dendrites, as well as the synthesis of proteins upon stimulation, add to the neuronal energy usage. In this review, we discuss how this enormous amount of ATP is supplied by the different routes of ATP generation and is altered in disease states.Energy metabolism is mediated by the interplay of cytosolic glycolysis with mitochondrial oxidative phosphorylation (OXPHOS). Glucose is the main energy source for the brain and is first imported into the cytoplasm of neurons or astrocytes and converted to pyruvate via glycolysis. Pyruvate is then transported across the mitochondrial membranes and decarboxylated to form acetyl-CoenzymeA (acetyl-CoA). Acetyl-CoA is also the final product of fatty acid β-oxidation, which can occur in mitochondria or peroxisomes alike. Another source of acetyl-CoA is ketone bodies derived from fatty acid oxidation in the liver and secreted into the bloodstream for uptake by extrahepatic tissues. Acetyl-CoA from various sources can then enter the tricarboxylic acid (TCA) cycle to produce the reducing equivalents NADH and FADH, which will finally be fed into the respiratory chain to produce ATP.Although mitochondria are essential for OXPHOS, they are more than just the "powerhouse of the cell," as they also regulate several catabolic processes, such as amino acid or steroid synthesis, influence Ca 2+ and redox equivalent concentrations in the cytosol, and are crucial hubs in the execution of cell death (Figure 1). Thus, it is impossible to review mitochondrial energy metabolism in health and disease without discussing other mitochondrial functions. Therefore, we will briefly describe our current knowledge of mitochondrial function in other catabolic and homeostatic processes. Further, we discuss the cellular responses to mitochondrial dysfunction and how these pathways enhance or deteriorate the pathogenesis of Parkinson's (PD), a neurodegenerative disease that is deeply linked to mitochondrial dysfunction. ATP Sources in NeuronsUnlike most metabolically demanding tissues such as muscle, neurons do not have significant energy stores in the form of glycogen, lipids, or creatine phosphate. [4] As a result, neuronal energy metabolism is tightly regulated, and even acute interruptions in fuel supply rapidly suppress cognitive function. Below, we discuss some of the important sources of ATP in neurons and their contribution to energetic homeostasis and neuronal survival. Mitochondria are the main suppliers of neuronal adenosine triphosphate and play a critical role in brain energy metabolism. Mitochondria also serve as Ca 2+ sinks and anabolic factories and are therefore essential for neuronal function and survival. Dysregulation of neuronal bioenergetics is increasingly implicated in neurodegenerative disorders, particularly Parkinson's disease. This review describes the role of mitochondria in energy metabolism under resting conditions and during synaptic transmission, and presents evidence for the contribution of neuronal mitochondrial dysfunction to Parkinson's disease.

Physiological noise has been shown to have a large impact on the quality of functional MRI data, especially in areas close to fluid-filled cavities and arteries, such as the brainstem. Commonly, physiological recordings during scanning are transformed with methods such as RETROICOR and used as nuisance regressors in general linear models to remove variance associated with cardiac and respiratory cycles from the data. In contrast, modern pre-processing pipelines such as fMRIPrep, have created easy access to streamlined data-driven noise reduction methods such as aCompCor and ICA-AROMA. In combination, these methods have shown efficacy in correcting for motion, scanner as well as physiological artifacts. Given the ease of usability, it has to be questioned, whether there is any added benefit to applying logistically demanding methods such as RETROICOR. To answer this question, we applied RETROICOR, ICA-AROMA and aCompCor to a resting-state data set and compared variance explained by the respective methods and improvements in temporal signal-to-noise ratio throughout different regions of interest in the brain. In line with previous literature, RETROICOR significantly explains variance throughout the brain with peaks around areas of strong cardiac pulsations. ICA-AROMA and aCompCor largely account for the same variance. Nonetheless, RETROICOR retains unique explanatory power in individual participants. Further analysis points towards a pattern of unreliability of ICA-AROMA and aCompCor to consistently remove physiological noise across recordings, which is compensated by RETROICOR. While some of this inconsistency could be attributed to misclassifications in the noise selection models of ICA-AROMA, most is likely the consequence of secondary factors such as fMRI sequence parameters (e.g. long TR) limiting the efficiency of aCompCor and ICA-AROMA. Thus, it is advisable to additionally apply RETROICOR, especially when assuming regionally high levels of physiological noise.