shashankbansal56
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fitlins_tests
fork de shotgunosine/fitlins_tests


			
			
				
					
						
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>FitLins - -level report</title>
<style type="text/css">
.sub-report-title {}
.summary-heading {}
.run-title {}
.elem-title {}
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<body>
    <div id="summary">
        <h1 class="sub-report-title">Summary</h1>
        <ul class="elem-desc">
            <li>Dataset: Rhyme judgment (doi:<a href="https://doi.org/10.18112/openneuro.ds000003.v1.0.0">10.18112/openneuro.ds000003.v1.0.0</a>)</li>
            <li>Model: ds003_model001</li>
            <li>Participants (3): 01, 02, 03
        </ul>
    </div>
    <div id="model">
        <h1 class="sub-report-title">Model</h1>
        <details>
        <summary>Model specification</summary>
        <pre>{
  "bids_model_version": "1.0.0",
  "description": "",
  "edges": [
    {
      "destination": "t-test",
      "filter": {
        "contrast": [
          "task_vs_baseline",
          "word_gt_pseudo"
        ]
      },
      "source": "subject"
    },
    {
      "destination": "F-test",
      "filter": {
        "contrast": [
          "trial_type.word",
          "trial_type.pseudoword"
        ]
      },
      "source": "subject"
    }
  ],
  "input": {
    "task": "rhymejudgment"
  },
  "name": "ds003_model001",
  "nodes": [
    {
      "contrasts": [
        {
          "condition_list": [
            "trial_type.word",
            "trial_type.pseudoword"
          ],
          "name": "word_gt_pseudo",
          "test": "t",
          "weights": [
            1,
            -1
          ]
        },
        {
          "condition_list": [
            "trial_type.word",
            "trial_type.pseudoword"
          ],
          "name": "task_vs_baseline",
          "test": "t",
          "weights": [
            0.5,
            0.5
          ]
        }
      ],
      "dummy_contrasts": {
        "conditions": [
          "trial_type.word",
          "trial_type.pseudoword"
        ],
        "test": "t"
      },
      "group_by": [
        "subject"
      ],
      "level": "run",
      "model": {
        "x": [
          "trial_type.word",
          "trial_type.pseudoword",
          "framewise_displacement",
          "trans_x",
          "trans_y",
          "trans_z",
          "rot_x",
          "rot_y",
          "rot_z",
          "a_comp_cor_00",
          "a_comp_cor_01",
          "a_comp_cor_02",
          "a_comp_cor_03",
          "a_comp_cor_04",
          "a_comp_cor_05",
          1
        ]
      },
      "name": "subject",
      "transformations": {
        "instructions": [
          {
            "input": [
              "trial_type"
            ],
            "name": "Factor"
          },
          {
            "input": [
              "trial_type.word",
              "trial_type.pseudoword"
            ],
            "model": "spm",
            "name": "Convolve"
          }
        ],
        "transformer": "pybids-transforms-v1"
      }
    },
    {
      "dummy_contrasts": {
        "test": "t"
      },
      "group_by": [
        "contrast"
      ],
      "level": "dataset",
      "model": {
        "type": "glm",
        "x": [
          1
        ]
      },
      "name": "t-test"
    },
    {
      "contrasts": [
        {
          "condition_list": [
            "trial_type.word",
            "trial_type.pseudoword"
          ],
          "name": "any_words",
          "test": "F",
          "weights": [
            [
              1,
              0
            ],
            [
              0,
              1
            ]
          ]
        }
      ],
      "group_by": [],
      "level": "dataset",
      "model": {
        "type": "glm",
        "x": [
          "trial_type.word",
          "trial_type.pseudoword"
        ]
      },
      "name": "F-test"
    }
  ]
}</pre>
        </details>

        <!-- { % for node in nodes % } -->
        
        <h2>Subject level</h2>

        <!-- { % if loop.first %} -->
        <h3>Design matrices</h3>
        <p>A design matrix was generated for each subject. All but the
        first are collapsed, but each should be inspected for correctness.
        
        <details open>
        <summary class="heading-1">Subject: 01</summary>
        <div class="warning">
    The following confounds had NaN values for the first volume: framewise_displacement.
    The mean of non-zero values for the remaining entries was imputed.
    If another strategy is desired, it must be explicitly specified in
    the model.
</div>

        <img src="sub-01/figures/level-run_sub-01_design.svg" />
        <h4>Correlation matrix</h4>
        
        <p>The correlation matrix of a design matrix shows the correlation between
        each pair of regressors. Very high or low correlations among variables of
        interest (top left) or between variables of interest and nuisance regressors
        (top right) can indicate deficiency in the design. High correlations among
        nuisance regressors will generally have little effect on the model.
        </p>
        
        <img src="sub-01/figures/level-run_sub-01_corr.svg" />
        </details>
        
        <details>
        <summary><em>...</em></summary>
        
        
        <details>
        <summary class="heading-1">Subject: 02</summary>
        <div class="warning">
    The following confounds had NaN values for the first volume: framewise_displacement.
    The mean of non-zero values for the remaining entries was imputed.
    If another strategy is desired, it must be explicitly specified in
    the model.
</div>

        <img src="sub-02/figures/level-run_sub-02_design.svg" />
        <h4>Correlation matrix</h4>
        
        <img src="sub-02/figures/level-run_sub-02_corr.svg" />
        </details>
        
        
        <details>
        <summary class="heading-1">Subject: 03</summary>
        <div class="warning">
    The following confounds had NaN values for the first volume: framewise_displacement.
    The mean of non-zero values for the remaining entries was imputed.
    If another strategy is desired, it must be explicitly specified in
    the model.
</div>

        <img src="sub-03/figures/level-run_sub-03_design.svg" />
        <h4>Correlation matrix</h4>
        
        <img src="sub-03/figures/level-run_sub-03_corr.svg" />
        </details>
        
        
        </details>
        <!-- { % endif %} -->

        <h3>Contrasts</h3>
        <p>A contrast matrix was generated for each subject. Except
        in very rare cases, these should be identical, so these should be
        inspected to ensure no unexpected differences are present.</p>
        
        <details open>
        <summary class="heading-1">Subject: 01</summary>
        <img src="sub-01/figures/level-run_sub-01_contrasts.svg" />
        </details>
        
        <details>
        <summary><em>...</em></summary>
        
        
        <details>
        <summary class="heading-1">Subject: 02</summary>
        <img src="sub-02/figures/level-run_sub-02_contrasts.svg" />
        </details>
        
        
        <details>
        <summary class="heading-1">Subject: 03</summary>
        <img src="sub-03/figures/level-run_sub-03_contrasts.svg" />
        </details>
        
        
        </details>

        <!-- { % endfor %} -->
    </div>
    <div id="contrasts">
        <h1 class="sub-report-title">Contrasts</h1>

        
        <h2>Subject level</h2>
        
        <h3>Subject: 01</h3>
        
        <h4>word &gt; pseudo</h4>
        
        <img src="sub-01/figures/level-run_name-subject_sub-01_contrast-wordGtPseudo_stat-t_ortho.png" />
        
        
        <h4>task vs. baseline</h4>
        
        <img src="sub-01/figures/level-run_name-subject_sub-01_contrast-taskVsBaseline_stat-t_ortho.png" />
        
        
        <h4>trial_type.pseudoword</h4>
        
        <img src="sub-01/figures/level-run_name-subject_sub-01_contrast-trialTypePseudoword_stat-t_ortho.png" />
        
        
        <h4>trial_type.word</h4>
        
        <img src="sub-01/figures/level-run_name-subject_sub-01_contrast-trialTypeWord_stat-t_ortho.png" />
        
        
        
        <h3>Subject: 02</h3>
        
        <h4>word &gt; pseudo</h4>
        
        <img src="sub-02/figures/level-run_name-subject_sub-02_contrast-wordGtPseudo_stat-t_ortho.png" />
        
        
        <h4>task vs. baseline</h4>
        
        <img src="sub-02/figures/level-run_name-subject_sub-02_contrast-taskVsBaseline_stat-t_ortho.png" />
        
        
        <h4>trial_type.pseudoword</h4>
        
        <img src="sub-02/figures/level-run_name-subject_sub-02_contrast-trialTypePseudoword_stat-t_ortho.png" />
        
        
        <h4>trial_type.word</h4>
        
        <img src="sub-02/figures/level-run_name-subject_sub-02_contrast-trialTypeWord_stat-t_ortho.png" />
        
        
        
        <h3>Subject: 03</h3>
        
        <h4>word &gt; pseudo</h4>
        
        <img src="sub-03/figures/level-run_name-subject_sub-03_contrast-wordGtPseudo_stat-t_ortho.png" />
        
        
        <h4>task vs. baseline</h4>
        
        <img src="sub-03/figures/level-run_name-subject_sub-03_contrast-taskVsBaseline_stat-t_ortho.png" />
        
        
        <h4>trial_type.pseudoword</h4>
        
        <img src="sub-03/figures/level-run_name-subject_sub-03_contrast-trialTypePseudoword_stat-t_ortho.png" />
        
        
        <h4>trial_type.word</h4>
        
        <img src="sub-03/figures/level-run_name-subject_sub-03_contrast-trialTypeWord_stat-t_ortho.png" />
        
        
        
        
        <h2>T-test level</h2>
        
        <h3></h3>
        
        <h4>word &gt; pseudo</h4>
        
        <img src="figures/level-dataset_name-tTest_contrast-wordGtPseudo_stat-t_ortho.png" />
        
        
        
        <h3></h3>
        
        <h4>task vs. baseline</h4>
        
        <img src="figures/level-dataset_name-tTest_contrast-taskVsBaseline_stat-t_ortho.png" />
        
        
        
        
        <h2>F-test level</h2>
        
        <h3></h3>
        
        <h4>any_words</h4>
        
        <img src="figures/level-dataset_name-FTest_contrast-anyWords_stat-F_ortho.png" />
        
        
        
        
    </div>
    <div id="about">
        <h1 class="sub-report-title">About</h1>
        <ul>
            <li>Fitlins version: 0.9.2+132.g48b08df</li>
            <li>Fitlins command: <tt>/home/shank/miniconda3/envs/fitlins39/bin/fitlins /home/shank/HDDLinux/Stanford/data/narps-multiverse/OpenNeuro/ds000003/ /home/shank/HDDLinux/Stanford/fitlins/outputs/ds000003/nistats_blurto dataset --participant-label 01 02 03 --space MNI152NLin2009cAsym -d /home/shank/HDDLinux/Stanford/data/narps-multiverse/ds000003/derivatives/fmriprep -m /home/shank/HDDLinux/Stanford/fitlins/models/model-ds0003_smdl.json --estimator nistats --smoothing 5.0:l1:isoblurto --drift-model cosine -w /home/shank/HDDLinux/Stanford/fitlins/workdir/ds000003/nistats_blurto --n-cpus=3 --mem-gb=15 --debug</tt></li>
            <li>Date processed: 2022-01-13 13:34:15 -0800</li>
        </ul>
    </div>
</body>
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