ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

ADVANCES IN FUNCTIONAL MAGNETIC RESONANCE IMAGING FOR

THE EVALUATION OF NEURODEGENERATIVE DISEASES

João Pedro do Valle Varela1

Kerlen de Sousa Martins2

Alice Borem Camargos3

Júlia Pinheiro Amantéa Vilela4

Maria Luisa Araujo Lopes5

Izabel Brito Teixeira6

Hamilton Ricardo Moreira de Oliveira Carriço7

Jaqueline Carrara Folly Valente8

Ciro Cunha Daruich Tannus9

Isadora Larissa Morozewsky Costa10

Enrico Resende Carletti11

Rodney Freire Andrade12

Paulo Victor Elias Sobrinho13

Yago Machado dos Reis14

1 UniSãoCarlos

2 UniSãoCarlos

3 PUC Minas

4 PUC Minas

5 PUC Minas

6 PUC Minas

7 Unisul Pedra Branca

8 University of Vassouras

9 Unig Itaperuna

10 EMESCAM

11 Multivix Vitória

12 Famesc

13 Universidad Sudamericana

14 Unifeso

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

Beatriz Rebonato de Souza Ribeiro15

Wanessa de Oliveira Gualandi16

Abstract: Functional magnetic resonance imaging (fMRI) has become an indispensable tool in the

research and clinical assessment of neurodegenerative diseases such as Alzheimer’s, Parkinson’s

and amyotrophic lateral sclerosis (ALS). Its ability to map changes in cerebral blood ow associated

with neuronal activity provides insights into the mechanisms underlying these diseases, allowing for

advances in diagnosis and monitoring. This paper aims to analyze recent advances in the application

of fMRI in the assessment of neurodegenerative diseases, highlighting its usefulness in early

detection, monitoring disease progression and evaluating therapeutic responses. The literature review

explores the use of functional magnetic resonance imaging (fMRI) in the study of neurodegenerative

diseases, addressing technical advances, biomarkers based on functional connectivity, and deep

learning applications. The analysis includes studies on Alzheimer’s, Parkinson’s, multiple sclerosis

and frontotemporal dementia, with a focus on diagnosis, disease progression and potential therapies.

Advances in fMRI, such as resting-state functional connectivity and machine learning-based analysis,

have made it possible to identify specic patterns of altered brain connectivity associated with

neurodegenerative diseases. Recent studies show that fMRI can detect subtle changes in the early

stages of these conditions, enabling early interventions. In addition, the use of standardized protocols

and the integration of fMRI with other modalities, such as PET and EEG, have improved diagnostic

accuracy and provided a comprehensive view of brain alterations. Therefore, fMRI represents one

of the most promising technologies for understanding and managing neurodegenerative diseases. Its

technical and methodological advances are expanding its application, from early diagnosis to the

evaluation of personalized therapies. However, challenges such as inter-individual variability and high

costs still need to be overcome for this technology to be widely incorporated into clinical practice.

15 Centro Universitário Maurício de Nassau de Cacoa

16 UniRedentor University

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

Keywords: Magnetic Resonance Imaging; Neurology; Radiology and Diagnostic Imaging;

Neurodegenerative Diseases.

INTRODUCTION

Functional magnetic resonance imaging (fMRI) has revolutionized the diagnostic

and monitoring approach to neurodegenerative diseases, standing out for its ability to map brain

changes at the functional and structural level. Unlike traditional imaging modalities, fMRI offers

dynamic insights into neural connectivity and brain activity at rest or during specic tasks, and is

an indispensable tool for understanding the underlying mechanisms of diseases such as Alzheimer’s,

Parkinson’s, and multiple sclerosis (Bullmore and Sporns, 2018).

In recent years, technological advances have expanded the reach of fMRI, with emphasis

on the integration of machine learning techniques and an increase in spatial and temporal resolution.

These advances have made it possible not only to identify early changes in neural networks, but also

to differentiate stages of neurodegenerative diseases and predict their progression. For example, in

Alzheimer’s patients, changes in default mode network (DMN) connectivity have been associated

with cognitive and functional decline, indicating that fMRI can act as a reliable biomarker for early

diagnosis (Pereira et al., 2021).

In addition, fMRI has contributed to the development of precision medicine in neurology.

Recent studies show that brain connectivity patterns obtained by fMRI can help stratify patients based

on specic characteristics, allowing for more personalized treatments. In the context of Parkinson’s,

the technique has been used to map changes in motor and non-motor pathways, improving the

understanding of disease progression and therapeutic response (Smith et al., 2022).

These advances reinforce the role of fMRI as a powerful and promising tool in the ght

against neurodegenerative diseases. By integrating technological innovations and advanced analytical

approaches, the technique not only offers unprecedented perspectives for diagnosis and treatment, but

also enhances scientic understanding of the complexities of the human brain (Johnson et al., 2023).

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

This paper aims to analyze recent advances in the application of fMRI in the evaluation

of neurodegenerative diseases, highlighting its usefulness in early detection, monitoring disease

progression, and evaluating therapeutic responses.

MATERIALS AND METHODS

The literature review explores the use of functional magnetic resonance imaging (fMRI) in

the study of neurodegenerative diseases, addressing technical advances, functional connectivity-based

biomarkers, and deep learning applications. The analysis includes studies on Alzheimer’s, Parkinson’s,

multiple sclerosis and frontotemporal dementia, focusing on diagnosis, disease progression and

potential therapies.

Guiding Question:

How does fMRI contribute to the identication of biomarkers and advances in the diagnosis

and management of neurodegenerative diseases?

Boolean Markers:

- “Functional MRI” AND “Neurodegenerative Disorders”

- “fMRI Biomarkers” AND “Alzheimer’s Disease Progression”

- “Resting-State fMRI” AND “Parkinson’s Disease”

- “Deep Learning” AND “Functional Connectivity Biomarkers”

Inclusion Criteria:

Studies published between 2018 and 2023;

Peer-reviewed articles, including systematic reviews, meta-analyses, and experimental

studies;

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

Publications focused on biomarkers and advances in fMRI for neurodegenerative diseases.

Exclusion Criteria:

Work outside the delimited period;

Studies on imaging techniques without a focus on fMRI or biomarkers;

Articles with non-representative samples or limited methodology.

THEORETICAL FOUNDATION

Functional magnetic resonance imaging (fMRI) represents an essential tool in the

understanding of neurodegenerative diseases, allowing the mapping of brain changes based on

cerebral hemodynamics. By detecting variations in the level of blood oxygenation, fMRI assesses

the functional activity of specic brain regions during tasks or in a resting state, which is crucial for

studying diseases such as Alzheimer’s, Parkinson’s, and multiple sclerosis (Beckmann et al., 2022).

One of the central aspects of fMRI is its ability to identify early changes in brain circuits.

In Alzheimer’s, for example, studies show a reduction in functional connectivity in important brain

networks, such as the default mode network (DMN), even before the appearance of clinical symptoms.

This dysfunction in DMN is associated with beta-amyloid accumulation and synaptic degeneration,

which are crucial factors for the development of the disease (Zhang et al., 2022).

In the case of Parkinson’s, fMRI has been used to evaluate changes in motor and non-motor

networks. Recent research indicates that dysfunctions in the connectivity of the primary motor cortex

and in dopaminergic pathways are correlated with disease progression and characteristic motor

symptoms, such as tremors and muscle stiffness. These ndings help in stratifying patients and

targeting personalized therapies (Vanderah et al., 2023).

Another signicant advance is the application of fMRI in the study of multiple sclerosis.

The technique makes it possible to detect changes in functional connectivity that precede axonal

100

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

degeneration and loss of brain volume, critical factors for the worsening of the disease. This information

has been used to monitor the efcacy of immunomodulatory treatments and predict long-term clinical

outcomes (Harrison et al., 2021).

In addition, the combination of fMRI with other modalities, such as diffusion tensor imaging

(DTI) and high-resolution magnetic resonance imaging, has expanded its applicability. The integration

of structural and functional data allows for a more comprehensive analysis of brain changes, offering

a multimodal view of neurodegenerative diseases. This approach has been particularly useful in the

study of complex conditions such as frontotemporal dementia, where specic changes in cortical and

subcortical networks are often observed (Khan et al., 2023).

Finally, the use of articial intelligence (AI) has revolutionized the analysis of data obtained

by fMRI. Deep learning-based models have been developed to identify specic brain connectivity

patterns associated with different stages of neurodegenerative diseases, improving diagnostic accuracy

and enabling early interventions (Chen et al., 2022).

CONCLUSION

It is concluded that advances in functional magnetic resonance imaging (fMRI) have played

a crucial role in expanding knowledge about neurodegenerative diseases. fMRI’s ability to identify

changes in brain connectivity before clinical symptoms emerge offers a unique opportunity for early

diagnosis and more effective interventions. In the case of Alzheimer’s, Parkinson’s, and multiple

sclerosis, fMRI has allowed a deeper understanding of the underlying mechanisms, contributing to

the development of personalized treatments and continuous monitoring of disease progression.

In addition, the integration of fMRI with other imaging modalities and the use of articial

intelligence have enhanced its applicability, making it an indispensable tool for both research and

clinical practice. These innovations have facilitated the identication of specic patterns of brain

degeneration and dysfunction, allowing the stratication of patients and the personalization of

101

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

therapies, optimizing clinical outcomes.

Despite signicant advances, challenges such as high cost, technical complexity, and the need

for standardization in protocols still limit the broad application of fMRI. However, continued progress

in imaging and data analysis technologies, coupled with collaborative efforts between researchers,

healthcare professionals, and industries, promises to overcome these barriers, consolidating fMRI as

an essential pillar in the management of neurodegenerative diseases.

Thus, functional magnetic resonance imaging not only transforms the understanding of

neurodegenerative diseases, but also paves the way for a future in which personalized medicine and

precision neuroscience are the foundation of patient care.

REFERENCES

Bullmore, E., & Sporns, O. (2018). Complex brain networks: graph theoretical analysis of structural

and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.

Pereira, J. B., et al. (2021). Network-based biomarkers of Alzheimer’s disease progression using

functional MRI. Frontiers in Neuroscience, 15, 715.

Smith, R., et al. (2022). Functional MRI insights into Parkinson’s disease progression and treatment.

Journal of Neurology, 269(4), 2001-2013.

Johnson, L. A., et al. (2023). Advances in functional MRI for the study of neurodegenerative diseases.

NeuroImage Clinical, 37, 103112.

Beckmann, C. F., et al. (2022). Advances in resting-state fMRI for neurodegenerative disorders.

Annual Review of Neuroscience, 45, 343-366.

Zhang, Y., et al. (2022). Functional connectivity biomarkers in Alzheimer’s disease: Insights from

resting-state fMRI. Journal of Alzheimer’s Disease, 86(3), 1245-1260.

Vanderah, T. W., et al. (2023). Functional MRI and Parkinson’s disease: New horizons in diagnosis

102

ISSN: 2763-5724 / Vol. 05 - n 01 - ano 2025

and management. Movement Disorders Journal, 38(1), 22-34.

Harrison, D. M., et al. (2021). Functional MRI applications in multiple sclerosis: Current insights.

Multiple Sclerosis Journal, 27(4), 541-552.

Khan, S., et al. (2023). Multimodal neuroimaging in frontotemporal dementia: Functional and

structural perspectives. Neurobiology of Aging, 124, 13-25.

Chen, X., et al. (2022). Deep learning applications in fMRI for neurodegenerative diseases: A

systematic review. Articial Intelligence in Medicine, 132, 102368.