น เราจำเป็นต้องพิจารณา causation เพื่อสร้างความสัมพันธ์แบบระบุทิศทาง (edge ใน directed graph) ด้วย" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4ePT5cy1Rayf" }, "outputs": [], "source": [ "correlation_measure = ConnectivityMeasure(kind='correlation')\n", "correlation_matrix = correlation_measure.fit_transform(roi_time_series)[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "xNmdjqzzbwSg" }, "source": [ "### Representing Graph as Functional Connectivity Matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 551 }, "id": "RQmrlbrwTLWz", "outputId": "7f3a6d2a-f348-4948-ca1d-9b0273149dcb" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.fill_diagonal(correlation_matrix, 0)\n", "\n", "# Standard 7 Yeo Network Names (ลำดับอาจจะผิดหมดก็ได้)\n", "yeo7_region_names = [f\"Network {i+1}\" for i in range(7)]\n", "plotting.plot_matrix(correlation_matrix, labels=yeo7_region_names,\n", " vmax=0.8, vmin=-0.8, colorbar=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "s7uzWV_Lb5tV" }, "source": [ "## Graph Visualization\n", "\n", "เราสามารถ visualize ตัว graph ที่ผ่านการ register ไปยัง template และใช้ atlas ที่ nilearn รองรับได้อย่างง่ายดาย ผ่านการเรียกใช้ `nilearn.plotting.view_connectome`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E6HJWV6zw6aQ", "outputId": "dd54e6d4-0565-4804-bbeb-ef509f74ca45" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of the coordinates: (7, 3)\n" ] } ], "source": [ "yeo7_atlas_coordinates = plotting.find_parcellation_cut_coords(labels_img=yeo7_atlas.maps)\n", "print(f\"Shape of the coordinates: {yeo7_atlas_coordinates.shape}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 821 }, "id": "YRDZ_YeJTPwD", "outputId": "df94fe78-8cd0-4856-c866-3bf1cbd4a9a1" }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotting.view_connectome(correlation_matrix, edge_threshold=0.2,\n", " node_coords=yeo7_atlas_coordinates)" ] }, { "cell_type": "markdown", "metadata": { "id": "rStoaXQN8auo" }, "source": [ "ผู้สอนหวังว่าในบทเรียนนี้จะช่วยให้ผู้เรียนเห็นภาพมากขึ้นว่าเราสามารถมอง fMRI เป็นข้อมูลประเภท graph ได้อย่างไร เพื่อให้มีพื้นฐานเพียงพอที่จะไปศึกษาต่อจาก paper ในสายงานนี้ได้อย่างง่ายดายมากยิ่งขึ้น\n", "\n", "## ผู้จัดทำ\n", "**ผู้จัดทำบทเรียน** ดร. อิทธิ ฉัตรนันทเวช\n", "\n", "**ผู้ตรวจสอบบทเรียน** นพ. ศศินทร์ ตรีรัตน์ และ นพ. เสฏฐนันท์ จารุเกษมกิจ\n", "\n" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }