We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.

Dementia with Lewy bodies (DLB) is characterized by alpha-synuclein protein deposition with variable degree of concurrent Alzheimer's pathology. Neuroinflammation is also increasingly recognized as a significant contributor to degeneration. We aimed to examine the relationship between microglial activation as measured with [11C]-PK11195 brain PET, MR diffusion tensor imaging (DTI) and grey matter atrophy in DLB. Nineteen clinically probable DLB and 20 similarly aged controls underwent 3T structural MRI (T1-weighted) and diffusion-weighted imaging. Eighteen DLB subjects also underwent [11C]-PK11195 PET imaging and 15 had [11C]-Pittsburgh compound B amyloid PET, resulting in 9/15 being amyloid-positive. We used Computational Anatomy Toolbox (CAT12) for volume-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) for DTI to assess group comparisons between DLB and controls and to identify associations of [11C]-PK11195 binding with grey/white matter changes and cognitive score in DLB patients. VBM analyses showed that DLB had extensive reduction of grey matter volume in superior frontal, temporal, parietal and occipital cortices (family-wise error (FWE)-corrected p < 0.05). TBSS showed widespread changes in DLB for all DTI parameters (reduced fractional anisotropy, increased diffusivity), involving the corpus callosum, corona radiata and superior longitudinal fasciculus (FWE-corrected p < 0.05). Higher [11C]-PK11195 binding in parietal cortices correlated with widespread lower mean and radial diffusivity in DLB patients (FWE-corrected p < 0.05). Furthermore, preserved cognition in DLB (higher Addenbrookes Cognitive Evaluation revised score) also correlated with higher [11C]-PK11195 binding in frontal, temporal, and occipital lobes. However, microglial activation was not significantly associated with grey matter changes. Our study suggests that increased microglial activation is associated with a relative preservation of white matter and cognition in DLB, positioning neuroinflammation as a potential early marker of DLB etio-pathogenesis.

presence hallucinations (PH) are frequent in dementia with Lewy bodies (DLB), but their cortico-subcortical origin is unknown. Recent studies have defined key frontal and temporal areas contributing to the occurrence of PH (PH-network) and tested their relevance in subjects with Parkinson's disease (PD). With the present study, we aimed at disentangling the metabolic and dopaminergic correlates of pH as well as their relation to a recently defined PH brain network in DLB.

To investigate in vivo degeneration of the cholinergic system in mild cognitive impairment with Lewy bodies (MCI-LB), we studied nucleus basalis of Meynert (NBM) volumes from structural MR images and its relation to EEG slowing and cognitive impairment.

Cognitive fluctuations are a core symptom in dementia with Lewy bodies (DLB) and may relate to pathological alterations in distributed brain networks. To test this we analysed resting state fMRI changes in a cohort of fluctuating DLB patients (n = 16) compared with age matched controls (n = 17) with the aim of finding functional connectivity (FC) differences between these two groups and whether these associate with cognitive fluctuations in DLB. Resting state networks (RSNs) were estimated using independent component analysis and FC between the RSN maps and the entirety of the brain was assessed using dual regression. The default mode network (DMN) appeared unaffected in DLB compared to controls but significant cluster differences between DLB and controls were found for the left fronto-parietal, temporal, and sensory-motor networks. Desynchronization of a number of cortical and subcortical areas related to the left fronto-parietal network was associated with the severity and frequency of cognitive fluctuations. Our findings provide empirical evidence for the potential role of attention-executive networks in the aetiology of this core symptom in DLB.

The purpose of this study was to reveal the patterns of reorganization of rich club organization in brain functional networks in dementia with Lewy bodies (DLB) and Alzheimer's disease (AD). The study found that the rich club node shifts from sensory/somatomotor network to fronto-parietal network in DLB. For AD, the rich club nodes switch between the temporal lobe with obvious structural atrophy and the frontal lobe, parietal lobe and cerebellum with relatively preserved structure and function. In addition, compared with healthy controls, rich club connectivity was enhanced in the DLB and AD groups. The connection strength of DLB patients was related to cognitive assessment. In conclusion, we revealed the different functional reorganization patterns of DLB and AD. The conversion and redistribution of rich club members may play a causal role in disease-specific outcomes. It may be used as a potential biomarker to provide more accurate prevention and treatment strategies.

Percent whole brain volume change (PBVC) measured from serial MRI scans is widely accepted as a sensitive marker of disease progression in Alzheimer's disease (AD). However, the utility of PBVC in the differential diagnosis of dementia remains to be established. We compared PBVC in AD and dementia with Lewy bodies (DLB), and investigated associations with clinical measures.

To study the influence of concomitant Alzheimer's disease (AD) pathology in dementia with Lewy bodies (DLB) on dopamine transporter (DAT) and serotonin transporter (SERT) availability, using 123I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl) nortropane (123I-FP-CIT) single photon emission computed tomography (SPECT).

Visually preserved metabolism in posterior cingulate cortex relative to hypometabolism in precuneus and cuneus, the cingulate island sign, is a feature of dementia with Lewy bodies (DLB) on FDG-PET. Lower cingulate island sign ratio (posterior cingulate cortex/cuneus+precuneus; FDG-CISr) values have been associated with a higher Braak neurofibrillary tangle stage in autopsied DLB. Using voxel-wise analysis, we assessed the patterns of regional cortical perfusion and metabolism, and using an atlas-based approach, we measured perfusion cingulate island sign ratio on arterial spin labeling MRI (ASL-CISr), and its associations with FDG-CISr, uptake on tau-PET and clinical severity in DLB. Our study sample (n = 114) included clinically probable DLB patients (n = 19), age-matched patients with probable Alzheimer's disease dementia (AD; n = 19) and matched controls (n = 76) who underwent MRI with 3-dimensional pseudo-continuous arterial spin labeling, 18F-FDG-PET and 18F-AV-1451 tau PET. Patterns of cortical perfusion and metabolism were derived from quantitative maps using Statistical Parametric Mapping. DLB patients showed hypoperfusion on ASL-MRI in precuneus, cuneus and posterior parieto-occipital cortices, compared to controls, and relatively spared posterior cingulate gyrus, similar to pattern of hypometabolism on FDG-PET. DLB patients had higher ASL-CISr and FDG-CISr than AD patients (p <0.001). ASL-CISr correlated with FDG-CISr in DLB patients (r = 0.67; p =0.002). Accuracy of distinguishing DLB from AD patients was 0.80 for ASL-CISr and 0.91 for FDG-CISr. Lower ASL-CISr was moderately associated with a higher composite medial temporal AV-1451 uptake (r = -0.50; p =0.03) in DLB. Lower perfusion in precuneus and cuneus was associated with worse global clinical scores. In summary, the pattern of cortical hypoperfusion on ASL-MRI is similar to hypometabolism on FDG-PET, and respective cingulate island sign ratios correlate with each other in DLB. Non-invasive and radiotracer-free ASL-MRI may be further developed as a tool for the screening and diagnostic evaluation of DLB patients in a variety of clinical settings where FDG-PET is not accessible.

Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are thought to be part of a spectrum: both have a clinical profile including symptoms associated with dopaminergic and serotonergic loss, yet few imaging studies have focused on serotonergic neurodegeneration in both disorders. We aimed to study degeneration of terminals with dopamine and serotonin transporter (DAT and SERT, respectively) in patients with early-stage PD and DLB relative to healthy controls, using 123I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane (123I-FP-CIT) single photon emission computed tomography (SPECT). We conducted region of interest (ROI) and voxel-based analyses on 123I-FP-CIT SPECT scans. Using the cerebellum as a reference region, we determined binding ratios (BRs) for bilateral ROIs in the DAT-rich striatum (head of the caudate nucleus and posterior putamen) and SERT-rich extrastriatal brain regions (thalamus, hypothalamus and hippocampus). We compared BRs in PD and DLB patients with BRs in healthy controls (all groups: n = 16). Both PD and DLB patients had lower striatal 123I-FP-CIT BRs than healthy controls for the bilateral caudate head (PD-left: F(1,29) = 28.778, P < .001, ω2 = 0.35; right: F(1,29) = 35.338, P < .001, ω2 = 0.42; DLB-left: F(1,29) = 28.241, P < .001, ω2 = 0.31; right: F(1,29) = 18.811, P < .001, ω2 = 0.26) and bilateral posterior putamen (PD-left: F(1,29) = 107.531, P < .001, ω2 = 0.77; right: F(1,29) = 87.525, P < .001, ω2 = 0.72; DLB-left: F(1,29) = 39.910, P < .001, ω2 = 0.48; right: F(1,29) = 26.882, P < .001, ω2 = 0.38). DLB patients had lower hypothalamic 123I-FP-CIT BRs than healthy controls (F(1,29) = 6.059, P = .020, ω2 = 0.12). In the voxel-based analysis, PD and DLB patients had significantly lower striatal binding than healthy controls. Both PD patients in the early disease stages and DLB patients have reduced availability of striatal DAT, and DLB patients lower hypothalamic SERT compared with healthy controls. These observations add to the growing body of evidence that PD and DLB are not merely dopaminergic diseases, thereby providing additional clinicopathological insights.

In this retrospective cross-sectional study we compared 123I‑N‑ω‑fluoropropyl‑2β‑carbomethoxy‑3β‑(4‑iodophenyl)nortropane (123I-FP-CIT) binding to the striatal dopamine and the extrastriatal serotonin transporter (DAT and SERT, respectively) between Parkinson's disease (PD) and dementia with Lewy bodies (DLB) to gain more insight in the pathophysiology of the two diseases. We compared 123I-FP-CIT single photon emission computed tomography scans of, age-, gender matched patients with cognitive decline in same range of severity with PD (n = 53) or DLB (n = 53) using a regions of interest (ROIs) approach. We derived ROIs anatomically from individual magnetic resonance imaging brain scans. To corroborate the ROI findings, we performed additional whole-brain voxel-based analyses. In both ROI and voxel-based analyses, 123I-FP-CIT binding in PD patients was significantly lower in the bilateral posterior putamen than in DLB patients (left: F(1,103) = 18.363, P < 0.001, ω2  = 0.14; right: F(1,103) = 20.434, P < 0.001, ω2  = 0.15) (Pcorr  < 0.033). Caudate/putamen ratios were also significantly lower in DLB than in PD (U(105) = 724.0, P < 0.001). Extrastriatal SERT binding showed no difference between PD and DLB. These results suggest similar involvement of serotonergic structures in the degenerative process in PD and DLB.

Regional cortical thinning in dementia with Lewy bodies (DLB) and Parkinson disease dementia (PDD) may underlie some aspect of their clinical impairments; cortical atrophy likely reflects extensive Lewy body pathology with alpha-synuclein deposits, as well as associated Alzheimer's disease co-pathologies, when present. Here we investigated the topographic distribution of cortical thinning in these Lewy body diseases compared to cognitively normal PD and healthy non-PD control subjects, explored the association of regional thinning with clinical features and evaluated the impact of amyloid deposition.

Prior work has shown that functional connectivity between the midbrain and putamen is altered in patients with impairments in the dopamine system. This study examines whether individual differences in midbrain-striatal connectivity are proportional to the integrity of the dopamine system in patients with nigrostriatal dopamine loss (Parkinson's disease and dementia with Lewy bodies). We assessed functional connectivity of the putamen during resting state fMRI and dopamine transporter (DAT) availability in the striatum using 11C-Altropane PET in twenty patients. In line with the hypothesis that functional connectivity between the midbrain and the putamen reflects the integrity of the dopaminergic neurotransmitter system, putamen-midbrain functional connectivity was significantly correlated with striatal DAT availability even after stringent control for effects of head motion. DAT availability did not relate to functional connectivity between the caudate and thalamus/prefrontal areas. As such, resting state functional connectivity in the midbrain-striatal pathway may provide a useful indicator of underlying pathology in patients with nigrostriatal dopamine loss.

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.

Parkinson's disease (PD) episodic memory impairments are common; however, it is not known whether these impairments are due to hippocampal pathology. Hippocampal Lewy-bodies emerge by Braak stage 4, but are not uniformly distributed. For instance, hippocampal CA1 Lewy-body pathology has been specifically associated with pre-mortem episodic memory performance in demented patients. By contrast, the dentate gyrus (DG) is relatively free of Lewy-body pathology. In this study, we used ultra-high field 7-Tesla to measure hippocampal subfields in vivo and determine if these measures predict episodic memory impairment in PD during life.

Patients with dementia with Lewy bodies (DLB) are characterized by hypometabolism in the parieto-occipital cortex and the cingulate island sign (CIS) on 18F-fluorodeoxyglucose (FDG) PET. Whether this pattern of hypometabolism is present as early as the prodromal stage of DLB is unknown. We investigated the pattern of hypometabolism in patients with mild cognitive impairment (MCI) who progressed to probable DLB compared to MCI patients who progressed to Alzheimer's disease (AD) dementia and clinically unimpaired (CU) controls.

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.

Parkinson's dementia is a common and devastating part of Parkinson's disease. Whilst timing and severity vary, dementia in Parkinson's is often preceded by visual dysfunction. White matter changes, representing axonal loss, occur early in the disease process. Clarifying which white matter connections are affected in Parkinson's with visual dysfunction and why specific connections are vulnerable will provide important mechanistic insights. Here, we use diffusion tractography in 100 Parkinson's patients (33 low visual performers) and 34 controls to identify patterns of connectivity loss in Parkinson's with visual dysfunction. We examine the relationship between regional transcription and connectivity loss, using the Allen Institute for Brain Science transcriptome atlas. We show that interhemispheric connections are preferentially affected in Parkinson's low visual performers. Interhemispheric connection loss was associated with downweighted genes related to the smoothened signalling pathway (enriched in glutamatergic neurons) and upweighted metabolic genes. Risk genes for Parkinson's but not Alzheimer's or Dementia with Lewy bodies were over-represented in upweighted genes associated with interhemispheric connection loss. Our findings suggest selective vulnerability in Parkinson's patients at highest risk of dementia (those with visual dysfunction), where differences in gene expression and metabolic dysfunction, affecting longer connections with higher metabolic burden, drive connectivity loss.

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[18F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[18F]FDG-PET data. The objective of the study was to evaluate 2-[18F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[18F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[18F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[18F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[18F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.