Different Alterations of Cerebral Regional Homogeneity in Early-Onset and Late-Onset Parkinson's Disease

Sheng, Ke; Fang, Weidong; Zhu, Yingcheng; Shuai, Guangying; Zou, Dezhi; Su, Meilan; Han, Yu; Cheng, Oumei

doi:10.3389/fnagi.2016.00165

Cited by 20 publications

(22 citation statements)

References 52 publications

(73 reference statements)

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“…These are summarised briefly. Comparing early onset PD to late onset PD using ReHo, differences were found in the right putamen and left superior frontal gyrus ( Sheng et al, 2016 ). In PD patients suffering from pain resulting from the disease, a disconnection between right nucleus accumbens and left hippocampus as well as altered orbitofrontal and cerebellar fractional ALFF were revealed compared to PD patients without pain ( Polli et al, 2016 ).…”

Section: Evidence On Resting-state Fmri In Neurodegenerative Diseasesmentioning

confidence: 99%

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Hohenfeld

Werner

Reetz

2018

NeuroImage: Clinical

194

141

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Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity.While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others.For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias.Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.

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Section: Evidence On Resting-state Fmri In Neurodegenerative Diseasesmentioning

confidence: 99%

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Hohenfeld

Werner

Reetz

2018

NeuroImage: Clinical

194

141

View full text Add to dashboard Cite

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“…With the ReHo analysis, the dysfunction of neural synchronization in the cortico-striatalthalamo-cortical circuit (CSTS) (Zhang et al, 2015), visual processing circuit (Li et al, 2016), and emotional processing circuit (Hu et al, 2019) have been clarified in PD. In terms of age of onset, the synchronizations in both the right putamen and left superior frontal gyrus were different between EOPD and LOPD, and the ReHo values of the right putamen were negatively correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) total scores in LOPD that indicated the distribution of cerebral ReHo was associated with onset-age (Sheng et al, 2016). Sheng et al's (2016) study also found higher ReHo values in the left inferior temporal gyrus in EOPD and suggested that further studies are needed to confirm these findings and effects on the emotion regulation in EOPD.…”

Section: Introductionmentioning

confidence: 99%

ALFF and ReHo Mapping Reveals Different Functional Patterns in Early- and Late-Onset Parkinson’s Disease

Yue

Jiang²,

Shen³

et al. 2020

Front. Neurosci.

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Heterogeneity between late-onset Parkinson's disease (LOPD) and early-onset Parkinson's disease (EOPD) is mainly reflected in the following aspects including genetics, disease progression, drug response, clinical manifestation, and neuropathological change. Although many studies have investigated these differences in relation to clinical significance, the functional processing circuits and underlying neural mechanisms have not been entirely understood. In this study, regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) maps were used to explore different spontaneous brain activity patterns in EOPD and LOPD patients. Abnormal synchronizations were found in the motor and emotional circuits of the EOPD group, as well as in the motor, emotional, and visual circuits of the LOPD group. EOPD patients showed functional activity change in the visual, emotional and motor circuits, and LOPD patients only showed increased functional activity in the emotional circuits. In summary, the desynchronization process in the LOPD group was relatively strengthened, and the brain areas with changed functional activity in the EOPD group were relatively widespread. The results might point out different impairments in the synchronization and functional activity for EOPD and LOPD patients.

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“…However, subject dropout led to fewer available subjects at each successive timepoint, with 33 subjects remaining at year 4. Though we have used more subjects than previous studies which have successfully detected associations between rs-fMRI and PD severity/progression with 39, 22, and even 17 subjects [6,5,36], we expect a larger dataset would likely increase the predictive power of the models. Furthermore, the group of 33 subjects at year 4 had earlier-stage disease (mean disease duration 532 ± 346 versus 770 ± 565 days, p = 0.03) compared to the original 82 subjects.…”

Section: Limitationsmentioning

confidence: 99%

“…Regional homogeneity (ReHo) quantifies the similarity of each voxel's activity with that of its neighbors [4]. ReHo in the cerebellum and lingual gyrus has been shown to be positively correlated with disease severity, while ReHo in the putamen is negatively correlated with Parkinson's disease severity [5,6]. Amplitude of low frequency fluctuation (ALFF) and its normalized form, fractional ALFF (fALFF), are measures of the power of the low frequency resting-state signals [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Second, this work adopts and extensively optimizes a multivariate, machine learning approach. With higher statistical capacity, machine learning models are more likely to find complex associations between neuroimaging features and progression than the voxel-wise or univariate approaches which have been the predominant approach in the literature [5,6,10,11]. Through cross-validation, the accuracy of the predictive models is rigorously evaluated on heldout data, providing more confidence that the associations found will generalize to subjects beyond the current dataset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Longitudinal Disease Severity for Individuals with Parkinson’s Disease using Functional MRI and Machine Learning Prognostic Models

Nguyen¹,

Raval

Treacher³

et al. 2020

Preprint

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Parkinson's disease is the second most common neurodegenerative disorder and is characterized by the loss of ability to control voluntary movements. Predictive biomarkers of progression in Parkinson's Disease are urgently needed to expedite the development of neuroprotective treatments and facilitate discussions about disease prognosis between clinicians and patients. Resting-state functional magnetic resonance imaging (rs-fMRI) shows promise in predicting progression, with derived measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), having been previously been associated with current disease severity. In this work, ReHo and fALFF features from 82 Parkinson's Disease subjects are used to train machine learning predictors of baseline clinical severity and progression at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Depression Rating Scale (MDS-UPDRS) score. This is the first time that rs-fMRI and machine learning have been combined to predict future disease progression. The machine learning models explain up to 30.4% (R 2 = 0.304) of the variance in baseline MDS-UPDRS scores, 55.8% (R 2 = 0.558) of the variance in year 1 scores, and 47.1% (R 2 = 0.471) of the variance in year 2 scores with high statistical significance (p < 0.0001). For distinguishing high-and low-progression individuals (MDS-UPDRS score above or below the median), the models achieve positive predictive values of up to 71% and negative predictive values of up to 84%. The models learn patterns of ReHo and fALFF measures that predict better and worse prognoses. Higher ReHo and fALFF in regions of the default motor network predicted lower current severity and lower future progression. The rs-fMRI features in the temporal lobe, limbic system, and motor cortex were also identified as predictors. These results present a potential neuroimaging biomarker that accurately predicts progression, which may be useful as a clinical decision-making tool and in future trials of neuroprotective treatments.

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Different Alterations of Cerebral Regional Homogeneity in Early-Onset and Late-Onset Parkinson's Disease

Cited by 20 publications

References 52 publications

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

ALFF and ReHo Mapping Reveals Different Functional Patterns in Early- and Late-Onset Parkinson’s Disease

Predicting Longitudinal Disease Severity for Individuals with Parkinson’s Disease using Functional MRI and Machine Learning Prognostic Models

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