Multimodal combination of neuroimaging methods for localizing the epileptogenic zone in MR-negative epilepsy.

The objective was to determine the optimal combination of multimodal imaging methods (IMs) for localizing the epileptogenic zone (EZ) in patients with MR-negative drug-resistant epilepsy. Data from 25 patients with MR-negative focal epilepsy (age 30 ± 10 years, 16M/9F) who underwent surgical resection of the EZ and from 110 healthy controls (age 31 ± 9 years; 56M/54F) were used to evaluate IMs based on 3T MRI, FDG-PET, HD-EEG, and SPECT. Patients with successful outcomes and/or positive histological findings were evaluated. From 38 IMs calculated per patient, 13 methods were selected by evaluating the mutual similarity of the methods and the accuracy of the EZ localization. The best results in postsurgical patients for EZ localization were found for ictal/ interictal SPECT (SISCOM), FDG-PET, arterial spin labeling (ASL), functional regional homogeneity (ReHo), gray matter volume (GMV), cortical thickness, HD electrical source imaging (ESI-HD), amplitude of low-frequency fluctuation (ALFF), diffusion tensor imaging, and kurtosis imaging. Combining IMs provides the method with the most accurate EZ identification in MR-negative epilepsy. The PET, SISCOM, and selected MRI-post-processing techniques are useful for EZ localization for surgical tailoring.

Contribution of the multi-echo approach in accelerated functional magnetic resonance imaging multiband acquisition.

We wanted to verify the effect of combining multi-echo (ME) functional magnetic resonance imaging (fMRI) with slice acceleration in simultaneous multi-slice acquisition. The aim was to shed light on the benefits of multiple echoes for various acquisition settings, especially for levels of slice acceleration and flip angle. Whole-brain ME fMRI data were obtained from 26 healthy volunteers (using three echoes; seven runs with slice acceleration 1, 4, 6, and 8; and two different flip angles for each of the first three acceleration factors) and processed as single-echo (SE) data and ME data based on optimal combinations weighted by the contrast-to-noise ratio. Global metrics (temporal signal-to-noise ratio, signal-to-noise separation, number of active voxels, etc.) and local characteristics in regions of interest were used to evaluate SE and ME data. ME results outperformed SE results in all runs; the differences became more apparent for higher acceleration, where a significant decrease in data quality is observed. ME fMRI can improve the observed data quality metrics over SE fMRI for a wide range of accelerated fMRI acquisitions.

Insights into déjà vu: Associations between the frequency of experience and amplitudes of low-frequency oscillations in resting-state functional magnetic resonance imaging.

The phenomenon of déjà vu (DV) has intrigued scientists for decades, yet its neurophysiological underpinnings remain elusive. Brain regions have been identified in which morphometry differs between healthy individuals according to the frequency of their DV experiences. This study built upon these findings by assessing if and how neural activity in these and other brain regions also differ with respect to DV experience. Resting-state fMRI was performed on 68 healthy volunteers, 44 of whom reported DV experiences (DV group) and 24 who did not (NDV group). Using multivariate analyses, we then assessed the (fractional) amplitude of low-frequency fluctuations (fALFF/ALFF), a metric that is believed to index brain tissue excitability, for five discrete frequency bands within sets of brain regions implicated in DV and those comprising the default mode network (DMN). Analyses revealed significantly lower values of fALFF/ALFF for specific frequency bands in the DV relative to the NDV group, particularly within mesiotemporal structures, bilateral putamina, right caudatum, bilateral superior frontal cortices, left lateral parietal cortex, dorsal and ventral medial prefrontal cortex, and the posterior cingulate cortex. The pattern of differences in fALFF/ALFF measures between the brains of individuals who have experienced DV and those who have not provides new neurophysiological insights into this phenomenon, including the potential role of the DMN. We suggest that the erroneous feeling of familiarity arises from a temporary disruption of cortico-subcortical circuitry together with the upregulation of cortical excitability.

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging.

Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients.

The role of the striatum in visuomotor integration during handwriting: an fMRI study.

This study investigates the role of the dorsal/sensorimotor striatum in visuomotor integration (i.e., the transformation of internal visual information about letter shapes into motor output) during handwriting. Twenty healthy participants underwent fMRI scanning with tasks consisting of self-paced handwriting of alphabetically ordered single letters and simple dots, with both tasks performed without visual feedback. Functional connectivity (FC) from these two tasks was compared to demonstrate the difference between coordinated activity arising during handwriting and the activity during a simpler motor condition. Our study focused upon the writing-specific cortico-striatal network of preselected regions of interest consisting of the visual word form area (VWFA), anterior intraparietal sulcus/superior parietal lobule, striatum, premotor cortex/Exner's area, and primary and supplementary motor regions. We observed systematically increased task-induced cortico-striatal and cortico-cortical FC. This increased synchronization of neural activity between the VWFA, i.e., the visual cortical area containing information about letter shapes, and the frontoparietal motor regions is mediated by the striatum. These findings suggest the involvement of the striatum in integrating stored letter-shape information with motor planning and execution during handwriting.

Social support modulates subjective and neural responses to sad mental imagery.

Mental imagery related to the recent death of a loved one is associated with intense sadness and distress. Social relations, such as with one's significant other, can regulate negative emotions and provide comfort, but the neural correlates of social comfort are largely unknown. In this functional magnetic resonance imaging study, we examined brain responses to sad mental imagery and how these are modulated by holding hands with one's romantic partner. We found that mental imagery of a recently deceased loved one was associated with increased reactivity in the dorsal striatum, medial prefrontal cortex, anterior and posterior cingulate cortex, thalamus and cerebellum. Holding hands with one's partner as compared to being alone or holding hands with a stranger provided subjective comfort and reduced neural reactivity in the dorsal striatum without affecting the vividness of the imagery. Our findings indicate an important role for the dorsal striatum in sad mental imagery and social comfort and suggest that tactile social support by one's romantic partner regulates subjective distress through other processes than mere distraction from the mental imagery.

Soothing the emotional brain: modulation of neural activity to personal emotional stimulation by social touch.

Social touch may modulate emotions, but the neurobehavioral correlates are poorly understood. Here, we investigated neural responses to a picture of a deceased close person and if neural activity and connectivity are modulated by social touch from one's romantic partner. Using functional magnetic resonance imaging, we found altered reactivity in several brain areas including the anterior cingulate cortex (ACC) and the anterior insula in response to the personal picture compared to a picture of an unfamiliar person. Hand holding with the romantic partner, compared to being alone, reduced reactivity in the ACC and cerebellum and provided subjective comfort. To separate physical touch from the emotional effect of partner presence, we evaluated hand holding with the partner relative to a stranger and found reduced reactivity in the anterior insula. Connectivity between the anterior insula and the ACC was reduced during partner touch, and the connectivity strength was negatively related to attachment security, with higher reported partner security associated with weaker connectivity. Overall, holding hands with one's partner attenuates reactivity in emotional brain areas and reduces between-region connectivity.

EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions.

BACKGROUND: Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. NEW METHOD: We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. RESULTS: The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. COMPARISON WITH EXISTING METHOD(S): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. CONCLUSIONS: This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation.

This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data-driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal-to-noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal-noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting-state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR-corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations.

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics.

OBJECTIVE: Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. APPROACH: The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. MAIN RESULTS: We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. SIGNIFICANCE: Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Changes in connectivity of the posterior default network node during visual processing in mild cognitive impairment: staged decline between normal aging and Alzheimer's disease.

Visual processing difficulties are often present in Alzheimer's disease (AD), even in its pre-dementia phase (i.e. in mild cognitive impairment, MCI). The default mode network (DMN) modulates the brain connectivity depending on the specific cognitive demand, including visual processes. The aim of the present study was to analyze specific changes in connectivity of the posterior DMN node (i.e. the posterior cingulate cortex and precuneus, PCC/P) associated with visual processing in 17 MCI patients and 15 AD patients as compared to 18 healthy controls (HC) using functional magnetic resonance imaging. We used psychophysiological interaction (PPI) analysis to detect specific alterations in PCC connectivity associated with visual processing while controlling for brain atrophy. In the HC group, we observed physiological changes in PCC connectivity in ventral visual stream areas and with PCC/P during the visual task, reflecting the successful involvement of these regions in visual processing. In the MCI group, the PCC connectivity changes were disturbed and remained significant only with the anterior precuneus. In between-group comparison, we observed significant PPI effects in the right superior temporal gyrus in both MCI and AD as compared to HC. This change in connectivity may reflect ineffective "compensatory" mechanism present in the early pre-dementia stages of AD or abnormal modulation of brain connectivity due to the disease pathology. With the disease progression, these changes become more evident but less efficient in terms of compensation. This approach can separate the MCI from HC with 77% sensitivity and 89% specificity.

Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks.

Multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method, parallel factor analysis (PARAFAC). We focused on patterns' stability over time and in population and divided the complete data set containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time, as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large-scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, the common way of dealing with EEG data. Altogether, our results suggest that PARAFAC is a suitable method for research in the field of large-scale brain networks and their manifestation in EEG signal.

Mask_explorer: A tool for exploring brain masks in fMRI group analysis.

BACKGROUND AND OBJECTIVE: Functional magnetic resonance imaging (fMRI) studies of the human brain are appearing in increasing numbers, providing interesting information about this complex system. Unique information about healthy and diseased brains is inferred using many types of experiments and analyses. In order to obtain reliable information, it is necessary to conduct consistent experiments with large samples of subjects and to involve statistical methods to confirm or reject any tested hypotheses. Group analysis is performed for all voxels within a group mask, i.e. a common space where all of the involved subjects contribute information. To our knowledge, a user-friendly interface with the ability to visualize subject-specific details in a common analysis space did not yet exist. The purpose of our work is to develop and present such interface. METHODS: Several pitfalls have to be avoided while preparing fMRI data for group analysis. One such pitfall is spurious non-detection, caused by inferring conclusions in the volume of a group mask that has been corrupted due to a preprocessing failure. We describe a MATLAB toolbox, called the mask_explorer, designed for prevention of this pitfall. RESULTS: The mask_explorer uses a graphical user interface, enables a user-friendly exploration of subject masks and is freely available. It is able to compute subject masks from raw data and create lists of subjects with potentially problematic data. It runs under MATLAB with the widely used SPM toolbox. Moreover, we present several practical examples where the mask_explorer is usefully applied. CONCLUSIONS: The mask_explorer is designed to quickly control the quality of the group fMRI analysis volume and to identify specific failures related to preprocessing steps and acquisition. It helps researchers detect subjects with potentially problematic data and consequently enables inspection of the data.

Impact of Parkinson's disease and levodopa on resting state functional connectivity related to speech prosody control.

BACKGROUND: Impaired speech prosody is common in Parkinson's disease (PD). We assessed the impact of PD and levodopa on MRI resting-state functional connectivity (rs-FC) underlying speech prosody control. METHODS: We studied 19 PD patients in the OFF and ON dopaminergic conditions and 15 age-matched healthy controls using functional MRI and seed partial least squares correlation (PLSC) analysis. In the PD group, we also correlated levodopa-induced rs-FC changes with the results of acoustic analysis. RESULTS: The PLCS analysis revealed a significant impact of PD but not of medication on the rs-FC strength of spatial correlation maps seeded by the anterior cingulate (p = 0.006), the right orofacial primary sensorimotor cortex (OF_SM1; p = 0.025) and the right caudate head (CN; p = 0.047). In the PD group, levodopa-induced changes in the CN and OF_SM1 connectivity strengths were related to changes in speech prosody. CONCLUSIONS: We demonstrated an impact of PD but not of levodopa on rs-FC within the brain networks related to speech prosody control. When only the PD patients were taken into account, the association between treatment-induced changes in speech prosody and changes in rs-FC within the associative striato-prefrontal and motor speech networks was found.

An fMRI investigation into the effect of preceding stimuli during visual oddball tasks.

BACKGROUND: This study investigates the modulatory effect of stimulus sequence on neural responses to novel stimuli. A group of 34 healthy volunteers underwent event-related functional magnetic resonance imaging while performing a three-stimulus visual oddball task, involving randomly presented frequent stimuli and two types of infrequent stimuli - targets and distractors. NEW METHOD: We developed a modified categorization of rare stimuli that incorporated the type of preceding rare stimulus, and analyzed the event-related functional data according to this sequence categorization; specifically, we explored hemodynamic response modulation associated with increasing rare-to-rare stimulus interval. RESULTS: For two consecutive targets, a modulation of brain function was evident throughout posterior midline and lateral temporal cortex, while responses to targets preceded by distractors were modulated in a widely distributed fronto-parietal system. As for distractors that follow targets, brain function was modulated throughout a set of posterior brain structures. For two successive distractors, however, no significant modulation was observed, which is consistent with previous studies and our primary hypothesis. COMPARISON WITH EXISTING METHODS: The addition of the aforementioned technique extends the possibilities of conventional oddball task analysis, enabling researchers to explore the effects of the whole range of rare stimuli intervals. CONCLUSION: This methodology can be applied to study a wide range of associated cognitive mechanisms, such as decision making, expectancy and attention.

Default mode network connectivity patterns associated with visual processing at different stages of Parkinson's disease.

BACKGROUND: The default mode network (DMN) decreases its activity when switching from a resting state to a cognitive task condition, while activity of the network engaged in the given task increases. Visual processing is typically disturbed in Parkinson's disease dementia (PDD). OBJECTIVE: Using functional MRI, we studied the DMN effective connectivity patterns in PDD as compared with cognitively normal patients with Parkinson's disease (PD) and healthy controls (HC) when switching from baseline to a visual cognitive task condition. METHODS: In all, 14 PDD, 18 PD, and 18 age-matched healthy controls participated in this functional MRI study. We used a psychophysiological interaction analysis with the precuneus (PCu) as a seed. The threshold was set at p(FWE) <0.05. RESULTS: The healthy controls showed greater PCu connectivity with the bilateral middle temporal/middle occipital gyri at baseline than during the task condition. The correlation direction changed from positive to negative. Both PD and PDD showed disturbed DMN connectivity with the brain regions that are involved in bottom-up visual processing. In PD, we also found impaired integration of the areas engaged in the ventral attentional network, which might reflect specific attentional deficits observed during the early course of PD. In mild PDD, we detected increased engagement of areas involved in the dorsal attentional network, which corresponds to increased top-down control in this patient group as compared to the healthy controls. CONCLUSION: Our results show impaired dynamic interplay between large scale brain networks in PD that spread far beyond the motor system.

An evaluation of traffic-awareness campaign videos: empathy induction is associated with brain function within superior temporal sulcus.

Acting appropriately within social contexts requires an ability to appreciate others' mental and emotional states. Indeed, some campaign programs designed to reduce anti-social behaviour seek to elicit empathy for the victims. The effectiveness of these campaigns can be evaluated according to the degree to which they induce such responses, but by applying neuroscientific techniques this can be done at the behavioural and neurophysiological level. Neuroimaging studies aimed at identifying the neural mechanisms behind such socio-cognitive and -emotional processes frequently reveal the role of the superior temporal sulcus (STS). We applied this knowledge to assess the effectiveness of traffic-awareness campaign adverts to induce empathic expression. Functional magnetic resonance imaging (fMRI) data were acquired from 20 healthy male volunteers as they watched these campaign videos consisting of a dramatic sequence of events and catastrophic endings, and control videos without such dramatic endings. Among other structures, a significantly greater neural response was observed within bilateral STS, particularly within the right hemisphere, during the observation of campaign relative to control videos. Furthermore, activation in these brain regions correlated with the subjects' empathic expression. Our results develop our understanding of the role of STS in social cognition. Moreover, our data demonstrate the utility of neuroscientific methods when evaluating the effectiveness of campaign videos in terms of their ability to elicit empathic responses. Our study also demonstrates the utility of these specific stimuli for future neuroscientific research.

Disruption of resting functional connectivity in Alzheimer's patients and at-risk subjects.

The resting brain exhibits continuous intrinsic activity, which is correlated between groups of regions forming resting state networks. Evaluating resting connectivity is a popular approach for studying brain diseases. Several hundred studies are now available that address integrity of resting connectivity in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as preclinical at-risk subjects. Most studies focus on the default mode network, a system of specific brain areas showing strong connected resting activity that attenuates during goal-directed behavior. The extent of intrinsic brain activity tends to be strongly correlated with cognitive processes and is specifically disrupted in AD and MCI patients and at-risk subjects, with changes seeming to evolve during the transition between the disease stages. In this study, we review the current findings in default mode network and other resting state network studies in AD and MCI patients and at-risk subjects as assessed by resting state functional magnetic resonance imaging.