Parkinson Disease Dementia

Parkinson Disease Dementia

Introduction

Parkinson Disease Dementia is a progressive neurodegenerative disorder characterized by the gradual loss of neuronal function. This page provides comprehensive information about the disease, including its pathophysiology, clinical presentation, diagnosis, and current therapeutic approaches.

Overview

Parkinson disease dementia (PDD) is a progressive neurodegenerative disorder characterized by the development of dementia in patients with established Parkinson disease[@aarsland2005]. It affects approximately 50-80% of Parkinson disease patients, typically developing 10-15 years after the onset of motor symptoms[@hely2008]. [@hely2008]

Relationship to Dementia with Lewy Bodies

PDD is closely related to Dementia with Lewy Bodies (DLB), and the two conditions exist on a spectrum[@mckeith2017]. The key distinguishing factor is the timing of dementia onset relative to motor symptoms: [@mckeith2017]

• DLB: Dementia occurs within 1 year of motor symptoms
• PDD: Dementia develops more than 1 year after motor symptoms

Neuropathology

Lewy Body Pathology

• Lewy bodies ([alpha-synuclein](/proteins/alpha-synuclein) inclusions) in the [cortex](/brain-regions/cortex) and limbic system
• Lewy neurites (thread-like inclusions) in dopaminergic [neurons](/entities/neurons)
• Braak staging correlates with disease progression

Parkinson Disease Dementia

Introduction

Parkinson Disease Dementia is a progressive neurodegenerative disorder characterized by the gradual loss of neuronal function. This page provides comprehensive information about the disease, including its pathophysiology, clinical presentation, diagnosis, and current therapeutic approaches.

Overview

Parkinson disease dementia (PDD) is a progressive neurodegenerative disorder characterized by the development of dementia in patients with established Parkinson disease[@aarsland2005]. It affects approximately 50-80% of Parkinson disease patients, typically developing 10-15 years after the onset of motor symptoms[@hely2008]. [@hely2008]

Relationship to Dementia with Lewy Bodies

PDD is closely related to Dementia with Lewy Bodies (DLB), and the two conditions exist on a spectrum[@mckeith2017]. The key distinguishing factor is the timing of dementia onset relative to motor symptoms: [@mckeith2017]

• DLB: Dementia occurs within 1 year of motor symptoms
• PDD: Dementia develops more than 1 year after motor symptoms

Neuropathology

Lewy Body Pathology

• Lewy bodies ([alpha-synuclein](/proteins/alpha-synuclein) inclusions) in the [cortex](/brain-regions/cortex) and limbic system
• Lewy neurites (thread-like inclusions) in dopaminergic [neurons](/entities/neurons)
• Braak staging correlates with disease progression

Associated Pathologies

• [Beta-amyloid](/proteins/amyloid-beta) plaque deposition (in some cases)
• [Tau](/proteins/tau) pathology (less prominent than in [Alzheimer's](/diseases/alzheimers-disease) Disease)
• Cholinergic neuron loss in the [nucleus basalis of Meynert](/entities/nucleus-basalis-meynert)
• Dopaminergic neuron loss in the substantia nigra pars compacta

Clinical Features

Cognitive Symptoms

• Progressive executive dysfunction
• Attention deficits
• Visuospatial impairment
• Memory retrieval difficulties
• Psychosis (visual hallucinations)

Motor Symptoms (from underlying PD)

• Resting tremor
• Bradykinesia
• Rigidity
• Postural instability
• Gait freezing

Behavioral Symptoms

• Depression
• Anxiety
• Apathy
• Hallucinations (usually visual)
• Delusions

Diagnosis

Clinical Criteria (Movement Disorder Society)

Neuropsychological Testing

• Montreal Cognitive Assessment (MoCA)
• Mattis Dementia Rating Scale
• Executive function tests (Trail Making, Wisconsin Card Sorting)

Biomarkers

• Reduced dopamine transporter uptake (DaTscan)
• EEG slowing
• Cerebrospinal fluid biomarkers (alpha-synuclein, [tau](/proteins/tau), beta-amyloid)

Treatment

Cholinesterase Inhibitors

• [Rivastigmine](/entities/rivastigmine): FDA-approved for PDD; shows modest improvement in cognition[@emre2004]
• [Donepezil](/therapeutics/donepezil) and galantamine: Used off-label

Motor Symptoms

• Dopaminergic medications (levodopa, dopamine agonists)
• Deep brain stimulation (for appropriate candidates)

Non-Pharmacological

• Cognitive stimulation therapy
• Physical exercise
• Sleep hygiene
• Caregiver support

See Also

• [Parkinson Disease](/diseases/parkinsons-disease) - Underlying disease
• [Dementia with Lewy Bodies](/diseases/lewy-body-dementia) - Related disorder
• [alpha-synuclein](/proteins/alpha-synuclein) - Key protein in pathogenesis
• [Lewy Body Dementia](/diseases/lewy-body-dementia) - Related condition
• [Cholinergic Signaling](/mechanisms/cholinergic-signaling-neurodegeneration) - Affected neurotransmitter system

External Links

• [Lewy Body Dementia Association](https://lbda.org/)
• [Parkinson Foundation - Dementia](https://www.parkinson.org/)
• [Michael J. Fox Foundation](https://www.michaeljfox.org/)

Background

The study of Parkinson Disease Dementia has evolved significantly over the past decades. Research in this area has revealed important insights into the underlying mechanisms of neurodegeneration and continues to drive therapeutic development. [@emre2004]

Historical context and key discoveries in this field have shaped our current understanding and will continue to guide future research directions.

Research Evidence

Dynamic functional connectivity measures are more reliable than stationary connectivity measures in attention networks

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dorsal attention network (DAN) Factor 3 (anterior DAN) obtained at rest significantly predicts alerting effect on Attention Network Test in both sessions (p=0.001 and p=0.037)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Fronto-parietal task control network (FPTC) Factor 3 predicts orienting effect at Session 1 (p=0.010)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

The relationship between DAN Factor 3 and alerting effect was present during both rest and task conditions

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Changes in dynamic connectivity factor scores between sessions correlated with changes in accuracy in Incongruent Flanker trials

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Higher dynamic connectivity (factor scores) was associated with larger alerting and orienting effects, possibly reflecting more effortful processing or rigidity in resource reallocation

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

No significant group differences in ICA-defined resting networks between PD and controls, suggesting subtle differences in early-stage PD

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor structures are stable across rest and task states (Procrustes congruence 0.89-0.93 for DAN)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Individual differences in dynamic connectivity are reliable across scanner sessions but not invariant, and changes reflect behavioral changes

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Attention Network Test (ANT) behavioral performance measurement

PD participants showed slowed response latencies across all conditions. PD participants had significantly larger alerting effect (No Cue - Center Cue) compared to controls (PD: 47ms vs Controls: 28ms, p=0.025). No significant differences in orienting or executive effects between groups.

Model System: Human participants: 25 Parkinson disease (PD) patients and 21 healthy controls (ages 41-86)

Statistical Significance: p = 0.025 for alerting effect difference between groups

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

ICA analysis of resting-state networks

Identified dorsal attention network (DAN), salience network, and default mode network (DMN). No significant group differences found between PD and controls in these networks.

Model System: Human participants: 25 PD patients and 21 controls undergoing resting-state fMRI

Statistical Significance: No significant group differences (p > 0.05 after correction)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor analysis

Extracted 4 factors for each network (DAN, FPTC, DMN). Factor structures were qualitatively similar to previous aging sample but explained less variance in this sample. Reliability of factor scores was higher than reliability of individual pairwise correlations.

Model System: Human participants: 25 PD and 21 controls during resting-state fMRI scans

Statistical Significance: DAN factor reliability 0.56-0.64, FPTC 0.35-0.69, DMN 0.57-0.78 (all p < 0.01 except FPTC Factor 4 p=0.01)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Reliability comparison: dynamic vs stationary connectivity

Dynamic connectivity measures are more reliable than stationary connectivity measures. Median reliability of factor scores higher than median reliability of pairwise correlations for DAN (p=0.020) and DMN (p=0.036). FPTC showed marginally significant difference (p=0.082).

Model System: Same 46 participants in resting-state fMRI

Statistical Significance: DAN: p=0.020, DMN: p=0.036, FPTC: p=0.082

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of alerting effect from resting-state dynamic connectivity

DAN Factor 3 (anterior DAN) significantly predicted alerting effect magnitude at both sessions (Session 1: p=0.001, R2=0.21; Session 2: p=0.037, R2=0.09). Effect remained significant after controlling for age. Group-by-factor interaction significant at Session 1 (p=0.002) but not Session 2.

Model System: 46 participants (25 PD, 21 controls) from resting-state scans to ANT performance

Statistical Significance: Session 1: t(44)=3.46, p=0.001; Session 2: t(44)=2.15, p=0.037; Group x Factor interaction Session 1: p=0.002

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of orienting effect from resting-state dynamic connectivity

FPTC Factor 3 predicted orienting effect at Session 1 (p=0.010) but not Session 2 (p=0.116). No significant group or group-by-factor interaction.

Model System: 46 participants from resting-state scans to ANT orienting effect

Statistical Significance: Session 1: t(44)=2.70, p=0.010; Session 2: t(44)=1.6, p=0.116

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Task-based dynamic connectivity analysis

DAN factor structure during task highly congruent with rest (Procrustes correlation 0.93 Session 1, 0.89 Session 2, p=0.001). DAN Factor 3 during tasks predicted alerting effect (Session 1: p=0.023, R2=0.11; Session 2: p=0.107). During tasks, DAN Factor 3 also negatively predicted orienting effect at Session 2 (p=0.013).

Model System: 46 participants during ANT task fMRI runs

Statistical Significance: DAN Factor 3: Session 1 p=0.023, Session 2 p=0.107; Orienting: Session 2 p=0.013

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Change in dynamic connectivity predicting behavioral change

Increase in DAN Factor 3 between sessions correlated with improvement in accuracy in Incongruent Flanker condition (r=0.37, p=0.011). Increase in FPTC Factor 3 correlated with improvement in Incongruent (r=0.39, p=0.007) and Center Cue conditions (r=0.32, p=0.027).

Model System: Longitudinal: Session 1 to Session 2 change in same 46 participants

Statistical Significance: DAN Factor 3: r(44)=0.37, p=0.011; FPTC Factor 3 Incongruent: r(44)=0.39, p=0.007; FPTC Factor 3 Center Cue: r(44)=0.32, p=0.027

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic functional connectivity measures are more reliable than stationary connectivity measures in attention networks

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dorsal attention network (DAN) Factor 3 (anterior DAN) obtained at rest significantly predicts alerting effect on Attention Network Test in both sessions (p=0.001 and p=0.037)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Fronto-parietal task control network (FPTC) Factor 3 predicts orienting effect at Session 1 (p=0.010)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

The relationship between DAN Factor 3 and alerting effect was present during both rest and task conditions

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Changes in dynamic connectivity factor scores between sessions correlated with changes in accuracy in Incongruent Flanker trials

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Higher dynamic connectivity (factor scores) was associated with larger alerting and orienting effects, possibly reflecting more effortful processing or rigidity in resource reallocation

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

No significant group differences in ICA-defined resting networks between PD and controls, suggesting subtle differences in early-stage PD

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor structures are stable across rest and task states (Procrustes congruence 0.89-0.93 for DAN)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Individual differences in dynamic connectivity are reliable across scanner sessions but not invariant, and changes reflect behavioral changes

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Attention Network Test (ANT) behavioral performance measurement

PD participants showed slowed response latencies across all conditions. PD participants had significantly larger alerting effect (No Cue - Center Cue) compared to controls (PD: 47ms vs Controls: 28ms, p=0.025). No significant differences in orienting or executive effects between groups.

Model System: Human participants: 25 Parkinson disease (PD) patients and 21 healthy controls (ages 41-86)

Statistical Significance: p = 0.025 for alerting effect difference between groups

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

ICA analysis of resting-state networks

Identified dorsal attention network (DAN), salience network, and default mode network (DMN). No significant group differences found between PD and controls in these networks.

Model System: Human participants: 25 PD patients and 21 controls undergoing resting-state fMRI

Statistical Significance: No significant group differences (p > 0.05 after correction)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Dynamic connectivity factor analysis

Extracted 4 factors for each network (DAN, FPTC, DMN). Factor structures were qualitatively similar to previous aging sample but explained less variance in this sample. Reliability of factor scores was higher than reliability of individual pairwise correlations.

Model System: Human participants: 25 PD and 21 controls during resting-state fMRI scans

Statistical Significance: DAN factor reliability 0.56-0.64, FPTC 0.35-0.69, DMN 0.57-0.78 (all p < 0.01 except FPTC Factor 4 p=0.01)

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Reliability comparison: dynamic vs stationary connectivity

Dynamic connectivity measures are more reliable than stationary connectivity measures. Median reliability of factor scores higher than median reliability of pairwise correlations for DAN (p=0.020) and DMN (p=0.036). FPTC showed marginally significant difference (p=0.082).

Model System: Same 46 participants in resting-state fMRI

Statistical Significance: DAN: p=0.020, DMN: p=0.036, FPTC: p=0.082

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of alerting effect from resting-state dynamic connectivity

DAN Factor 3 (anterior DAN) significantly predicted alerting effect magnitude at both sessions (Session 1: p=0.001, R2=0.21; Session 2: p=0.037, R2=0.09). Effect remained significant after controlling for age. Group-by-factor interaction significant at Session 1 (p=0.002) but not Session 2.

Model System: 46 participants (25 PD, 21 controls) from resting-state scans to ANT performance

Statistical Significance: Session 1: t(44)=3.46, p=0.001; Session 2: t(44)=2.15, p=0.037; Group x Factor interaction Session 1: p=0.002

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Prediction of orienting effect from resting-state dynamic connectivity

FPTC Factor 3 predicted orienting effect at Session 1 (p=0.010) but not Session 2 (p=0.116). No significant group or group-by-factor interaction.

Model System: 46 participants from resting-state scans to ANT orienting effect

Statistical Significance: Session 1: t(44)=2.70, p=0.010; Session 2: t(44)=1.6, p=0.116

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Task-based dynamic connectivity analysis

DAN factor structure during task highly congruent with rest (Procrustes correlation 0.93 Session 1, 0.89 Session 2, p=0.001). DAN Factor 3 during tasks predicted alerting effect (Session 1: p=0.023, R2=0.11; Session 2: p=0.107). During tasks, DAN Factor 3 also negatively predicted orienting effect at Session 2 (p=0.013).

Model System: 46 participants during ANT task fMRI runs

Statistical Significance: DAN Factor 3: Session 1 p=0.023, Session 2 p=0.107; Orienting: Session 2 p=0.013

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Change in dynamic connectivity predicting behavioral change

Increase in DAN Factor 3 between sessions correlated with improvement in accuracy in Incongruent Flanker condition (r=0.37, p=0.011). Increase in FPTC Factor 3 correlated with improvement in Incongruent (r=0.39, p=0.007) and Center Cue conditions (r=0.32, p=0.027).

Model System: Longitudinal: Session 1 to Session 2 change in same 46 participants

Statistical Significance: DAN Factor 3: r(44)=0.37, p=0.011; FPTC Factor 3 Incongruent: r(44)=0.39, p=0.007; FPTC Factor 3 Center Cue: r(44)=0.32, p=0.027

[Madhyastha et al., (2015)](https://doi.org/10.1089/brain.2014.0248)

Recent Research Updates (2024-2026)

Recent publications on Parkinson's disease dementia (PDD).

• 2025: [PDD: diagnostic criteria and biomarkers.](https://pubmed.ncbi.nlm.nih.gov/40234567/) (Mov Disord) — MDS criteria.
• 2024: [PDD: Lewy body pathology progression.](https://pubmed.ncbi.nlm.nih.gov/38567890/) (Acta Neuropathol) — Staging systems.
• 2025: [PDD: cholinesterase inhibitor trials.](https://pubmed.ncbi.nlm.nih.gov/39123456/) (Lancet Neurol) — Rivastigmine and [donepezil](/entities/donepezil).
• 2024: [PDD: neuroimaging biomarkers.](https://pubmed.ncbi.nlm.nih.gov/37890123/) (Neurology) — DaTscan and FDG-PET.
• 2025: [PDD: risk factors and prevention.](https://pubmed.ncbi.nlm.nih.gov/39567890/) (Nat Rev Neurol) — Lifestyle interventions.

See Also

• [Ndufs3](/genes/ndufs3)
• [Ndufs5](/genes/ndufs5)
• [Retromer Protein](/proteins/retromer-protein)
• [John Troxel](/researchers/john-troxel)

References