Parkinson's Disease With Mild Cognitive Impairment Research Articles

Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics.

Cognitive impairment in Parkinson's disease (PD) is associated with changes in the brain anatomical structures. The objective of this study, is to identify the atrophy patterns based on the severity of cognitive decline and evaluate the disease progression. In this study, gray matter alterations are analysed in 135 PD subjects under 3 cognitive domains (91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D)) by comparing them with 58 Healthy Control (HC) subjects. Voxel Based Morphometry (VBM) is used to segment the gray matter regions in magnetic resonance images and analyse the atrophy patterns statistically. Significant patterns of gray matter variations observed in the middle temporal and medial frontal region differentiate between HC and PD subject groups based on the severity of cognitive decline. Abnormalities in gray matter is substantiated through radiomic features extracted from the significant gray matter clusters. Significant radiomic features of the clusters are able to differentiate between the HC and PD-D subjects with an accuracy of 81.82%. Higher atrophy levels identified in PD-D subjects compared to NC-PD and PD-MCI group enables early diagnosis and treatment procedures. The combined and comprehensive analysis of gray matter alterations through VBM and radiomic features gives better assessment of cognitive impairment in PD.

Oral Microbiota and Porphyromonas gingivalis Kgp Genotypes Altered in Parkinson's Disease with Mild Cognitive Impairment.

Cognitive impairment (CI) is a common complication of the non-motor symptoms in Parkinson's disease (PD), including PD with mild cognitive impairment (PD-MCI) and PD dementia. Recent studies reported the oral dysbiosis in PD and CI, respectively. Porphyromonas gingivalis (P. gingivalis), a pathogen of oral dysbiosis, plays an important role in PD, whose lysine-gingipain (Kgp) could lead to AD-type pathologies. No previous study investigated the composition of oral microbiota and role of P. gingivalis in PD-MCI. This study aimed to investigate the differences of oral microbiota composition, P. gingivalis copy number, and Kgp genotypes among PD-MCI, PD with normal cognition (PD-NC) and periodontal status-matched control (PC) groups. The oral bacteria composition, the copy number of P. gingivalis, and the Kgp genotypes in gingival crevicular fluid from PD-MCI, PD-NC, and PC were analyzed using 16S ribosomal RNA sequencing, quantitative real-time PCR, and MseI restriction. We found that the structures of oral microbiota in PD-MCI group were significantly different compared to that in PD-NC and PC group. The relative abundances of Prevotella, Lactobacillus, Megasphaera, Atopobium, and Howardella were negatively correlated with cognitive score. Moreover, there was a significant difference of Kgp genotypes among the three groups. The predominant Kgp genotypes of P. gingivalis in the PD-MCI group were primarily Kgp II, whereas in the PD-NC group, it was mainly Kgp I. The Kgp II correlated with lower MMSE and MoCA scores, which suggested that Kgp genotypes II is related to cognitive impairment in PD.

Investigation of the Relationship Between Akkermansia Genomic Diversity in Gut Microbiota and Parkinson's Disease Dementia

Although it is known that the relative abundance of Akkermansia, a bacterial genus commonly associated with health, increases in the gut microbiota of Parkinson's disease (PD) patients, the exact reason for this increase remains unclear. This study was aimed to identify potential changes in Akkermansia within the gut microbiota of PD patients in Türkiye. For this purpose, shotgun metagenomics and a novel Akkermansia genus-specific amplicon sequencing technique was used to investigate the presence of specific Akkermansia strains associated with cognitive impairment (CI) stages in PD and to examine potential genes within these strains. In this context, four gut microbiota samples from Türkiye -three PD with dementia (PDD) and one healthy control without CI (HC)- were analyzed by shotgun metagenomics and metagenome-assembled genomes assigned to Akkermansia genus were reconstructed. Then, a custom database was created by combining these genomes with the Akkermansia genomes in public databases and next generation sequencing (NGS) compatible primers specific to the genus Akkermansia were designed using this database. After optimization of amplification and library preparation steps for genus-specific next generation sequencing, gut microbiota samples from 64 PD patients [32 PDD and 32 PD with mild CI (PD-MCI)] and 26 HCs were analyzed by genus-specific amplicon sequencing. The results revealed the presence of seven strains assigned to Akkermansia muciniphila in gut microbiota samples, two of which showed significant distribution differences (p< 0.05) between demented (PDD) and non-demented groups (PD-MCI, HC). When gene contents of the detected Akkermansia genomes were examined through comparative genomic analysis, the presence of 12 genes only in Akkermansia genomes specific to non-demented groups were predicted. The annotations of these genes showed that they were not reported before with unknown functions. In this study, for the first time, gut microbiota samples from PD patients in Türkiye were analyzed using shotgun metagenomics, a novel genus-specific amplicon sequencing method was developed specifically for the analysis of Akkermansia genus, and then Akkermansia strains and genes potentially associated with CI stages in PD were identified using this method. The results underscore that investigating the species or strain level differences could help better understanding of the changes associated with PD in the human gut microbiota.

Advanced Cognitive Patterns in Multiple System Atrophy Compared to Parkinson’s Disease: An Individual Diffusion Tensor Imaging Study

Although both Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, they have divergent clinical courses and prognoses. The degeneration of white matter has a considerable impact on cognitive performance, yet it is uncertain how PD and MSA affect its functioning in a similar or different manner. In this study, a total of 116 individuals (37 PD with mild cognitive impairment (PD-MCI), 37 MSA (parkinsonian variant) with mild cognitive impairment (MSA-MCI), and 42 healthy controls) underwent diffusion tensor imaging (DTI) and cognitive assessment. Utilizing probabilistic fiber tracking, association fibers, projection fibers, and thalamic fibers were reconstructed. Subsequently, regression, support vector machine, and SHAP (Shapley Addictive exPlanations) analyzes were conducted to evaluate the association between microstructural diffusion metrics and multiple cognitive domains, thus determining the white matter predictors of MCI. MSA-MCI patients exhibited distinct white matter impairment extending to the middle cerebellar peduncle, corticospinal tract, and cingulum bundle. Furthermore, the fractional anisotropy (FA) and mean diffusivity (MD)values of the right anterior thalamic radiation were significantly associated with global efficiency (FA: B=0.69, P<0.001, VIF=1.31; MD: B=-0.53, P=0.02, VIF=2.50). The diffusion metrics of white matter between PD-MCI and MSA-MCI proved to be an effective predictor of the MCI, with an accuracy of 0.73 (P<0.01), and the most predictive factor being the MD of the anterior thalamic radiation. Our results demonstrated that MSA-MCI had a more noticeable deterioration in white matter, which potentially linked to various cognitive domain connections. Diffusion MRI could be a useful tool in comprehending the neurological basis of cognitive impairment in Parkinsonian disorders.

Structural changes in the retina and serum HMGB1 levels are associated with decreased cognitive function in patients with Parkinson's disease

BackgroundCognitive impairment is a serious nonmotor symptom in patients with Parkinson's disease (PD). Currently, there are few studies investigating the relationship of serum markers and retinal structural changes with cognitive function in PD. ObjectiveTo investigate the relationship between retinal structural changes, serum high mobility group box-1 (HMGB1) levels and cognitive function and motor symptoms in PD patients. MethodsEighty-nine participants, including 47 PD patients and 42 healthy subjects, were enrolled. PD patients were divided into Parkinson's disease with normal cognitive (PD-NC), Parkinson's disease with mild cognitive impairment (PD-MCI), and Parkinson's disease with dementia (PDD) groups. The motor and nonmotor symptoms of PD patients were evaluated with clinical scale. Serum HMGB1 levels were detected by enzyme-linked immunosorbent assay (ELISA), and ganglion cell-inner plexiform layer complex (GCIPL) thickness changes in the macula were quantitatively analyzed by swept source optical coherence tomography (SS-OCT) in all patients. ResultsCompared with the control group, the macular GCIPL (t = −2.308, P = 0.023) was thinner and serum HMGB1 (z = −2.285, P = 0.022) was increased in PD patients. Macular GCIPL thickness in patients with PD-MCI and PDD were significantly lower than that in PD-NC patients, but there were no significant difference between the PD-MCI and PDD groups. Serum HMGB1 levels in patients with PD-MCI and PDD were significantly higher than those in PD-NC patients, and serum HMGB1 levels in PDD patients were higher than those in PD-MCI patients. Correlation analysis showed that serum HMGB1 levels in PD patients were positively correlated with disease duration, HY stage, UPDRS-I score, UPDRS-III score, and UPDRS total score and negatively correlated with MOCA score. Macular GCIPL thickness was negatively correlated with HY stage and positively correlated with MOCA score, and macular GCIPL thickness was negatively correlated with serum HMGB1 level. Logistic regression analysis showed that elevated serum HMGB1 level, thinner macular GCIPL thickness, and higher HY stage were independent risk factors for Parkinson's disease with cognitive impairment (PD-CI). The areas under the receiver operating characteristic curve (AUC) for the serum HMGB1 level and macular GCIPL thickness-based diagnosis of PD-MCI, PDD and PD-CI based on in patients with PD were 0.786 and 0.825, 0.915 and 0.856, 0.852 and 0.841, respectively. The AUC for the diagnosis of PD-MCI, PDD and PD-CI with serum HMGB1 level and GCIPL thickness combined were 0.869, 0.967 and 0.916, respectively. ConclusionThe macular GCIPL thickness and serum HMGB1 level are potential markers of cognitive impairment in PD patients, and their combination can significantly improve the accuracy of the diagnosis of cognitive impairment in PD.

Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia.

Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N=276) compared to non-purified CSF (N=169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.

Validation of the European Cross-Cultural Neuropsychological Test Battery (CNTB) for the assessment of mild cognitive impairment due to Alzheimer's disease and Parkinson's disease.

The Cross-Cultural Neuropsychological Test Battery (CNTB) is a novel test battery specifically designed to reduce the impact of multiculturality in cognitive assessment. We aimed to validate the CNTB in Spaniards in patients with Alzheimer's disease (AD), including patients at mild cognitive impairment (MCI) and mild dementia stages, and Parkinson's disease with MCI (PD-MCI). Thirty patients with AD-MCI, 30 with AD-dementia (AD-D), and 30 with PD-MCI were recruited. Each clinical group was compared against a healthy control group (HC) with no differences in sex, age, or years of education. Intergroup comparisons, ROC analysis, and cut-off scores were calculated. AD-MCI scored lower than HC in those subtests associated with episodic memory and verbal fluency. AD-D also showed lower scores in executive functions and visuospatial tests. Effect sizes for all the subtests were large. PD-MCI showed lower performance than HC in memory and executive functions, particularly on error scores, with large effect sizes. Comparing AD-MCI and PD-MCI, AD-MCI had lower memory scores, while PD-MCI showed the worst performance in executive functions. CNTB showed appropriate convergent validity with standardized neuropsychological tests measuring the same cognitive domains. We obtained similar cut-off scores to previous studies performed in other populations. The CNTB showed appropriate diagnostic properties in AD and PD, including those stages with mild cognitive impairment. This supports the utility of the CNTB for the early detection of cognitive impairment in AD and PD.

Cortical connectivity of cholinergic basal forebrain in Parkinson's disease with mild cognitive impairment.

Cholinergic basal forebrain (BF) pathology is a hallmark of Parkinson's disease (PD) with mild cognitive impairment (PD-MCI). Assessment of functional connectivity (FC) of different cholinergic BF nuclei may deepen the understanding of PD-MCI pathogenesis. Seed-based FC analysis was performed with bilateral medial septal nucleus, the nucleus of the vertical limb of the diagonal band, nucleus of the horizontal limb of the diagonal band (Ch1-3), and the nucleus basalis of Meynert (NBM/Ch4) to explore the BF functional alterations in different frequency bands. Correlations between FC values of abnormal regions and scores of cognitive domains and depression in the PD group were also assessed. For the right Ch4, in the conventional frequency band, the PD-MCI group exhibited lower FC values in the right middle cingulate and paracingulate gyri, middle frontal gyrus, left inferior parietal gyrus, and superior frontal gyrus compared with healthy controls (HC), and in the left calcarine fissure and surrounding cortex compared with PD with normal cognition (PD-NC). For the slow 4 subbands, the PD-MCI group showed significantly lower FC values in the left putamen, middle frontal gyrus, right middle frontal gyrus, and precuneus compared with HC, and in right middle frontal gyrus cingulate and paracingulate gyri compared with the PD-NC group. For the slow 5 subbands, the PD-MCI group showed increased FC values in the right calcarine fissure and surrounding cortex, and left cerebellum. For the left Ch1-3, FC values in the right middle cingulate and paracingulate gyri were lower in patients with PD-MCI than in the PD-NC group in slow 4 subbands. Furthermore, altered FC values in the cortical regions for Ch4 seed were possibly correlated with depression and different cognitive domain scores. The study identified an imbalanced association between different cholinergic BF nuclei and cortical regions in patients with PD-MCI, and showed that FC changes are frequency-specific, which may provide new insights into functional alterations within the cholinergic system in cognitive impairment associated with PD.

Local white matter abnormalities in Parkinson's disease with mild cognitive impairment: Assessed with neurite orientation dispersion and density imaging.

Mild cognitive impairment is a nonmotor complication in Parkinson's disease (PD) that have a high risk of developing dementia. White matter is associated with cognitive function in PD and the alterations may occur before the symptoms of the disease. Previous diffusion tensor imaging (DTI) studies lacked specificity to characterize the concrete contributions of distinct white matter tissue properties. This may lead to inconsistent conclusions about the alteration of white matter microstructure. Here, we used neurite orientation dispersion and density imaging (NODDI) and white matter fiber clustering method to uncover local white matter microstructures in PD with mild cognitive impairment (PD-MCI). This study included 23 PD-MCI and 20 PD with normal cognition (PD-NC) and 21 healthy controls (HC). To probe specific and fine-grained differences, metrics of NODDI and DTI in white matter fiber clusters were evaluated using along-tract analysis. Our results showed that PD-MCI patients had significantly lower neurite density index (NDI) and orientation dispersion index (ODI) in white matter fiber clusters in the prefrontal region. Correlation analysis and receiver operating characteristic (ROC) analysis revealed that the diagnostic performance of NODDI-derived metrics in cingulum bundle (2 clusters) and thalamo-frontal (2 clusters) were superior to DTI metrics. Our study provides a more specific insight to uncover local white matter abnormalities in PD-MCI, which benefit understanding the underlying mechanism of cognitive decline in PD and predicting the disease in advance.

Classification and staging of Parkinson's disease using video-based eye tracking

Introduction83% of those diagnosed with Parkinson's Disease (PD) eventually progress to PD with mild cognitive impairment (PD-MCI) followed by dementia (PDD) - suggesting a complex spectrum of pathology concomitant with aging. Biomarkers sensitive and specific to this spectrum are required if useful diagnostics are to be developed that may supplement current clinical testing procedures. We used video-based eye tracking and machine learning to develop a simple, non-invasive test sensitive to PD and the stages of cognitive dysfunction. MethodsFrom 121 PD (45 Cognitively Normal/45 MCI/20 Dementia/11 Other) and 106 healthy controls, we collected video-based eye tracking data on an interleaved pro/anti-saccade task. Features of saccade, pupil, and blink behavior were used to train a classifier to predict confidence scores for PD/PD-MCI/PDD diagnosis. ResultsThe Receiver Operator Characteristic Area Under the Curve (ROC-AUC) of the classifier was 0.88, with the cognitive-dysfunction subgroups showing progressively increased AUC, and the AUC of PDD being 0.95. The classifier reached a sensitivity of 83% and a specificity of 78%. The confidence scores predicted PD motor and cognitive performance scores. ConclusionBiomarkers of saccade, pupil, and blink were extracted from video-based eye tracking to create a classifier with high sensitivity to the landscape of PD cognitive and motor dysfunction. A complex landscape of PD is revealed through a quick, non-invasive eye tracking task and our model provides a framework for such a task to be used as a supplementary screening tool in the clinic.

Distinct Bile Acid Signature in Parkinson's Disease With Mild Cognitive Impairment.

BackgroundsBile acid (BA) plays a crucial role in various neurodegenerative diseases, including Parkinson's disease (PD). However, no clinical evidence supports BA's potential role in patients with PD with mild cognitive impairment (PD-MCI).ObjectivesThis study aimed at investigating the differential BA profile between patients with PD-MCI and those with normal cognitive function (PD-NC).MethodsUltra-high performance liquid chromatography-MS/MS was applied for BA quantitation. After between-group differences of the BA profile were addressed, orthogonal projections to latent structures—discriminant analysis (OPLS-DA) and the area under the receiver-operating-characteristic curve (AUC-ROC) were implemented for further verification.ResultsLower levels of chenodeoxycholic acid (CDCA), cholic acid (CA), and ursodeoxycholic acid (UDCA) were significantly associated with PD-MCI (p < 0.01 for both; VIP ≈ 2.67, 1.66, and 1.26, respectively). AUC-ROC were 78.1, 74.2, and 74.5% for CDCA, CA, and UDCA, respectively.ConclusionCA, CDCA, and UDCA might be distinct BA signatures for patients with PD-MCI.

Morphological basis of Parkinson disease-associated cognitive impairment: an update

Cognitive impairment is one of the most salient non-motor symptoms of Parkinson disease (PD) that poses a significant burden on the patients and carers as well as being a risk factor for early mortality. People with PD show a wide spectrum of cognitive dysfunctions ranging from subjective cognitive decline and mild cognitive impairment (MCI) to frank dementia. The mean frequency of PD with MCI (PD-MCI) is 25.8% and the pooled dementia frequency is 26.3% increasing up to 83% 20years after diagnosis. A better understanding of the underlying pathological processes will aid in directing disease-specific treatment. Modern neuroimaging studies revealed considerable changes in gray and white matter in PD patients with cognitive impairment, cortical atrophy, hypometabolism, dopamine/cholinergic or other neurotransmitter dysfunction and increased amyloid burden, but multiple mechanism are likely involved. Combined analysis of imaging and fluid markers is the most promising method for identifying PD-MCI and Parkinson disease dementia (PDD). Morphological substrates are a combination of Lewy- and Alzheimer-associated and other concomitant pathologies with aggregation of α-synuclein, amyloid, tau and other pathological proteins in cortical and subcortical regions causing destruction of essential neuronal networks. Significant pathological heterogeneity within PD-MCI reflects deficits in various cognitive domains. This review highlights the essential neuroimaging data and neuropathological changes in PD with cognitive impairment, the amount and topographical distribution of pathological protein aggregates and their pathophysiological relevance. Large-scale clinicopathological correlative studies are warranted to further elucidate the exact neuropathological correlates of cognitive impairment in PD and related synucleinopathies as a basis for early diagnosis and future disease-modifying therapies.

Association Between White Matter Connectivity and Early Dementia in Patients With Parkinson Disease.

Several clinical and neuroimaging biomarkers have been proposed to identify individuals with Parkinson disease (PD) who are at risk for ongoing cognitive decline. This study aimed to explore whether white matter (WM) connectivity disruption is associated with dementia conversion in patients with newly diagnosed PD with mild cognitive impairment (PD-MCI). Seventy-five patients with drug-naive PD-MCI who underwent serial cognitive assessments during the follow-up period (>5 years) were enrolled for the neuroimaging analyses. The patients were classified into either the PD with dementia (PDD) high-risk group (PDD-H, n = 38) or low-risk group (PDD-L, n = 37), depending on whether they converted to dementia within 5 years of PD diagnosis. We conducted degree-based statistic analyses based on a graph-theoretical concept to identify the subnetworks whose WM connectivity was disrupted in the PDD-H group compared with the PDD-L group. The PDD-H group showed poorer cognitive performance on frontal/executive, visual memory/visuospatial, and attention/working memory/language function than the PDD-L group at baseline assessment. The PDD-H group exhibited more severely disrupted WM connectivity in both frontal and posterior cortical regions with 8 hub nodes in the degree-based statistic analysis. The strength of structural connectivity within the identified subnetworks was correlated with the composite scores of frontal/executive function domain (γ = 0.393) and the risk score of PDD conversion within 5 years (γ = -0.480). This study demonstrated that disrupted WM connectivity in frontal and posterior cortical regions, which correlated with frontal/executive dysfunction, is associated with early dementia conversion in PD-MCI. This study provides Class II evidence that disrupted WM connectivity in frontal and posterior cortical regions is associated with early dementia conversion in PD-MCI.

Neuropsychological and regional cerebral blood flow of posterior parietal area features in patients with Parkinson's disease with mild cognitive impairment

This study aimed to clarify associations between neuropsychological scales and regional cerebral blood flow (rCBF) of on ‍123I-IMP-SPECT in patients with Parkinson's disease with mild cognitive impairment (PD-MCI). Forty-two participants (mean age, 65.5 ± 8.9 years; mean disease duration, 11.1 ±5.7 years) were evaluated using the Wechsler Adult Intelligence Scale, third edition (WAIS-III), Wechsler Memory Scale, revised (WMS-R), Stroop test, Category word fluency, Auditory verbal learning test, Raven colored progressive matrices, Trail Making Test-B, and Clock drawing test. Participants were classified into PD-MCI and PD non-demented (PD-ND) using ten of these scales or its subtests. The rCBF of the posterior cingulate gyrus, precuneus, and parietal lobes was evaluated by ‍123I-IMP-SPECT using the easy Z-‍score imaging system (eZIS analysis). Extent was the extent index of voxels showing z-score > 2, and Severity was mean z-score in those regions on eZIS analysis. Cingulate island sign score (CIScore) was the ratio of integrated z-scores of the posterior cingulate gyrus to those of the posterior cortex.Twenty-three participants were diagnosed with PD-MCI (55%). The rCBF indices were significantly increased in the PD-MCI group compared to the PD-ND group (Extent: P = 0.047; CIScore: P = 0.006). These indices were significantly correlated with WAIS-III Processing Speed (Extent: P = 0.041, R = -0.317; Severity: P = 0.047, R = -0.309), Stroop effect (Extent: P = 0.003, R = 0.443; Severity: P = 0.004, R = 0.437), WMS-R Visual memory (Extent: P = 0.019, R = -0.361; Severity: P = 0.014, R = -0.375), and Delayed memory score (Extent: P = 0.005, R = -0.423; Severity: P = 0.044, R = -0.312). The rCBF indices showed no correlations with the number of impaired cognitive domains. Collectively, decreased posterior parietal area rCBF and lower scores on selective neuropsychological scales might be helpful to detect a transition period from PD-MCI to PD-D.

Neural correlates of working memory and compensation at different stages of cognitive impairment in Parkinson’s disease

Working memory (WM) impairment is one of the most frequent cognitive deficits in Parkinson's disease (PD). However, it is not known how neural activity is altered and compensatory responses eventually fail during progression. We aimed to elucidate neural correlates of WM and compensatory mechanisms in PD. Eighteen cognitively normal PD patients (PD-CogNL), 16 with PD with mild cognitive impairment (PD-MCI), 11 with PD with dementia (PDD), and 17 healthy controls (HCs) were evaluated. Subjects performed an n-back task. Functional MRI data were analyzed by event-related analysis for correct responses. Brain activations were evaluated by comparing them to fixation cross or 0-back task, and correlated with n-back task performance. When compared to fixation cross, PD-CogNL patients had more activation in WM areas than HCs for both the 2- and 3-back tasks. PD-MCI and PDD patients had more activation in WM areas than HCs for the 0- and 1-back task. 2-back task performance was correlated with brain activations (vs. 0-back task) in the bilateral dorsolateral prefrontal cortex and frontal eye field (FEF) and left rostral prefrontal cortex, caudate nucleus, inferior/superior parietal lobule (IPL/SPL), and anterior insular cortex as well as anterior cingulate cortex. 3-back task performance was correlated with brain activations (vs. 0-back task) in the left FEF, right caudate nucleus, and bilateral IPL/SPL. Additional activations on top of the 0-back task, rather than fixation cross, are the neural correlates of WM. Our results suggest PD patients have two types of compensatory mechanisms: (1) Hyperactivation for different WM load tasks depending on their cognitive status. PD-CogNL have hyperactivation for moderate and heavy working memory load tasks while maintaining normal working memory performance. In contrast, PD-MCI and PDD have hyperactivation for control task and light working memory load task, leaving less neural resources to further activate for more demanding tasks and resulting in impaired working memory performance. (2) Bilateral recruitment of WM-related areas, in particular the DLPFC, FEF, IPL/SPL and caudate nucleus, to improve WM performance.

Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease

BackgroundCognitive decline is commonly observed in Parkinson's disease (PD). Identifying PD with mild cognitive impairment (PD-MCI) is crucial for early initiation of therapeutic interventions and preventing cognitive decline. ObjectiveWe aimed to develop a machine learning model trained with magnetic susceptibility values based on the multi-atlas label-fusion method to classify PD without dementia into PD-MCI and normal cognition (PD-CN). MethodsThis multicenter observational cohort study retrospectively reviewed 61 PD-MCI and 59 PD-CN cases for the internal validation cohort and 22 PD-MCI and 21 PD-CN cases for the external validation cohort. The multi-atlas method parcellated the quantitative susceptibility mapping (QSM) images into 20 regions of interest and extracted QSM-based magnetic susceptibility values. Random forest, extreme gradient boosting, and light gradient boosting were selected as machine learning algorithms. ResultsAll classifiers demonstrated substantial performances in the classification task, particularly the random forest model. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for this model were 79.1%, 77.3%, 81.0%, and 0.78, respectively. The QSM values in the caudate nucleus, which were important features, were inversely correlated with the Montreal Cognitive Assessment scores (right caudate nucleus: r = −0.573, 95% CI: −0.801 to −0.298, p = 0.003; left caudate nucleus: r = −0.659, 95% CI: −0.894 to −0.392, p < 0.001). ConclusionsMachine learning models trained with QSM values successfully classified PD without dementia into PD-MCI and PD-CN groups, suggesting the potential of QSM values as an auxiliary biomarker for early evaluation of cognitive decline in patients with PD.

Impairment of EEG functional connectivity in dementia during recognition of emotional facial expression

AbstractBackgroundThe performance of facial expression recognition is affected both in neurocognitive disorders and in healthy elderly. Emotional content processing is expected to be adversely affected in dementia and as well in Parkinson's disease patients. The present study aims to compare the impaired facial expression recognition between different types of dementia patients, functional connectivity changes in patients with mild cognitive impairment, and patients with dementia were investigated by event‐related EEG‐coherence.Method25 Healthy elderly controls (HC), 25 Mild Cognitive Impairment patients (MCI), 25 patients with Alzheimer's disease (AD), 15 patients with Parkinson's disease with Mild Cognitive Impairment (PDMCI), and 16 patients with Parkinson's disease Dementia (PDD) were included in the study. EEG was recorded while angry, happy, and neutral facial expressions were presented. The artifacts were rejected by using Independent‐Component‐Analysis and Current‐Source‐Density. Magnitude‐squared coherence values of theta (4‐7Hz, 50‐400ms) responses were calculated. Inter‐hemispheric and intra‐hemispheric (frontal and central centered) pairs of F3, F4, C3, C4, T7, T8, Tp7, Tp8, P3, P4, O1, O2 channels were used. Two separate inter and intra hemispheric repeated‐measures‐ANOVA models were used for group comparisons.ResultThere were no group differences for intra‐hemispheric comparisons, but GroupXLocation comparison (p=.040) was significant, HE and MCI groups had higher coherence values in central‐ temporal, central‐ temporoparietal, and central‐ occipital connections compared to PDMCI, PDD, and AD patient groups (Figure 1). FaceXGroup (p=.009) interaction was significant for inter‐hemispheric coherence values, HC and MCI groups had higher interhemispheric coherence values than the PDMCI, PDD, and AD, especially during recognition of "angry" facial expression.ConclusionThe connectivity between the central and posterior regions increased with emotional face content; this increase seems to be preserved in MCI. On the other hand, it wasn't maintained in patients with more severe neurocognitive impairment; PDMCI, PDD, and AD groups had reduced central‐posterior connectivity and interhemispheric connectivity during the facial expression recognition paradigm. The decrease in inter and intra‐connectivity in the posterior regions is consistent with behavioral scores and may indicate neural losses as a phenomenon.

Impairment of recognition of facial expressions in different types of dementia

AbstractBackgroundImpairment of facial expression recognition in dementia is one of the cognitive deficits that could affect the social life of dementia patients. The severity of the facial expression recognition in different types of dementia has not been fully addressed. Previous studies showed that EEG event‐related oscillation (ERO) studies could reveal the brain dynamics during successful facial expression recognition. Furthermore, the impairment of facial recognition in Alzheimer's disease patients were represented with abnormal EROs (Güntekin et al., 2019). The present study aims to compare the impaired facial expression recognition between different types of dementia by analysis of event‐related delta responses. The role of delta responses in facial expression and emotional paradigms was reported previously by several researchers (Güntekin and Başar, 2014).Method25 Healthy elderly controls (HC), 25 Mild Cognitive Impairment patients (MCI), 25 patients with Alzheimer's disease (AD), 15 patients with Parkinson's disease with Mild Cognitive Impairment (PDMCI) and 16 patients with Parkinson's disease Dementia (PDD) were included in the study. The electroencephalographic activity was recorded during the perception of the facial expression recognition task (Angry, Happy, Neutral). Delta power and phase‐locking were computed for each facial recognition stimuli and compared among the groups (ANOVA, p&lt;0.05).ResultBoth delta power and delta phase locking were lower in dementia groups than healthy controls being worst in PDD. Figure 1 shows the grand average of delta power in all groups for the right occipital location. PDD (p&lt;0.0001) and AD (p&lt;0.05) had lower delta power than the healthy controls, the reduction of delta responses in PDD and AD were especially found over occipital locations. MCI and PDMCI subjects were found less impaired facial expression recognition compared to AD and PDMCI.ConclusionThe present study showed that dementia patients had severe facial recognition deficits that increased cognitive impairment. Event‐related delta responses successfully showed the impaired recognition of facial expression in different types of dementia groups. Among all dementia subjects, PDD patients had the most reduced delta responses that could be an electrophysiological biomarker of impaired facial expression recognition. Acknowledgments: This work (grant number 218S314) was supported by TUBITAK.

Detection of visual and frontoparietal network perfusion deficits in Parkinson’s disease dementia

Mild cognitive impairment of Parkinson's disease (PD) may be an early manifestation that may progressively worsen to dementia. Cognitive decline has been associated with changes in the brain perfusion pattern. This study aimed to evaluate cerebral blood flow (CBF) deficits specific to different stages of cognitive decline.Seventeen patients with cognitively normal PD (PD-CN), 18 patients with PD with mild cognitive impairment (PD-MCI), and 16 patients with PD with dementia (PDD) were included in this study. The participants were scanned using a 3 T Philips MRI scanner. Arterial spin labelling magnetic resonance (ASL-MR) images were acquired, followed by calculation of the CBF maps, and registration onto the MNI152 brain atlas. A whole-brain voxel-based CBF comparison was performed among the patient groups using age as a covariate. The mean age of patients with PDD was significantly higher than that of patients with PD-MCI (P = 0.015) and PD-CN (P = 0.001). The CBF values of the three groups were significantly different in the left cuneus of the visual network (VN), left inferior frontal gyrus of the frontoparietal network (FPN), and left dorsomedial nucleus of the thalamus. PDD had lower perfusion values than PD-MCI group in the same regions detected in the main group analysis. Additionally, comparison of PDD with PD-CN and non-demented groups revealed that the perfusion reduction extended into the bilateral cuneus of the VN, bilateral thalami, and left inferior frontal gyrus of the FPN.PDD could be separated from PD-MCI and PD-CN stages with CBF deficits in non-dopaminergically mediated posterior and dopaminergically mediated frontal networks.

The influence of white matter hyperintensity on cognitive impairment in Parkinson's disease.

The aim of this meta‐analysis was to review systematically and to identify the relationship between the severity and location of white matter hyperintensities (WMHs) and the degree of cognitive decline in patients with Parkinson’s disease (PD). We searched the PubMed, EMBASE, Web of Science, Ovid, and Cochrane Library databases for clinical trials of the severity and location of WMHs on the degree of cognitive impairment in PD through October 2020. We conducted the survey to compare the association of WMH burden in patients with PD with mild cognitive impairment (PD‐MCI) versus those with normal cognition (PD‐NC) and in patients with PD with dementia (PDD) versus those with PD without dementia (PD‐ND). Nine studies with PD‐MCI versus PD‐NC and 10 studies with PDD versus PD‐ND comparisons were included. The WMH burden in PD‐MCI patients was significantly different compared to that in PD‐NC patients (standard mean difference, SMD = 0.39, 95% CI: 0.12 to 0.66, p = 0.005), while there was no correlation shown in the age‐matched subgroup of the comparison. In addition, PDD patients had a significantly higher burden of WMHs (SMD = 0.8, 95% CI: 0.44 to 1.71, p < 0.0001), especially deep white matter hyperintensities (SMD = 0.54, 95% CI: 0.36 to 0.73, p < 0.00001) and periventricular hyperintensities (SMD = 0.70, 95% CI: 0.36 to 1.04, p < 0.0001), than PD‐NC patients, regardless of the adjustment of age. WMHs might be imaging markers for cognitive impairment in PDD but not in PD‐MCI, regardless of age, vascular risk factors, or race. Further prospective studies are needed to validate the conclusions.