Functional and Structural MRI Biomarkers to Detect Pre-Clinical Neurodegeneration

IntroductionDiabetes affects both the peripheral and central nervous systems. The aim of this study was to explore the changes in brain activity in response to thermal stimuli in diabetic patients with and without diabetic peripheral neuropathy (DPN) using functional magnetic resonance imaging (fMRI).MethodsA total of 36 right-handed volunteers were enrolled: eight patients with Type-2 diabetes mellitus and DPN, 13 patients with Type-2 diabetes mellitus lacking DPN (NDPN patients), and 15 healthy volunteers (HV). Blood oxygenation level-dependent baseline scans were performed, first without any stimuli, and then with four sessions of thermal stimuli (0, 10, 34, and 44°C, in a random order) applied to the lateral side of the right lower extremity. There was a 240-s rest interval between each thermal stimulation. Each stimulation session consisted of three cycles of 30 s of stimulation followed by 30 s of rest. After each stimuli session, the participant rated pain and itch perception on a visual analog scale. The fMRI data series were analyzed by using Statistical Parametric Mapping 8 and Data Processing Assistant for Resting-State fMRI.ResultsIn response to temperature stimuli, DPN patients showed stronger activation than HV and NDPN patients, not only in brain areas that participate in somatosensory pathways (right insula, left caudate nucleus, frontal gyrus, and cingulate cortex), but also in the cognition-related cerebral areas (right temporal lobe, left hippocampus, and left fusiform gyrus). Activation of vermis 1–3 was greater in NDPN patients than in HV in response to 0°C stimulation.ConclusionsfMRI may be useful for the early detection of central nervous system impairment caused by DPN. Our results indicate that central nervous system impairment related to diabetic neuropathy may not be limited to motion- and sensation-related cortical regions. Cognition-associated cerebral regions such as the hippocampus and fusiform gyrus are also affected by functional changes caused by DPN. This suggests that fMRI can detect the early stages of cognitive impairment in DPN patients before the symptoms become clinically significant.

The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.

1AbstractThe arrival of submillimetre ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the Blood Oxygenation Level Dependent (BOLD) signal measured by Gradient-Echo fMRI is biased towards superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations and example human 7T fMRI data from Regions-of-Interest (ROIs) during a visual attention paradigm. The simulations show that two methods - the ratio of ROI means across conditions and a novel application of Deming regression - offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the example dataset, these methods suggest that attentional modulation of activation is similar across cortical layers within the ventral visual stream, despite a naïve activation-based analysis producing stronger modulation in superficial layers. Our study demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of Gradient-Echo fMRI data.