Neurodegenerative diseases are a key health issue facing the world today as they are marked by the gradual destruction of neurons and their functions resulting in debilitating cognitive and movement disorders (1). Diseases like Alzheimer disease (AD) and Parkinson disease (PD) impact millions of people across the world, and their occurrence is likely to increase significantly as populations age (2). The pathological alterations that is years or decades before clinical manifestation of the disorders makes medical treatment of the disorders difficult and also presents possibilities as well. Early diagnosis has emerged as an important aspect of the disease management process and can allow some intervention, which can potentially help to 3 slow out the disease process and improve the quality of life of the patient (3).Magnetic Resonance Imaging (MRI) has become one of the most promising techniques in the early detection of neurodegenerative diseases, providing non-invasive information of brain structure and brain activity which previously could not be available in living patients (4). MRI has transformed the way we treat these disorders because it has the capability of detecting subtle changes in brain tissue before clinical symptoms develop. State-of-the-art MRI methods are capable of detecting microstructural changes, volumetric changes, and functional changes that can be used as early biomarkers of neurodegeneration (5).There is no use overstating the value of early diagnosis as it offers a valuable period of therapeutic intervention when brain damage can still be reversed or its course greatly reduced (6). Conventional methods of diagnosis have been based on clinical symptom presentation, which in most cases is presented after a considerable loss of neurons has already taken place. Such a constraint has necessitated the pressing need to obtain objective, sensitive biomarkers capable of detecting disease processes at their initial stages (7).The structural MRI methods have been shown to have an amazing ability to detect the pattern of regional brain atrophy depending on the anatomical distribution of the pathological changes in different neurodegenerative diseases (8). These two kinds of volumetric measurements, both manual and automated, show that the brain atrophies in a patient following stage-specific neuropathological pathways that are reminiscent of post-mortem results. Diffusion tensor imaging (DTI) is a particularly promising advanced technique as it has demonstrated the ability to detect alterations in white matter integrity in AD patients, where fractional anisotropy and diffusion are lower and greater than in healthy controls (9).Functional MRI methods have offered complementary information on the onset of neurodegeneration and have shown the disruption of regular brain activation states during cognitive processes even in those who are at risk of developing these diseases (10). The functional changes usually come first before a structural change, providing a further earlier insight into the pathogenesis of disease. Combining several MRI modalities has improved the accuracy of diagnosis and has provided a more detailed picture of disease processes (11).

Clinical implications of MRI-based early detection are not only limited to individual patient care applications but also to population health measures and clinical trial design. Early detection of vulnerable people allows recruiting them into clinical trials to compare previous treatment results objectively. Moreover, MRI is non-invasive and, therefore, it can be used to conduct repeated measurements to enable clinicians to assess the development of the disease and response to treatment over time.

With the continued improvement of machine learning methods, their combination with the analysis of MRI data will likely continue to improve the sensitivity and specificity of the early detection methods. These forms of computation can be used to discern subtle patterns in the image data that would otherwise not be visible to the older techniques in an analysis, establishing new biomarkers and enabling more accurate diagnoses. Hopefully, improvement of these neuroimaging techniques will provide the future of the cure of neurodegenerative ailment since it will not only provide prompter treatment but will also have improved results in millions of patients worldwide.

Study Design

This prospective, observational study will be conducted over a duration of 9 months at the Department of Radiology, Pacific Institute of Medical Sciences, Umarda, Udaipur, Rajasthan, India. The study will aim to explore the role of magnetic resonance imaging (MRI) in the early detection of neurodegenerative diseases. The study's design will involve recruiting 50 patients and collecting both clinical and imaging data at baseline, with potential follow-up scans to monitor changes over the study period. The dependent variables will be the MRI findings, while the independent variables will be the clinical characteristics and diagnoses of the patients.

Ethical Considerations

Before the commencement of the study, ethical approval will be obtained from the Institutional Ethics Committee of the Pacific Institute of Medical Sciences. The study will be conducted in accordance with the ethical principles for medical research involving human subjects. All participants will be provided with a detailed explanation of the study, and written informed consent will be obtained from each participant before their inclusion in the research. Patient data will be anonymized to ensure confidentiality and data protection.

Study Population

A total of 50 patients will be recruited for this study from the outpatient and inpatient departments of the Pacific Institute of Medical Sciences. Participants will be selected based on specific inclusion and exclusion criteria to ensure a homogenous study population.

• Inclusion Criteria:

  • Patients with early clinical signs and symptoms suggestive of a neurodegenerative disease.

  • Patients aged 50 years and above.

  • Patients who are willing to undergo an MRI scan and provide informed consent.

• Exclusion Criteria:

  • Patients with a history of other neurological disorders that could confound the MRI findings.

  • Patients with contraindications to MRI, such as metallic implants or claustrophobia.

  • Patients who are unable to give informed consent.

Data Collection

Data collection will be conducted through clinical assessments and MRI scans.

• Clinical Assessment: A detailed clinical history will be taken for each patient, including demographic information, presenting complaints, duration of symptoms, and relevant medical history. A thorough neurological examination will also be performed.

• MRI Data Acquisition: All MRI examinations will be performed using a 5 Tesla (Siemens) scanner. The imaging protocol will include a combination of structural and functional MRI sequences to assess different aspects of brain pathology. The following sequences may be included:

  • Structural MRI (sMRI): T1-weighted and T2-weighted images to assess brain anatomy, including cortical thickness and hippocampal volume.

  • Diffusion Tensor Imaging (DTI): To evaluate the integrity of white matter tracts, which can be affected in the early stages of neurodegenerative diseases.

  • Functional MRI (fMRI): To assess brain function and connectivity by measuring blood-oxygen-level-dependent (BOLD) signals.

  • Susceptibility-Weighted Imaging (SWI): To detect iron accumulation in the brain, which is a feature of some neurodegenerative disorders.

Image Processing and Analysis

The acquired MRI data will be processed and analyzed using specialized software. The analysis may involve:

• Voxel-Based Morphometry (VBM): To identify regional differences in gray matter volume between patients and a control group.

• Region of Interest (ROI) Analysis: To measure the volume and other characteristics of specific brain regions known to be affected by neurodegenerative diseases, such as the hippocampus and medial temporal lobe.

• Machine Learning Algorithms: Advanced techniques like convolutional neural networks (CNNs) could be employed to classify subjects and predict disease progression based on imaging features. A multi-modal approach combining different MRI techniques may improve diagnostic accuracy.

Statistical Analysis

Statistical analysis will be performed using appropriate statistical software (e.g., SPSS, R). Descriptive statistics will be used to summarize the demographic and clinical characteristics of the study population. Appropriate statistical tests will be used to compare MRI findings between different groups of patients and to analyze longitudinal changes over the 9-month study period. A p-value of less than 0.05 will be considered statistically significant.

The cohort of 50 individuals is categorized into four diagnosis groups. The largest group is the Prodromal AD (Alzheimer's Disease), with 20 individuals, making up 40% of the total. The second group, Control, consists of 15 individuals, or 30% of the cohort. The Prodromal PD (Parkinson's Disease) group includes 10 individuals, representing 20% of the cohort, while the smallest group, labeled Other Prodromal (including conditions like FTD, LBD, and MCI), comprises 5 individuals, accounting for 10% of the total. This distribution reflects a range of early-stage neurodegenerative conditions, with a higher concentration in the Prodromal AD group.

Table 1. Cohort Composition by Diagnosis Group (N = 50)

The demographic breakdown of the cohort reveals key information about sex, age, family history, and APOE4 status. In terms of sex, there are 26 males (52%) and 24 females (48%). Regarding age, the majority of participants are in the 60-69 age group, comprising 24 individuals (48%), followed by 16 individuals (32%) in the 70-79 group, 8 individuals (16%) in the 50-59 group, and 2 individuals (4%) aged 80 or older. For family history, 18 participants (36%) have a family history of neurodegenerative conditions, while 32 individuals (64%) do not. Lastly, regarding APOE4 status, 22 individuals (44%) are positive for the APOE4 gene, and 28 individuals (56%) are negative. This demographic profile provides insight into the composition of the cohort in relation to sex, age, genetic risk, and family history.

Table 2. Demographics by Category

The MRI data for each diagnosis group shows varying patterns of brain changes related to hippocampal atrophy, white matter hyperintensities (WMH), nigrosome 1 loss, and olfactory bulb abnormalities. In the Control group, the majority show no hippocampal atrophy (13 individuals), no WMH (12 individuals), and no nigrosome 1 loss (15 individuals), with 14 individuals having a normal olfactory bulb. In the Prodromal AD group, there is a more mixed distribution, with a significant number exhibiting mild (9) and moderate to severe (8) hippocampal atrophy, as well as mild (10) and moderate to severe (4) WMH. A notable proportion (20 individuals) do not show nigrosome 1 loss, and 12 individuals have a reduced olfactory bulb.

The Prodromal PD group mostly shows no hippocampal atrophy (7), no WMH (6), and a higher prevalence of nigrosome 1 loss (8). For the Other Prodromal (FTD/LBD/MCI) group, patterns are less pronounced, with mild hippocampal atrophy in 2 individuals and mild to moderate WMH in a few, but they show some nigrosome 1 loss (2) and a mix of normal and reduced olfactory bulbs. This table highlights the varying degree of neurodegenerative changes across the different diagnostic groups.

Table 3. MRI Categories by Diagnosis Group

The cognitive and clinical data shows distinct patterns across diagnosis groups. In the Control group, all participants scored within the normal range on both the MMSE (15 individuals) and MoCA (14 individuals), with no clinical symptoms reported within the first year. For the Prodromal AD group, the MMSE shows a distribution with 6 individuals in the normal range, 10 with mild cognitive impairment, and 4 with low scores. On the MoCA, 3 individuals are normal, while 10 show mild impairment, and 7 have low scores. Symptoms in this group are more pronounced, with 9 individuals experiencing symptoms for 1-2 years and 7 for more than 2 years.

In the Prodromal PD group, most participants (7) score normally on the MMSE, with 3 having mild impairments, and on the MoCA, 4 are normal, while 5 have mild impairments. Symptoms are more concentrated in the first year, with 6 individuals reporting symptoms within that time frame. In the Other Prodromal (FTD/LBD/MCI) group, there are fewer individuals with cognitive impairment, with 2 scoring normally on the MMSE and 1 on the MoCA. The symptom distribution is more evenly spread, with participants experiencing symptoms within 1-2 years and some beyond that. This table illustrates the varying levels of cognitive impairment and symptom onset across the different diagnosis groups.

Table 4. Cognitive/Clinical Categories

The diagnostic performance of various MRI markers is summarized by their sensitivity and specificity in detecting specific neurodegenerative changes. For Medial Temporal Atrophy, the threshold for moderate or worse atrophy shows a sensitivity of 70% and specificity of 93.3%, with 14 true positives, 2 false positives, 28 true negatives, and 6 false negatives. Nigrosome 1 Sign Loss, when present, demonstrates high accuracy with a sensitivity of 90% and specificity of 97.5%, identifying 9 true positives and only 1 false positive.

The WMH (White Matter Hyperintensities) Burden marker, at moderate or worse levels, is perfect in sensitivity (100%) but slightly lower in specificity (93.2%), with 6 true positives and 3 false positives. For Parietal Hypometabolism Surrogate, the presence of this marker shows a sensitivity of 84.6% and specificity of 89.2%, identifying 11 true positives, 4 false positives, and 2 false negatives. Lastly, the Olfactory Bulb Reduction marker, with a reduced threshold, shows a sensitivity of 85.7% and specificity of 83.3%, detecting 12 true positives and 6 false positives. These markers provide varying levels of diagnostic performance, with some, like nigrosome 1 loss, showing particularly high specificity and sensitivity.

Table 5. Diagnostic Performance Categories (Per MRI Marker)

These results are quite consistent with the current studies on MRI markers in neurodegenerative diseases. The good Nigrosome 1 Sign Loss performance as diagnostic tool is supported by prior reports where Schwarz et al. noted a sensitivity of 100 per cent and specificity of 91-93 per cent on SWI sequence (12). Other more recent studies by Dwivedi et al., reconfirmed similar findings using VenoBOLD and SWI sequences with sensitivity and specificity of 90% and 66.7% respectively with VenoBOLD and 94% and 80% respectively with SWi sequences (13). These synergistic results of several studies highlight the validity of nigrosome-1 imaging as a diagnosis of Parkinson disease.

The Prodromal AD group results of the Medial Temporal Atrophy (MTA) are consistent with the seminal studies by Scheltens et al., who have demonstrated that MTA on MRI is a strong clinical indicator of the diagnosis of Alzheimer’s disease and is highly correlated with memory functioning (14). These observations are also confirmed in the Radiology Assistant guidelines, which state that MTA-scores of 2 or higher are abnormal below the age of 75 years and 3 or higher are abnormal above the age of 75 years and that sensitivities and specificities are 85% to recognize AD (15). This consistency helps to support the clinical utility of MTA assessment in the detection of early AD.

Patterns of cognitive decline in the Prodromal AD group confirm the results of longitudinal studies; Wilson et al. reported that cognitive decline in prodromal AD starts approximately 4.5 years prior to the onset of MCI, and amnestic MCI exhibits earlier and faster progression than nonamnestic (16). Their results of accelerated global cognitive decline that is at least 10-fold ahead of amnestic MCI diagnosis are similar to the severe MMSE and MoCA losses in this study Prodromal AD cohort.

The results of the Olfactory Bulb Reduction of Prodromal PD are consistent with the diffusion tensor imaging results of Skorpil et al. who reported lower fractional anisotropy of olfactory bulbs in PD patients with the new DTI measures (17). These findings are further corroborated by a recent study by Kim et al. which found that the height of the olfactory bulb measured by MRI is highly reduced in idiopathic PD of all ages and sexes and is associated with motor symptom progression (18). These unanimous results among various approaches to MRI support the use of olfactory bulb in early PD diagnosis.

The results of the Parietal Hypometabolism Surrogate are consistent with the findings of metabolic imaging studies conducted by Chételat et al. who reported right temporo-parietal hypometabolism as a superior predictor of later cognitive deterioration in MCI compared to neuropsychological and structural MRI measurements (19). Patterns of anterior cingulate and parahippocampal hypometabolism in amnestic MCI on FDG-PET studies have steadily progressed to bilateral temporal and parietal lobes in the AD procession (20).

The reason why multiple MRI markers can be highly diagnostically accurate is that the evidence of multimodal imaging methods is increasingly growing in this research. As stated by Young et al., the integration of structural and functional MRI with machine learning methods could increase the diagnostic accuracy when compared with single modalities (21). In their systematic review, they stressed that the findings of neural networks built on gray matter density with the use of MRI with metabolic measures are superior to those of single-modality tests.

Certain results should be interpreted with care though. Recently, Jensen et al. showed that MRI characteristics of movement disorders might decrease diagnostic sensitivity without increasing specificity, especially in multiple system atrophy (22). This means that, promising as MRI markers are, they must be cautiously included in the clinical criteria so that diagnostic accuracy failures are avoided.

Findings in white matter burdens of the WMH Burden are consistent with current knowledge of white matter alterations in neurodegenerative disorders, but the literature indicates mixed findings based on the type of measurement methods and populations examined. Diffusion tensor imaging and other technologies have shown a consistent result of loss of white matter integrity in neurodegenerative pathologies and a loss of fractional anisotropy is a consistent result in many diseases.

The findings suggest that a combination of MRI markers and cognitive measures can be used to develop a tool that can potentially significantly enhance early diagnosis and differentiation of neurodegenerative diseases and offer a more appropriate intervention and management strategy. This multimodal methodology is consistent with the current best practices in neurodegeneration studies, in which the combination of imaging biomarkers and clinical measures offers the most viable diagnostic model.

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