---
description: Introduction writing — section tags, annotated well-written example, and usage for scientific manuscripts.
name: writing-introduction-example
tags: [scitex-writer, scitex-package]
---

# Scientific Introduction — Section Tags and Example

Continuation of [31_writing-introduction.md](31_writing-introduction.md).

## Section Tags

Insert the following section tags (START and END) to annotate the revised text:
- [START of 1. Opening Statement] ... [END of 1. Opening Statement]
- [START of 2. Importance of the Field] ... [END of 2. Importance of the Field]
- [START of 3. Existing Knowledge and Gaps] ... [END of 3. Existing Knowledge and Gaps]
- [START of 4. Limitations in Previous Works] ... [END of 4. Limitations in Previous Works]
- [START of 5. Research Question or Hypothesis] ... [END of 5. Research Question or Hypothesis]
- [START of 6. Approach and Methods] ... [END of 6. Approach and Methods]
- [START of 7. Overview of Results] ... [END of 7. Overview of Results]
- [START of 8. Significance and Implications] ... [END of 8. Significance and Implications]

## Example (Well-written Introduction)

[START of 1. Opening Statement] Dementia, which affects an estimated 50 million people worldwide, is a significant health challenge characterized by the progressive decline of memory, attention, and executive functions, which are pivotal for maintaining daily independence (Prince et al., 2015). [END of 1. Opening Statement] [START of 2. Importance of the Field] Accurate identification of its various subtypes, such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and idiopathic normal-pressure hydrocephalus (iNPH), is crucial for appropriate clinical management (Barker et al., 2002; Williams and Malm, 2016; McKeith et al., 2017; Arvanitakis et al., 2019; Leuzy et al., 2022). [END of 2. Importance of the Field]

[START of 4. Limitations in Previous Works] The pursuit of early and precise differentiation of dementia subtypes is hindered by several factors. Traditional diagnostic techniques, such as magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) tests, while effective, are costly, invasive, and not universally accessible, limiting their utility in certain regions (Garn et al., 2017; Hata et al., 2023). Moreover, the mild cognitive impairment (MCI) stage, often a precursor to dementia, presents a diagnostic challenge due to its subtle symptom presentation, complicating its diagnosis in clinical practice (Ieracitano et al., 2020). [END of 4. Limitations in Previous Works]

[START of 5. Research Question or Hypothesis] These issues underscore the necessity for a more accessible, noninvasive, and standardized screening tool capable of discerning the nuanced differences between dementia subtypes. [END of 5. Research Question or Hypothesis]

[START of 3. Existing Knowledge and Gaps] Further highlighting the urgency for early and accurate diagnosis, the recent identification of an antibody targeting amyloid beta protofibrils opens a promising avenue for decelerating the progression of AD in its nascent stages (Swanson et al., 2021). Furthermore, iNPH presents a unique case wherein early detection could lead to significant symptom relief through interventions such as CSF shunting, emphasizing the importance of precise early diagnostic techniques (Williams & Malm, 2016). [END of 3. Existing Knowledge and Gaps]

[Start of 6. Approach and Methods] Responding to the need for noninvasive, cost-effective, and widely available diagnostic methods, our research has pivoted toward the utilization of electroencephalography (EEG). This neurophysiological recording technique, renowned for its noninvasiveness and cost-effectiveness, holds untapped potential for enhancing the early detection and differentiation of dementia. [END of 6. Approach and Methods]

[START of 3. Existing Knowledge and Gaps] The EEG methods proposed by existing studies, however, have often been less successful in practical application due to limited comparative analyses being available in the literature, misapplication of machine learning techniques, e.g., the failure to appropriately segregate datasets from external institutions for independent testing compromises the assessment of a model's true classification accuracy across various clinical settings, and a dearth of generalizable findings (Rossini et al., 2008; Aoki et al., 2015; Bonanni et al., 2016; Ieracitano et al., 2020; Chatzikonstantinou et al., 2021; Sanchez-Reyes et al., 2021; Micchia et al., 2022). [END of 3. Existing Knowledge and Gaps]

[START of 6. Approach and Methods] A particularly promising yet underexplored domain is the use of deep convolutional neural networks (DCNNs) with EEG data for dementia classification; notably, this technique may bypass the intricacies of feature engineering and reveal the nuanced patterns indicative of various dementia subtypes. [END of 6. Approach and Methods]

In this study, we are committed to testing three pivotal hypotheses, each aimed at overcoming the challenges of early diagnosis of dementia and its diverse subtypes. Our first hypothesis contends that the proposed DCNN using EEG data can accurately distinguish between healthy volunteers (HVs) and patients with dementia across multiple institutions; verifying this hypothesis would indicate the robustness and wide applicability of our model. Our second hypothesis posits that this EEG-driven DCNN will be able to adeptly classify dementia subtypes (AD, DLB, or iNPH); evidence in support of this hypothesis would showcase the model's diagnostic precision. Last, our third hypothesis is that identifiable EEG patterns associated with dementia subtypes are anticipated to be present in the preceding MCI stages; if this is the case, it would suggest the existence of potential indicators that may provide insights into the progression from MCI to overt dementia. Our findings could mark a significant advancement in predictive diagnostics and tailor-made therapeutic interventions in the realm of neurodegenerative diseases.

## Usage

Provide your introduction draft text below. The revision will be returned in a LaTeX code block with section tags.

``` tex
YOUR REVISED INTRODUCTION
```

## Related

- [31_writing-introduction.md](31_writing-introduction.md) — role, aim, style/volume/miscellaneous rules, 8-section template
