• Concurrent Validity
• Confirmatory Factor Analysis
• Internalizing Symptoms
• Latent Class Analysis
• Mixture Modeling

• Humans
• Male
• Female
• Adult
• Latent Class Analysis
• Young Adult
• Factor Analysis, Statistical
• Middle Aged
• Depression/diagnosis
• Models, Psychological
• Anxiety/diagnosis
• Adolescent
• Anxiety Disorders/diagnosis
• Anxiety Disorders/psychology
• Psychiatric Status Rating Scales
• Personality

• Parkinson's disease
• cohort study
• depression
• depressive symptom

• Humans
• Aged
• Cohort Studies
• Parkinson Disease/epidemiology
• Depression/epidemiology
• Risk Factors
• Surveys and Questionnaires

• Anhedonia
• Depression
• Forced swim test
• Parkinson’s disease
• Peripheral non-invasive focused ultrasound
• Sucrose preference test

• Humans
• Rats
• Animals
• Anhedonia/physiology
• Rats, Wistar
• Tyrosine 3-Monooxygenase
• Quality of Life
• Parkinson Disease/pathology
• Oxidopamine
• Antidepressive Agents
• Sucrose
• Disease Models, Animal

• Depression
• HPA-axis
• Hippocampal neurogenesis
• Neuroinflammation
• Neurotransmitters
• Parkinson’s disease
• Trophic and genetic factors

This study aimed to investigate the association of altered cortical thickness and functional connectivity (FC) with depression in Parkinson’s disease (PD).

A total of 26 non-depressed PD patients (PD-ND), 30 PD patients with minor depression (PD-MnD), 32 PD patients with major depression (PD-MDD), and 30 healthy controls (HC) were enrolled. Differences in cortical thickness among the four groups were assessed, and the results were used to analyze FC differences in regions of cortical atrophy. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were also performed to identify clinical features and neuroimaging biomarkers that might help in the prediction of PD-MDD.

Patients with PD-MDD showed decreased cortical thickness compared to patients with PD-ND in the left superior temporal and right rostral middle frontal gyri (RMFG), as well as weak FC between the left superior temporal gyrus and right cerebellum posterior lobe and between right RMFG and right inferior frontal gyrus and insula. The combination of cortical thickness, FC, and basic clinical features showed strong potential for predicting PD-MDD based on the area under the ROC curve (0.927, 95% CI 0.854–0.999, p < 0.001).

Patients with PD-MDD show extensive cortical atrophy and FC alterations, suggesting that cortical thickness and FC may be neuroimaging-based diagnostic biomarkers for PD-MDD.

• National Natural Science Foundation of China
• Applied Basic Research Foundation of Yunnan Province

To investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI–based machine learning model in identifying depressed PD (dPD).

The DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD.

As compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71).

Depression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.

• National Natural Science Foundation of China

• Incident diabetes
• Latent class analysis
• Subpopulation

• Adult
• China/epidemiology
• Diabetes Mellitus/epidemiology
• Female
• Humans
• Latent Class Analysis
• Male
• Middle Aged
• Retrospective Studies
• Risk Assessment
• Risk Factors

• Adult spinal deformity
• Health-related quality of life
• Minimum clinically important difference
• Outcome measures

• Adult
• Age Factors
• Aged
• Analgesics/therapeutic use
• Female
• Humans
• Male
• Middle Aged
• Minimal Clinically Important Difference
• Quality of Life
• Sex Factors
• Spinal Curvatures/physiopathology
• Spinal Curvatures/therapy
• Spinal Fusion/methods
• Watchful Waiting/methods

Background: To explore the changes of neuroinflammatory factors in cerebrospinal fluid (CSF) and their correlation with monoamine neurotransmitters in Parkinson’s disease (PD) with depression (PD-D) patients.

Methods: Neuroinflammatory factors and neurotransmitters in CSF were measured and compared between PD with no depression (PD-ND) and PD-D groups. The relationship between PD-D and neuroinflammatory factors was studied by binary logistic regression equation, and the related factors of PD-D were adjusted. The correlations of the levels of neuroinflammatory factors and neurotransmitters in PD-D group were analyzed.

Results: The levels of tumor necrosis factor (TNF)-α in CSF from PD-D group were significantly higher and there were no significant differences in the levels of interleukin-1β, prostaglandin (PG) E₂, hydrogen peroxide (H₂O₂), and nitric oxide (NO). The 24-item Hamilton Depression Scale (HAMD-24) score was positively correlated with the level of TNF-α in CSF. Binary logistic regression showed that the OR of CSF TNF-α level was 1.035 (95% CI 1.002–1.069). The level of dopamine (DA) in CSF of PD-D group was significantly lower than that in PD-ND group. TNF-α level was negatively correlated with DA level in CSF from PD patients (r = −0.320, P = 0.003).

Conclusions: Neuroinflammatory factors, especially TNF-α, may play an important role in PD-D. It may cause damage to DA neurons and lead to the depletion of DA, which is related to the occurrence and development of PD-D.

• Parkinson’s disease
• cerebrospinal fluid
• depression
• neuroinflammatory factors
• neurotransmitters

The aim of this research was to investigate the alterations in functional brain networks and to assess the relationship between depressive impairment and topological network changes in Parkinson's disease (PD) patients with depression (DPD).

Twenty‐two DPD patients, 23 PD patients without depression (NDPD), and 25 matched healthy controls (HCs) were enrolled. All participants were examined by resting‐state functional magnetic resonance imaging scans. Graph theoretical analysis and network‐based statistic methods were used to analyze brain network topological properties and abnormal subnetworks, respectively.

The DPD group showed significantly decreased local efficiency compared with the HC group (P = .008, FDR corrected). In nodal metrics analyses, the degree of the right inferior occipital gyrus (P = .0001, FDR corrected) was positively correlated with the Hamilton Depression Rating Scale scores in the DPD group. Meanwhile, the temporal visual cortex, including the bilateral middle temporal gyri and right inferior temporal gyrus in the HC and NDPD groups and the left posterior cingulate gyrus in the NDPD group, was defined as hub region, but not in the DPD group. Compared with the HC group, the DPD group had extensive weakening of connections between the temporal‐occipital visual cortex and the prefrontal‐limbic network.

These results suggest that PD depression is associated with disruptions in the topological organization of functional brain networks, mainly involved the temporal‐occipital visual cortex and the posterior cingulate gyrus and may advance our current understanding of the pathophysiological mechanisms underlying DPD.

• Parkinson’s disease
• depression
• functional magnetic resonance imaging
• graph theory
• network-based statistical analysis

• Korean version
• Parkinson disease
• Parkinson's Disease Sleep Scale-2
• latent class analysis
• sleep
• validity

•

Patient-specific 42-ICNs template was created from 156 PD patients’ rs-fMRI data.

Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients.

This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not.

We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features.

42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%.

Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones.

• Depression
• Functional connectivity
• Intrinsic connectivity network
• Parkinson's disease
• Resting-state fMRI

• Adult
• Aged
• Brain/diagnostic imaging
• Brain/physiopathology
• Depression/diagnostic imaging
• Depression/etiology
• Depression/physiopathology
• Female
• Humans
• Image Interpretation, Computer-Assisted/methods
• Magnetic Resonance Imaging/methods
• Male
• Middle Aged
• Nerve Net/diagnostic imaging
• Nerve Net/physiopathology
• Neural Pathways/diagnostic imaging
• Neural Pathways/physiopathology
• Neuroimaging/methods
• Parkinson Disease/complications
• Parkinson Disease/diagnostic imaging
• Parkinson Disease/physiopathology

• Parkinson's disease
• movement disorders
• neuropsychiatry
• psychiatry

• Humans
• Parkinson Disease/psychology
• Suicide/statistics & numerical data
• Suicide Prevention

• T32 GM007309 NIGMS NIH HHS

Background and Purpose: Non-motor symptoms of Parkinson disease (PD) have a strong negative impact on the health-related quality of life (QoL) of patients with PD. Sleep disturbance is an important non-motor symptom because of its high prevalence. However, previous studies investigating the determinants of sleep quality in patients with PD have revealed inconsistent results. Our study evaluated the correlations between sleep quality in patients with PD and disease-related variables, medications used depression, anxiety, and QoL and identified the determinants of sleep disturbance in people with PD in Taiwan.

Methods: A total of 134 patients with PD were recruited from the outpatient clinic. We examined the correlations between the Parkinson disease sleep scale-2 (PDSS-2) scores and different variables, namely the Unified Parkinson Disease Rating Scale, Parkinson disease questionnaire, Beck Depression Inventory, and Beck Anxiety Inventory (BAI). Logistic regression analysis was used to assess the potential predictive variables for sleep quality in patients with PD.

Results: Among our participants, 47.8% were classified as poor sleepers (PDSS-2 = 15–60). Correlation analysis demonstrated that poor sleepers exhibited longer disease durations, higher levodopa equivalent daily doses (LEDDs), higher PD severity, more depression and anxiety symptoms, poorer QoL, more frequent unemployed status, higher hypnotics use, higher dependency for activities of daily living, more motor impairments, and more therapy-related complications. Logistic regression revealed that the LEDD was a significant predictive factor of sleep quality.

Conclusion: Poor sleepers constituted approximately half of our patients with PD. The participants experienced more favorable sleep if they were currently working. Increased PD duration, severity, depression or anxiety symptoms, and doses of dopaminergic therapy were significantly associated with poor sleep quality. Continued working, attempts to treat comorbid anxiety or depression, and avoidance of overdosage of dopaminergic treatments may improve sleep quality in patients with PD.

• Parkinson disease
• anxiety
• levodopa equivalent dose
• sleep quality
• working

• Alternative protein sources
• Consumer behaviour
• Credence attributes
• Food choice
• Meat consumption
• Meat substitutes

• Parkinson's disease
• depression
• dopamine
• risk factor
• serotonin

• Parkinson’s disease
• mental health
• occupational therapy
• quality of life
• scoping reviews

• Anxiety/etiology
• Anxiety Disorders/etiology
• Cognition Disorders/etiology
• Depression/etiology
• Disease Progression
• Dyskinesias/etiology
• Fatigue/etiology
• Humans
• Parkinson Disease/complications
• Parkinson Disease/psychology
• Quality of Life

• Parkinson disease
• depression
• geriatric psychiatry
• mental health

• Cancer
• Epidemiology
• Frailty
• Geriatric assessment
• Outcomes

• Parkinson's disease
• depression
• functional connectivity

• NEUROPSYCHIATRY
• NEUROPSYCHOLOGY
• PARKINSON'S DISEASE
• QUALITY OF LIFE
• STEREOTAXIC SURGERY

• anhedonia
• anxiety
• apathy
• depression
• dysphoria

• Aged
• Analysis of Variance
• Anhedonia
• Anxiety/diagnosis
• Apathy
• Comorbidity
• Educational Status
• Female
• Humans
• Male
• Middle Aged
• Models, Psychological
• Mood Disorders/diagnosis
• Motor Activity
• Parkinson Disease/psychology
• Psychiatric Status Rating Scales
• Regression Analysis
• Risk Factors

• P30 AG028740 NIA NIH HHS
• R21 NS079767 NINDS NIH HHS
• T32 AG020499 NIA NIH HHS
• T32-AG020499 NIA NIH HHS

• Parkinson’s disease
• depression
• neuropsychiatry
• nonmotor symptoms

Heart failure patients with reduced ejection fraction (HFREF) are heterogenous, and our ability to identify patients likely to respond to therapy is limited. We present a method of identifying disease subtypes using high-dimensional clinical phenotyping and latent class analysis that may be useful in personalizing prognosis and treatment in HFREF.

A total of 1121 patients with nonischemic HFREF from the β-blocker Evaluation of Survival Trial were categorized according to 27 clinical features. Latent class analysis was used to generate two latent class models, LCM A and B, to identify HFREF subtypes. LCM A consisted of features associated with HF pathogenesis, whereas LCM B consisted of markers of HF progression and severity. The Seattle Heart Failure Model (SHFM) Score was also calculated for all patients. Mortality, improvement in left ventricular ejection fraction (LVEF) defined as an increase in LVEF ≥5% and a final LVEF of 35% after 12 months, and effect of bucindolol on both outcomes were compared across HFREF subtypes. Performance of models that included a combination of LCM subtypes and SHFM scores towards predicting mortality and LVEF response was estimated and subsequently validated using leave-one-out cross-validation and data from the Multicenter Oral Carvedilol Heart Failure Assessment Trial.

A total of 6 subtypes were identified using LCM A and 5 subtypes using LCM B. Several subtypes resembled familiar clinical phenotypes. Prognosis, improvement in LVEF, and the effect of bucindolol treatment differed significantly between subtypes. Prediction improved with addition of both latent class models to SHFM for both 1-year mortality and LVEF response outcomes.

The combination of high-dimensional phenotyping and latent class analysis identifies subtypes of HFREF with implications for prognosis and response to specific therapies that may provide insight into mechanisms of disease. These subtypes may facilitate development of personalized treatment plans.

• Adrenergic beta-Antagonists/therapeutic use
• Adult
• Area Under Curve
• Bayes Theorem
• Double-Blind Method
• Female
• Heart Failure/drug therapy
• Heart Failure/mortality
• Heart Failure/physiopathology
• Humans
• Kaplan-Meier Estimate
• Logistic Models
• Male
• Middle Aged
• Models, Biological
• Multivariate Analysis
• Propanolamines/therapeutic use
• Randomized Controlled Trials as Topic
• Retrospective Studies
• Stroke Volume
• Treatment Outcome
• Ventricular Function, Left/drug effects

• P20 HL101438 NHLBI NIH HHS
• L30 HL110124 NHLBI NIH HHS
• T32 HL007822 NHLBI NIH HHS
• 2T32 NHL007822-12 PHS HHS
• 5 P20 HL101438-01 NHLBI NIH HHS

• apathy
• anhedonia
• negative affect
• dysphoria
• anxiety
• confirmatory factor analysis