Brain gray matter volume changes associated with motor symptoms in patients with Parkinson’s disease

Kang, Dezhi; Chen, Fuyong; Wang, Fangyu; Wu, Guo‐Rong; Liu, Ying; Wu, Gang; Yu, Long; Lin, Yuanxiang; Zhang, Lin

doi:10.1186/s41016-015-0003-6

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Females also tend to report more nonmotor symptoms than males, including increased fatigue, apathy, cardiovascular symptoms, anhedonia, sensory dysfunction, constipation, sweating, and pain (Martinez-Martin et al, 2012;Solla et al, 2012). Across all patients with PD, neuroimaging studies show lower gray matter volume in the basal ganglia, motor cortex, and cerebellum and lower FA in the substantia nigra of PD patients compared with controls (Berman & Miller-Patterson, 2019;Geng, Li, & Zee, 2006;Kang et al, 2015;Zhang et al, 2015). In recent work by the ENIGMA-PD Working Group, individuals with PD (n = 2,367, M age = 63.4 ± 9.8, 36% females) showed widespread cortical thinning in parietal association areas, primary and supplementary motor areas, inferior temporal cortex, precuneus, and PCC, and smaller volumes in the bilateral putamen, hippocampus, amygdala and accumbens compared to healthy controls (n = 1,183, M age = 59.4 ± 12.3, 46% females) (Laansma, Bright, Al-Bachari, & Anderson, 2020), but sex-by-diagnosis interactions were not significant.…”

Section: Parkinson's Diseasementioning

confidence: 99%

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Salminen

Tubi

Bright

et al. 2021

Human Brain Mapping

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Sex is a biological variable that contributes to individual variability in brain structure and behavior. Neuroimaging studies of population-based samples have identified normative differences in brain structure between males and females, many of which are exacerbated in psychiatric and neurological conditions. Still, sex differences in MRI outcomes are understudied, particularly in clinical samples with known sex differences in disease risk, prevalence, and expression of clinical symptoms. Here we review the existing literature on sex differences in adult brain structure in normative samples and in 14 distinct psychiatric and neurological disorders. We discuss commonalities and sources of variance in study designs, analysis procedures, disease subtype effects, and the impact of these factors on MRI interpretation. Lastly, we identify key problems in the neuroimaging literature on sex differences and offer potential recommendations to address current barriers and optimize rigor and reproducibility. In particular, we emphasize the importance of large-scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analyses consortium, the UK Biobank, Human Connectome Project, and others to provide unprecedented power to evaluate sex-specific phenotypes in major brain diseases.

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Section: Parkinson's Diseasementioning

confidence: 99%

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Salminen

Tubi

Bright

et al. 2021

Human Brain Mapping

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“…Despite the abovementioned sex differences in clinical presentation and symptom expression, few neuroimaging studies have compared brain metrics in males and females with PD. Across all patients with PD, neuroimaging investigations show abnormal gray matter integrity in the basal ganglia, motor cortex, and cerebellum and lower FA in the substantia nigra [Berman and Miller-Patterson, 2019;Geng et al, 2006;Kang et al, 2015;Zhang et al, 2015]. Recently, the ENIGMA Parkinson's Disease Working Group revealed widespread cortical thinning in parietal association areas, primary and supplementary motor areas, inferior temporal cortex, precuneus, and PCC, smaller volumes in the bilateral putamen, hippocampus, amygdala and accumbens, and a temporal impact on thalamic volume over the course of disease (larger to smaller) in PD compared to healthy controls [van der Werf et al,], but sex by diagnosis interactions were not significant.…”

Section: Parkinson's Diseasementioning

confidence: 99%

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Salminen¹,

Tubi²,

Bright³

et al. 2020

Preprint

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Sex differences are found in the incidence and expression of psychiatric and neurodegenerative conditions, and many studies suggest these differences are influenced by innate biological differences between males and females and risk factors that interact with these differences. However, few studies have used neuroimaging to examine brain signatures of disease separately by sex, and many studies of sex differences have been based on small samples and their findings have not been replicated in larger cohorts. Large-scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analyses (ENIGMA) consortium, the UK Biobank, Human Connectome Project, and others offer an unprecedented source of power to address important questions about the role of sex as a risk or protective factor for suboptimal brain health, as well as sex-specific neuroimaging phenotypes in brain-related illnesses. Here we review the existing neuroimaging literature on sex differences in the human brain in healthy adults and those with the most common and debilitating psychiatric and age-related neurodegenerative conditions. Finally, we discuss key gaps in this literature and opportunities of large-scale collaborative efforts to identify, characterize, and explain how biological sex influences the humanbrain in health and disease.

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“…As shown in Tables 2 , 3 , the highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Therefore, machine learning methods on GM have achieved better classification performance than those on WM, which has indicated that PD has a greater effect on the brain regions of GM than WM (Feldmann et al, 2010 ; Ji et al, 2010 ; Kang et al, 2015 ). As seen in Table 4 , ReliefF method achieved the best classification performance: 89.66% (sensitivity), 80.01% (specificity), 84.92% (accuracy), and 84.84% (AUC).…”

Section: Resultsmentioning

confidence: 99%

Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning

Song

Zhang

Jiang

et al. 2021

Front. Comput. Neurosci.

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Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.

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Brain gray matter volume changes associated with motor symptoms in patients with Parkinson’s disease

Cited by 7 publications

References 13 publications

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning

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