Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD ...are still missing, though they could improve diagnostic accuracy.
We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis.
Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD.
Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.
•Neuroimaging biomarkers could increase diagnostic accuracy in Parkinson's disease.•Multimodal meta-analysis including whole-brain FDG-PET, MRI, and DTI•Meta-analysis included 2323 Parkinson's disease patients and 1767 controls.•FDG-PET functional changes are more consistent than structural changes in MRI-VBM /DTI.•Glucose hypometabolism is detected in inferior parietal cortex/caudate nucleus.
In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to ...Alzheimer's disease (AD) dementia and non-AD dementias.
We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as “typical-AD”, “atypical-AD” (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), “non-AD” (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or “negative” patterns. To perform the statistical analyses, the individual patterns were grouped either as “AD dementia vs. non-AD dementia (all diseases)” or as “FTD vs. non-FTD (all diseases)”. Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated.
The multivariate logistic model identified FDG-PET “AD” SPM classification (Expβ = 19.35, 95% C.I. 4.8–77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64–25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The “FTD” SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1–63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55–70.46, p < 0.001).
Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers.
•Appropriate biomarkers measures improve early dementia diagnosis in MCI.•FDG-PET-SPM maps and CSF Aβ42 are the best predictors of AD dementia conversion.•FDG-PET-SPM maps accurately predict conversion to different dementia conditions.•A negative FDG-PET-SPM pattern characterizes stable or reverter MCI cases.
Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet ...applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.
We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.
Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r ≈ -0.86, p < 0.001).
The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.
The effects of dopaminergic therapy for Parkinson's disease (PD) on the brain functional architecture are still unclear. We investigated this topic in 31 PD patients (disease duration: 11.2 ± (SD) ...3.6 years) who underwent clinical and MRI assessments under chronic dopaminergic treatment (duration: 8.3 ± (SD) 4.4 years) and after its withdrawal. Thirty healthy controls were also included. Functional and morphological changes were studied, respectively, with eigenvector centrality mapping and seed-based connectivity, and voxel-based morphometry. Patients off medication, compared to controls, showed increased connectivity in cortical sensorimotor areas extending to the cerebello-thalamo-cortical pathway and parietal and frontal brain structures. Dopaminergic therapy normalized this increased connectivity. Notably, patients showed decreased interconnectedness in the medicated compared to the unmedicated condition, encompassing putamen, precuneus, supplementary motor and sensorimotor areas bilaterally. Similarly, lower connectivity was found comparing medicated patients to controls, overlapping with the within-group comparison in the putamen. Seed-based analyses revealed that dopaminergic therapy reduced connectivity in motor and default mode networks. Lower connectivity in the putamen correlated with longer disease duration, medication dose, and motor symptom improvement. Notably, atrophy and connectivity changes were topographically dissociated. After chronic treatment, dopaminergic therapy decreases connectivity of key motor and default mode network structures that are abnormally elevated in PD off condition.
Levodopa and, later, deep brain stimulation (DBS) have become the mainstays of therapy for motor symptoms associated with Parkinson's disease (PD). Although these therapeutic options lead to similar ...clinical outcomes, the neural mechanisms underlying their efficacy are different. Therefore, investigating the differential effects of DBS and levodopa on functional brain architecture and associated motor improvement is of paramount interest. Namely, we expected changes in functional brain connectivity patterns when comparing levodopa treatment with DBS.
Clinical assessment and functional magnetic resonance imaging (fMRI) was performed before and after implanting electrodes for DBS in the subthalamic nucleus (STN) in 13 PD patients suffering from severe levodopa-induced motor fluctuations and peak-of-dose dyskinesia. All measurements were acquired in a within subject-design with and without levodopa treatment, and with and without DBS. Brain connectivity changes were computed using eigenvector centrality (EC) that offers a data-driven and parameter-free approach—similarly to Google's PageRank algorithm—revealing brain regions that have an increased connectivity to other regions that are highly connected, too. Both levodopa and DBS led to comparable improvement of motor symptoms as measured with the Unified Parkinson's Disease Rating Scale motor score (UPDRS-III). However, this similar therapeutic effect was underpinned by different connectivity modulations within the motor system. In particular, EC revealed a major increase of interconnectedness in the left and right motor cortex when comparing DBS to levodopa. This was accompanied by an increase of connectivity of these motor hubs with the thalamus and cerebellum.
We observed, for the first time, significant functional connectivity changes when comparing the effects of STN DBS and oral levodopa administration, revealing different treatment-specific mechanisms linked to clinical benefit in PD. Specifically, in contrast to levodopa treatment, STN DBS was associated with increased connectivity within the cortico-thalamo-cerebellar network. Moreover, given the favorable effects of STN DBS on motor complications, the changes in the patients' clinical profile might also contribute to connectivity changes associated with STN-DBS. Understanding the observed connectivity changes may be essential for enhancing the effectiveness of DBS treatment, and for better defining the pathophysiology of the disrupted motor network in PD.
•Functional MRI was done before and after implanting DBS electrodes in same patients.•Impacts of DBS and levodopa administration on brain motor circuitry are different.•Comparison between DBS and levodopa treatment shows a major connectivity increase.•Treatment related connectivity changes can be disentangled from electrode implantation.
Although Alzheimer’s disease presents homogeneous histopathology, it causes several clinical phenotypes depending on brain regions involved. Beside the most abundant memory variant several atypical ...variants exist. Among them posterior cortical atrophy (PCA) is associated with severe visuospatial / visuoperceptual deficits in the absence of significant primary ocular disease. Here, we report for the first time a case of Capgras delusion – a delusional misidentification syndrome, where patients think that familiar persons are replaced by identical ‘doubles’ or an impostor – in a patient with PCA. The 57-year-old female patient was diagnosed with PCA and developed Capgras delusion eight years after first symptoms. The patient did not recognize her husband, misidentified him as a stranger and perceived him as a threat. Such misidentifications did not happen for other persons. Events could be interrupted by reassuring the husband’s identity by the patient’s female friend or children. We applied in-depth multimodal neuroimaging phenotyping and used single-subject voxel-based morphometry to identify atrophy changes specifically related to the development of the Capgras delusion. The latter, based on structural T1 magnetic resonance imaging, revealed progressive gray matter volume decline in occipital and temporoparietal areas, involving more the right than the left hemisphere, especially at the beginning. Correspondingly, the right fusiform gyrus was already affected by atrophy at baseline, whereas the left fusiform gyrus became involved in the further disease course. At baseline, glucose hypometabolism as measured by positron emission tomography (PET) with F18-fluorodesoxyglucose (FDG-PET) was evident in the parietooccipital cortex, more pronounced right-sided, and in the right frontotemporal cortex. Amyloid accumulation as assessed by PET with F18-florbetaben was found in the gray matter of the neocortex indicating underlying Alzheimer’s disease. Appearance of the Capgras delusion was related to atrophy in the right posterior cingulate gyrus/precuneus, as well as right middle frontal gyrus/frontal eye field, supporting right frontal areas as particularly relevant for Capgras delusion. Atrophy in these regions respectively might affect the default mode and dorsal attention networks as shown by meta-analytical co-activation and resting state functional connectivity analyses. This case elucidates the brain-behavior relationship in PCA and Capgras delusion.
•Diagnosis of dementia is challenging – especially in rare / orphan syndromes.•Classifiers were developed for seven dementia syndromes in 477 subjects using MRI.•Focus on frontotemporal lobar ...degeneration syndromes, and Alzheimer’s disease.•SVM performed optimal in classification vs controls, reasonable in multi-syndromes.•Automated methods for MR imaging are ready for translation to clinical routine.
Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).
Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).
The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.
Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease.
We aimed at testing the potential of biomarkers in predicting individual patient response to dopaminergic therapy for Parkinson's disease. Treatment efficacy was assessed in 30 Parkinson's disease ...patients as motor symptoms improvement from unmedicated to medicated state as assessed by the Unified Parkinson's Disease Rating Scale score III. Patients were stratified into weak and strong responders according to the individual treatment response. A multiple regression was implemented to test the prediction accuracy of age, disease duration and treatment dose and length. Univariate voxel-based morphometry was applied to investigate differences between the two groups on age-corrected T1-weighted magnetic resonance images. Multivariate support vector machine classification was used to predict individual treatment response based on neuroimaging data. Among clinical data, increasing age predicted a weaker treatment response. Additionally, weak responders presented greater brain atrophy in the left temporoparietal operculum. Support vector machine classification revealed that gray matter density in this brain region, including additionally the supplementary and primary motor areas and the cerebellum, was able to differentiate weak and strong responders with 74% accuracy. Remarkably, age and regional gray matter density of the left temporoparietal operculum predicted both and independently treatment response as shown in a combined regression analysis. In conclusion, both increasing age and reduced gray matter density are valid and independent predictors of dopaminergic therapy response in Parkinson's disease.
•Can brain MRI data predict dopaminergic treatment response in Parkinson's disease?•Lower gray matter density predicts a weaker response to dopaminergic therapy.•Age, but not treatment dose/duration, predicts response to dopaminergic therapy.•Imaging metrics and age are independent predictors of treatment response.•MRI-based machine learning helps to estimate individual treatment response.
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