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Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.
The most frequent neurodegenerative diseases (NDs) are Alzheimer’s disease (AD), Parkinson’s disease (PD), and frontotemporal lobar degeneration associated with protein TDP-43 (FTLD–TDP). Neuropathologically, NDs are characterized by abnormal intracellular and extra-cellular protein deposits and by disease-specific neuronal death. Practically all terminal stages of NDs are clinically associated with dementia. Therefore, major attention was directed to protein deposits and neuron loss in supratentorial (telencephalic) brain regions in the course of NDs. This was also true for PD, although the pathological hallmark of PD is degeneration of pigmented neurons of the brainstem’s substantia nigra (SN). However, PD pathophysiology was explained by dopamine depletion in the telencephalic basal ganglia due to insufficiency and degeneration of the projection neurons located in SN. In a similar line of argumentation AD- and FTLD-related clinical deficits were exclusively explained by supratentorial allo- and neo-cortical laminar neuronal necrosis. Recent comprehensive studies in AD and PD early stages found considerable and unexpected involvement of brainstem nuclei, which could have the potential to profoundly change our present concepts on origin, spread, and early clinical diagnosis of these diseases. In contrast with PD and AD, few studies addressed brainstem involvement in the course of the different types of FTLD–TDP. Some of the results, including ours, disclosed a higher and more widespread pathology than anticipated. The present review will focus mainly on the impact of brainstem changes during the course of the most frequent NDs including PD, AD, and FTLD–TDP, with special emphasis on the need for more comprehensive research on FTLDs.
Background: The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia.
Methods: Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures.
Results: Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score.
Conclusions: The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.