Complex networks reveal early MRI markers of Parkinson’s disease

Amoroso, Nicola; Rocca, Marianna La; Monaco, A.; Bellotti, R.; Tangaro, Sabina

doi:10.1016/j.media.2018.05.004

Cited by 117 publications

(67 citation statements)

References 70 publications

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“…Several studies [18][19][20] have achieved promising results for clinical applicability. Bron et al [18] organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multicenter data set.…”

Section: Discussionmentioning

confidence: 99%

“…is Scientific Programming challenge provided insight into the best strategies for computer-aided diagnosis of dementia. Amoroso et al [19] use MRI data from the Parkinson's Progression Markers Initiative (PPMI) to extract imaging markers and learn an accurate classification model. Heister et al [20] predicted MCI outcome with clinically available MRI and CSF biomarkers.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning for the Preliminary Diagnosis of Dementia

Zhu

Tang

et al. 2020

Scientific Programming

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Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning for the Preliminary Diagnosis of Dementia

Zhu

Tang

et al. 2020

Scientific Programming

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show abstract

“…To test the performance of different P values, two-sample t-tests were performed with three different P values (<0.05 [57][58][59], <0.01 [60], and <0.001 [61]). e cluster-size value was set to 50 [62][63][64][65], and three differentiated tissue maps of GM and WM were obtained and are shown in Figures 3 and 4.…”

Section: Two-sample T-testsmentioning

confidence: 99%

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. is study proposed an ML framework based on coarse-to-fine feature selection. e proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. e method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. e experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. e identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

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“…(1) PD patients had a very little gray matter atrophy, (2) white matter showed microstructural damage in PD patients, (3) the greatest difference evidenced in the pattern of white matter damage with moderate PD when compared to mild PD cases, and (4) the severity of white matter degeneration correlates with cognitive status. (Amoroso, La Rocca, Monaco, Bellotti, & Tangaro, 2018) made a classification model, the development is consisted in a brain connectivity based on grey and white matter voxel distribution; one that the segmentation threw the region interest, then they measure how different brain regions are correlated and for each region measure topological quantity. This model provides two important methodological understandings.…”

Section: White and Gray Mattermentioning

confidence: 99%

Review of prodromal symptoms in Parkinson's Disease detected by MRI, EEG And microbiome

Echeverri¹,

Vayá²,

Mateo³

et al. 2019

Preprint

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Context: Parkinson’s disease (PD) is catalogued as a disorder that causes motor symptoms; the evidence of literature shows the PD starts with non-motor signs, which can be detected in prodromal phases. These previous phases can be analyzed and studied through magnetic resonance images (MRI), electroencephalography (EEG) and microbiome.Objective: To systematically review the areas of the brain and brain-gut axis which affect in early Parkinson’s disease that can possibly be visualized and analyzed by MRI, EEG and the microbiome.Evidence acquisition: Pubmed and Embase databases were used until July 30, 2018 as to search for early Parkinson’s disease at its earliest non-motor symptoms stage by using MRI, EEG, and microbiome. The search was performed according to the requirements of a systematic review. In order to identify reports, we evaluated them following the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria. Evidence synthesis: MRI and EEG have provided the advances to find features for PD over the last decade. Those techniques identify motor symptoms on substantia nigra where the patient shows a dopamine deficiency. However, over recent years, researchers have found that PD has prodromal phases, that is, PD is not simply a neurodegenerative disorder characterized by the dysfunction of dopaminergic. Thus, high field MRI, event-related potential (ERP) and microbiota data shows a significant change on the brain cortex, white and grey matter, the extrapyramidal system, brain signals and the gut.Conclusion: The structural MRI is a useful technique in detecting the stages of motor symptoms on the substantia nigra in patients with PD. The use of magnetic resonance as an early detector requires a high magnetic field, as to identify the areas which diagnose that the patient could be in the premotor stages. On the other hand, EEG performed well in detecting PD features. Furthermore, microbiome sequencing might include the classification of bacterial families that could help to detect PD in its prodromal phase. Thus, the combination of all these techniques can support the possibility of diagnosing PD in its very early stages.

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Complex networks reveal early MRI markers of Parkinson’s disease

Cited by 117 publications

References 70 publications

Machine Learning for the Preliminary Diagnosis of Dementia

Machine Learning for the Preliminary Diagnosis of Dementia

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Review of prodromal symptoms in Parkinson's Disease detected by MRI, EEG And microbiome

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