NeuroImage
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We used a previously validated automated machine learning algorithm based on adaptive boosting to segment the hippocampi in baseline and 12-month follow-up 3D T1-weighted brain MRIs of 150 cognitively normal elderly (NC), 245 mild cognitive impairment (MCI) and 97 Dementia of the Alzheimer's type (DAT) ADNI subjects. Using the radial distance mapping technique, we examined the hippocampal correlates of delayed recall performance on three well-established verbal memory tests--ADAScog delayed recall (ADAScog-DR), the Rey Auditory Verbal Learning Test -DR (AVLT-DR) and Wechsler Logical Memory II-DR (LM II-DR). We observed no significant correlations between delayed recall performance and hippocampal radial distance on any of the three verbal memory measures in NC. ⋯ In DAT we observed stronger left-sided associations between hippocampal radial distance, LM II-DR and ADAScog-DR both at baseline and at follow-up. The strongest linkage between memory performance and hippocampal atrophy in the MCI sample was observed with the most challenging verbal memory test-the AVLT-DR, as opposed to the DAT sample where the least challenging test the ADAScog-DR showed strongest associations with the hippocampal structure. After controlling for baseline hippocampal atrophy, memory performance showed regionally specific associations with hippocampal radial distance in predominantly CA1 but also in subicular distribution.
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Functional MRI (fMRI) of default mode network (DMN) brain activity during resting state is gaining attention as a potential non-invasive biomarker to diagnose incipient Alzheimer's disease. The aim of this study was to identify effects of normal aging on the DMN using different methods of fMRI processing and evaluation. ⋯ Effects of normal aging such as loss of PCC co-activity could be detected by ICA, but not by signal time course correlation analyses of DMN inter-connectivity. This either indicates lower sensitivity of inter-connectivity measures to detect subtle DMN changes or indicate that ICA and time course analyses determine different properties of DMN co-activation. Our results, therefore, provide fundamental knowledge for a potential future use of functional MRI as biomarker for neurodegenerative dementias where diminished DMN activity needs to be reliably differentiated from that observed in health aging.
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Comparative Study
A group model for stable multi-subject ICA on fMRI datasets.
Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. ⋯ We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study.
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Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. ⋯ This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements.
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Recent studies have shown that functional magnetic resonance imaging (fMRI) can non-invasively assess spinal cord activity. Yet, a quantitative description of nociceptive and non-nociceptive responses in the human spinal cord, compared with random signal fluctuations in resting state data, is still lacking. Here we have investigated the intensity and spatial extent of blood oxygenation level dependent (BOLD) fMRI responses in the cervical spinal cord of healthy volunteers, elicited by stimulation of the hand dorsum (C6-C7 dermatomes). ⋯ In a second, general linear model analysis, we identified a voxel population preferentially responding to noxious stimulation, which extended rostro-caudally over the length (4 cm) of the explored spinal cord region. By contrast, we found no evidence of voxel populations responding uniquely to innocuous stimuli, or showing decreased activity following either kind of somatosensory stimulus. These results provide the first false-positive-controlled comparison of spinal BOLD fMRI responses to noxious and innocuous stimuli in humans, confirming and extending physiological information obtained in other species.