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* Remove unused notebooks

* Update index.md

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6 changes: 3 additions & 3 deletions jupyter-book/_toc.yml
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- file: deep_learning
title: Deep learning
- file: background
title: External ressources
title: External resources

- caption: Preprocessing
chapters:
- file: notebooks/preprocessing
title: Prepare neurimaging data
title: Prepare neuroimaging data
- file: notebooks/label_extraction
title: Define your population
- caption: Deep learning
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- file: notebooks/random_search
title: Random search
- file: notebooks/interpretability
title: Interpret trained models
title: Interpret trained models
125 changes: 100 additions & 25 deletions jupyter-book/clinical.md
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# Clinical context: Alzheimer's disease

Alzheimer’s disease (AD) is the main type of dementia, which are diseases characterised by memory troubles, behavioural changes and cognitive issues. Given that the processes causing AD start many years before the symptoms appear, it is of great importance to find a way to identify, as early as possible, if a certain subject will develop AD dementia. This is important to provide adequate care to the patient and information to the family. Moreover, this is vital in order to provide an effective treatment in the future, as therapies are more likely to be effective if administered early. It is thus important to identify which patients should be included in clinical trials and/or could benefit of the treatment.

The diagnosis of AD mainly relies on clinical evaluation and cognitive assessment using neuropsychological tests. However, diagnosis has evolved thanks to advances in neuroimaging. Neuroimaging provides useful information such as atrophy due to gray matter loss with anatomical magnetic resonance imaging (MRI) or hypometabolism with <sup>18</sup>F-fluorodeoxyglucose positron emission tomography (FDG PET). A major interest is then to analyse those markers to identify dementia at an early stage.
Alzheimer’s disease (AD) is the main type of dementia, which are diseases
characterized by memory troubles, behavioral changes and cognitive issues. Given
that the processes causing AD start many years before the symptoms appear, it is
of great importance to find a way to identify, as early as possible, if a
certain subject will develop AD dementia. This is important to provide adequate
care to the patient and information to the family. Moreover, this is vital in
order to provide an effective treatment in the future, as therapies are more
likely to be effective if administered early. It is thus important to identify
which patients should be included in clinical trials and/or could benefit of the
treatment.

The diagnosis of AD mainly relies on clinical evaluation and cognitive
assessment using neuropsychological tests. However, diagnosis has evolved thanks
to advances in neuroimaging. Neuroimaging provides useful information such as
atrophy due to gray matter loss with anatomical magnetic resonance imaging (MRI)
or hypometabolism with <sup>18</sup>F-fluorodeoxyglucose positron emission
tomography (FDG PET). A major interest is then to analyze those markers to
identify dementia at an early stage.

## Publicly available Alzheimer's disease datasets

The first thing that we need to start working on MRI classification is a dataset. Three publicly available datasets have been mainly used for the study of AD: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Open Access Series of Imaging Studies (OASIS).
The first thing that we need to start working on MRI classification is a
dataset. Three publicly available datasets have been mainly used for the study
of AD: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian
Imaging, Biomarkers and Lifestyle (AIBL) and the Open Access Series of Imaging
Studies (OASIS).

<img src="../../images/logo_datasets.png" style="width: 500px;" alt="logos of the different databases" class="center">


### ADNI
The primary goal of [ADNI](http://adni.loni.usc.edu/) has been to test whether MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment and early AD. The ADNI study is composed of 4 cohorts: ADNI-1, ADNI-GO, ADNI-2 and ADNI-3. These cohorts are dependant and longitudinal, meaning that one cohort may include the same patient more than once and that different cohorts may include the same patients.
The primary goal of [ADNI](http://adni.loni.usc.edu/) has been to test whether
MRI, PET, other biological markers, and clinical and neuropsychological
assessment can be combined to measure the progression of mild cognitive
impairment and early AD. The ADNI study is composed of 4 cohorts: ADNI-1,
ADNI-GO, ADNI-2 and ADNI-3. These cohorts are dependant and longitudinal,
meaning that one cohort may include the same patient more than once and that
different cohorts may include the same patients.


### AIBL
Similarly to ADNI, [AIBL](https://aibl.csiro.au/adni/index.html) seeks to discover which biomarkers, cognitive characteristics, and health and lifestyle factors determine the development of AD. The AIBL project includes a longitudinal cohort of patients. Several modalities are present in the dataset, such as clinical and imaging (MRI and PET) data, as well as the analysis of blood and CSF samples.
Similarly to ADNI, [AIBL](https://aibl.csiro.au/adni/index.html) seeks to
discover which biomarkers, cognitive characteristics, and health and lifestyle
factors determine the development of AD. The AIBL project includes a
longitudinal cohort of patients. Several modalities are present in the dataset,
such as clinical and imaging (MRI and PET) data, as well as the analysis of
blood and CSF samples.

### OASIS
The [OASIS](https://www.oasis-brains.org/) project includes three cohorts, OASIS- 1, OASIS-2 and OASIS-3. The first cohort is only cross-sectional, whereas the other two are longitudinal. Available data is more limited than in ADNI with only few clinical tests and imaging data (MRI, and PET only in OASIS-3).
The [OASIS](https://www.oasis-brains.org/) project includes three cohorts,
OASIS- 1, OASIS-2 and OASIS-3. The first cohort is only cross-sectional, whereas
the other two are longitudinal. Available data is more limited than in ADNI with
only few clinical tests and imaging data (MRI, and PET only in OASIS-3).

*Data used in this tutorial were obtained from the ADNI database ([adni.loni.usc.edu](http://adni.loni.usc.edu/)) and the OASIS project ([www.oasis-brains.org](https://www.oasis-brains.org/)).*
*Data used in this tutorial were obtained from the ADNI database
*([adni.loni.usc.edu](http://adni.loni.usc.edu/)) and the OASIS project
*([www.oasis-brains.org](https://www.oasis-brains.org/)).*

## Neuroimaging techniques

Neuroimaging refers to a range of techniques used to visualize and study the structure and function of the brain. These techniques can be broadly categorized into structural imaging, which provides information about the physical structure of the brain, and functional imaging, which reveals how different regions of the brain are active during particular tasks or at rest.
Neuroimaging refers to a range of techniques used to visualize and study the
structure and function of the brain. These techniques can be broadly categorized
into structural imaging, which provides information about the physical structure
of the brain, and functional imaging, which reveals how different regions of the
brain are active during particular tasks or at rest.


### MRI
Magnetic Resonance Imaging (MRI) uses a strong magnetic field and radio waves to produce detailed images of the brain's anatomy. MRI can reveal structural abnormalities, such as tumors or lesions.
Magnetic Resonance Imaging (MRI) uses a strong magnetic field and radio waves to
produce detailed images of the brain's anatomy. MRI can reveal structural
abnormalities, such as tumors or lesions.

### PET
Positron Emission Tomography (PET) uses a radioactive tracer that is injected into the bloodstream to visualize metabolic activity in the brain. The tracer emits positrons, which are detected by a scanner to create a 3D image of the brain.
Positron Emission Tomography (PET) uses a radioactive tracer that is injected
into the bloodstream to visualize metabolic activity in the brain. The tracer
emits positrons, which are detected by a scanner to create a 3D image of the
brain.

### Others
There is others neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI), diffusion Tensor Imaging (DTI) or electroencephalography (EEG) but `clinicadl` only take PET and MRI.
There is others neuroimaging techniques such as functional Magnetic Resonance
Imaging (fMRI), diffusion Tensor Imaging (DTI) or electroencephalography (EEG)
but `clinicadl` only take PET and MRI.



## Labels of interest in the context of Alzheimer's disease

During the evolution of the disease, the diagnostic status of the participants may progress from cognitively normal, then to mild cognitive impairment, and finally to AD dementia. This diagnostic status is established by clinicians based on cognitive scores such as the Mini-Mental State Evaluation (MMSE) and the Clinical Dementia Rating (CDR). Even though assessment protocols are standardized, these scores can be highly variable based on the examiner and the physical/psychological condition of the patient during the test.
During the evolution of the disease, the diagnostic status of the participants
may progress from cognitively normal, then to mild cognitive impairment, and
finally to AD dementia. This diagnostic status is established by clinicians
based on cognitive scores such as the Mini-Mental State Evaluation (MMSE) and
the Clinical Dementia Rating (CDR). Even though assessment protocols are
standardized, these scores can be highly variable based on the examiner and the
physical/psychological condition of the patient during the test.

> The goal of ADNI neuropsychological testing is to use standardized procedures to objectively and reliably assess a subject’s cognitive abilities. However, **neuropsychological testing is not a mechanical process**. The examiner encounters a wide range of emotional and physical problems that can interfere with testing, and the skill and judgment of the examiner often affect the subject’s willingness to be tested and the effort he/she invests.
<i><div style="text-align: right"> Excerpt from the <a href="http://adni.loni.usc.edu/wp-content/uploads/2010/09/ADNI_GeneralProceduresManual.pdf">ADNI general procedure manual</a>, p.76 </div></i>

To limit the influence of the variabilities in the results of neuropsychological tests, diagnostic labels can be defined based on the stability of the diagnostic status over time:
- **CN** (cognitively normal): corresponds to subjects who were diagnosed as _cognitively normal_ during all their follow-up;
- **AD** (Alzheimer's disease): corresponds to subjects who were diagnosed as _demented_ during all their follow-up;
- **MCI** (mild cognitive impairment): corresponds to subjects who were diagnosed as _mild cognitive impairment_ at baseline, who did not encounter multiple reversions and conversions and who did not convert back to cognitively normal;
- **pMCI** (progressive MCI): corresponds to sessions of subjects who were diagnosed as _mild cognitive impairment_ at baseline, and _progressed to dementia_ during the <font color='blue'> 36 months (time horizon)</font> following the current visit;
- **sMCI** (stable MCI): correspond to sessions of subjects who were diagnosed as _mild cognitive impairment_ at baseline, _remained stable_ during the <font color='blue'> 36 months (time horizon)</font> following the current visit and _never progressed to dementia_.
To limit the influence of the variabilities in the results of neuropsychological
tests, diagnostic labels can be defined based on the stability of the diagnostic
status over time:
- **CN** (cognitively normal): corresponds to subjects who were diagnosed as
_cognitively normal_ during all their follow-up;
- **AD** (Alzheimer's disease): corresponds to subjects who were diagnosed as
_demented_ during all their follow-up;
- **MCI** (mild cognitive impairment): corresponds to subjects who were
diagnosed as _mild cognitive impairment_ at baseline, who did not encounter
multiple reversions and conversions and who did not convert back to cognitively
normal;
- **pMCI** (progressive MCI): corresponds to sessions of subjects who were
diagnosed as _mild cognitive impairment_ at baseline, and _progressed to
dementia_ during the <font color='blue'> 36 months (time horizon)</font>
following the current visit;
- **sMCI** (stable MCI): correspond to sessions of subjects who were diagnosed
as _mild cognitive impairment_ at baseline, _remained stable_ during the <font
color='blue'> 36 months (time horizon)</font> following the current visit and
_never progressed to dementia_.

<div class="alert alert-block alert-info">
<b>Time horizon:</b><p>
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## A word on the OASIS dataset

For this tutorial we have chosen to use [OASIS-1](https://www.oasis-brains.org/) because of its easy access, however it is quite different from other commonly used datasets ADNI and AIBL.
For this tutorial we have chosen to use [OASIS-1](https://www.oasis-brains.org/)
because of its easy access, however it is quite different from other commonly
used datasets ADNI and AIBL.

In OASIS-1, only two diagnostic status are given:
- CN (corresponding to a null CDR),
- AD (corresponding to a non-null CDR).

Here, a CDR of 0.5 corresponds to "probable AD" as for all patients with a non-null CDR.

Finally, be aware that the OASIS-1 dataset is cross-sectional and not longitudinal, meaning that there is only one asssessment per participant. This also means that the stability of the diagnostic status cannot be assessed.
Finally, be aware that the OASIS-1 dataset is cross-sectional and not
longitudinal, meaning that there is only one assessment per participant. This
also means that the stability of the diagnostic status cannot be assessed.



## A word on the ADNI dataset

For this tutorial we have also chosen to use [ADNI](http://adni.loni.usc.edu/) because it is one of the most well-know dataset in neuroimaging. It contains a large variety of MRI modalities, notably PET images.
For this tutorial we have also chosen to use [ADNI](http://adni.loni.usc.edu/)
because it is one of the most well-know dataset in neuroimaging. It contains a
large variety of MRI modalities, notably PET images.

In ADNI, the subjects have labels for their diagnostic :
- CN (corresponding to a null CDR),
- MCI (corresponding to a CDR of 0.5),
- AD (corresponding to a CDR of 1).

This definition of the diagnostic status is based on other neuropsychological scores corrected by the level of education of participants.
This definition of the diagnostic status is based on other neuropsychological
scores corrected by the level of education of participants.

Finally, be aware that the ADNI dataset is longitudinal, meaning that there can be more than one asssessment per participant. This also means that the stability of the diagnostic status can be calculated.
Finally, be aware that the ADNI dataset is longitudinal, meaning that there can
be more than one assessment per participant. This also means that the stability
of the diagnostic status can be calculated.

This dataset is bigger than OASIS1 and will be needed for some notebooks in order to work with progressive diagnoses and pet images.
This dataset is bigger than OASIS1 and will be needed for some notebooks in
order to work with progressive diagnoses and pet images.
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