Lewandowski, Nicole M.; Bordelon, Yvette; Brickman, Adam M.; Angulo, Sergio; Khan, Usman; Muraskin, Jordan; Griffith, Erica Y.; Wasserman, Paula; Menalled, Liliana; Vonsattel, Jean Paul; Marder, Karen; Small, Scott A.; Moreno, Herman
Although the huntingtin gene is expressed in brain throughout life, phenotypically Huntington's disease (HD) begins only in midlife to affect specific brain regions. Here, to investigate regional vulnerability in the disease, we used functional magnetic resonance imaging (fMRI) to translationally link studies in patients with a mouse model of disease. Using fMRI, we mapped cerebral blood volume (CBV) in three groups: HD patients, symptom-free carriers of the huntingtin genetic mutation, and age-matched controls. In contrast to a region in the anterior caudate, in which dysfunction was linked to genotype independent of phenotype, a region in the posterior body of the caudate was differentially associated with disease phenotype. Guided by these observations, we harvested regions from the anterior and posterior body of the caudate in postmortem control and HD human brain tissue. Gene-expression profiling identified two molecules whose expression levels were most strongly correlated with regional vulnerability — protein phosphatase 1 regulatory subunit 7 (PPP1R7) and Wnt inhibitory factor-1 (WIF1). To verify and potentially extend these findings, we turned to the YAC128 (C57BL/6J) HD transgenic mice. By fMRI we longitudinally mapped CBV in transgenic and wildtype (WT) mice...
The prevalence of Alzheimer's disease (AD) is predicted to increase rapidly in the coming decade, highlighting the importance of early detection and intervention in patients with AD and mild cognitive impairment (MCI). Recently, remarkable advances have been made in the application of neuroimaging techniques in investigations of AD and MCI. Among the various neuroimaging techniques, functional magnetic resonance imaging (fMRI) has many potential advantages, noninvasively detecting alterations in brain function that may be present very early in the course of AD and MCI. In this paper, we first review task-related and resting-state fMRI studies on AD and MCI. We then present our recent fMRI studies with additional event-related potential (ERP) experiments during a motion perception task in MCI. Our results indicate that fMRI, especially when combined with ERP recording, can be useful for detecting spatiotemporal functional changes in AD and MCI patients.
Schizophrenia (SZ) is associated with a reduced ability to set meaningful goals to reach desired outcomes. The delay-discounting (DD) task, in which one chooses between sooner smaller and later larger rewards, has proven useful in revealing executive function and reward deficits in various clinical groups. We used fMRI in patients with SZ and healthy controls (HC) to compare brain activation during performance of a DD task. Prior to the neuroimaging session, we obtained each participant's rate of DD, k, on a DD task and used it to select a version of the DD task for each participant's fMRI session. Because of the importance of comparing fMRI results from groups matched on performance, we used a criterion value of R2 > 0.60 for response consistency on the DD task to analyze fMRI activation to DD task versus control trials from consistent SZ (n = 14) and consistent HC (n = 14). We also compared activation between the groups on contrasts related to trial difficulty. Finally, we contrasted the inconsistent SZ (n = 9) with the consistent HC and consistent SZ; these results should be interpreted with caution because of inconsistent SZ's aberrant performance on the task. Compared with consistent HC, consistent SZ showed reduced activation to DD task versus control trials in executive function and reward areas. In contrast...
In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex...
This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis—better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al....
The perfusion contribution to the total functional magnetic resonance imaging (fMRI) signal was investigated using a rat model with mild hypercapnia at 9.4 T, and human subjects with visual stimulation at 4 T. It was found that the total fMRI signal change could be approximated as a linear superposition of ‘true' blood oxygenation level-dependent (BOLD; T2/T2*) effect and the blood flow-related (T1) effect. The latter effect was significantly enhanced by using short repetition time and large radiofrequency pulse flip angle and became comparable to the ‘true' BOLD signal in response to a mild hypercapnia in the rat brain, resulting in an improved contrast-to-noise ratio (CNR). Bipolar diffusion gradients suppressed the intravascular signals but had no significant effect on the flow-related signal. Similar results of enhanced fMRI signal were observed in the human study. The overall results suggest that the observed flow-related signal enhancement is likely originated from perfusion, and this enhancement can improve CNR and the spatial specificity for mapping brain activity and physiology changes. The nature of mixed BOLD and perfusion-related contributions in the total fMRI signal also has implication on BOLD quantification...
Rising life expectancies coupled with an increasing awareness of age-related cognitive decline have led to the unwarranted use of psychopharmaceuticals, including acetylcholinesterase inhibitors (AChEIs), by significant numbers of healthy older individuals. This trend has developed despite very limited data regarding the effectiveness of such drugs on non-clinical groups and recent work indicates that AChEIs can have negative cognitive effects in healthy populations. For the first time, we use a combination of EEG and simultaneous EEG/fMRI to examine the effects of a commonly prescribed AChEI (donepezil) on cognition in healthy older participants. The short- and long-term impact of donepezil was assessed using two double-blind, placebo-controlled trials. In both cases, we utilised cognitive (paired associates learning (CPAL)) and electrophysiological measures (resting EEG power) that have demonstrated high-sensitivity to age-related cognitive decline. Experiment 1 tested the effects of 5 mg/per day dosage on cognitive and EEG markers at 6-hour, 2-week and 4-week follow-ups. In experiment 2, the same markers were further scrutinised using simultaneous EEG/fMRI after a single 5 mg dose. Experiment 1 found significant negative effects of donepezil on CPAL and resting Alpha and Beta band power. Experiment 2 replicated these results and found additional drug-related increases in the Delta band. EEG/fMRI analyses revealed that these oscillatory differences were associated with activity differences in the left hippocampus (Delta)...
Functional MRI (fMRI) has uncovered widespread hemodynamic fluctuations in the brain during rest. Recent electroencephalographic work in humans and microelectrode recordings in anesthetized monkeys have shown this activity to be correlated with slow changes in neural activity. Here we report that the spontaneous fluctuations in the local field potential (LFP) measured from a single cortical site in monkeys at rest exhibit widespread, positive correlations with fMRI signals over nearly the entire cerebral cortex. This correlation was especially consistent in a band of upper gamma-range frequencies (40–80 Hz), for which the hemodynamic signal lagged the neural signal by 6–8 s. A strong, positive correlation was also observed in a band of lower frequencies (2–15 Hz), albeit with a lag closer to zero. The global pattern of correlation with spontaneous fMRI fluctuations was similar whether the LFP signal was measured in occipital, parietal, or frontal electrodes. This coupling was, however, dependent on the monkey's behavioral state, being stronger and anticipatory when the animals’ eyes were closed. These results indicate that the often discarded global component of fMRI fluctuations measured during the resting state is tightly coupled with underlying neural activity.
The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.
In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain.
The posteromedial cortices and other regions of the “default network” are particularly vulnerable to the pathology of Alzheimer disease (AD). In this study, we performed fMRI to investigate whether the presence of apolipoprotein E (APOE) ε4 allele and degree of memory impairment were associated with dysfunction of these brain regions. Seventy-five elderly subjects ranging from cognitively normal to mild AD, divided into ε4 carriers and non-carriers, underwent fMRI during a memory encoding task. Across all subjects, posteromedial and ventral anterior cingulate cortices (key components of the default network) as well as right middle and inferior prefrontal regions demonstrated reduced task-induced deactivation in the ε4 carriers relative to non-carriers. Even among cognitively normal subjects, ε4 carriers demonstrated reduced posteromedial deactivation compared to non-carriers, in the same regions which demonstrated failure of deactivation in AD patients. Greater failure of posteromedial deactivation was related to worse memory performance (delayed recall) across all subjects and within the range of cognitively normal subjects. In summary, the posteromedial cortical fMRI response pattern is modulated both by the presence of APOE ε4 and episodic memory capability. Altered fMRI activity of the posteromedial areas of the brain default network may be an early indicator of risk for AD.
The aim of this study is to assess the value of resting-state fMRI in detecting the acute effects of alcohol on healthy human brains. Thirty-two healthy volunteers were studied by conventional MR imaging and resting-state fMRI prior to and 0.5 hours after initiation of acute alcohol administration. The fMRI data, acquired during the resting state, were correlated with different breath alcohol concentrations (BrAC). We use the posterior cingulate cortex/precuneus as a seed for the default mode network (DMN) analysis. ALFF and ReHo were also used to investigate spontaneous neural activity in the resting state. Conventional MR imaging showed no abnormalities on all subjects. Compared with the prior alcohol administration, the ALFF and ReHo also indicated some specific brain regions which are affected by alcohol, including the superior frontal gyrus, cerebellum, hippocampal gyrus, left basal ganglia, and right internal capsule. Functional connectivity of the DMN was affected by alcohol. This resting-state fMRI indicates that brain regions implicated are affected by alcohol and might provide a neural basis for alcohol's effects on behavioral performance.
Functional magnetic resonance imaging (fMRI) is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer's disease. In the first stage, spatial independent component analysis is applied to group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact logistic regression for matched pairs data. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.
Introduction. Aggressive surgical resection constitutes the optimal treatment for intracranial gliomas. However, the proximity of a tumor to eloquent areas requires exact knowledge of its anatomic relationships to functional cortex. The purpose of our study was to evaluate fMRI's accuracy by comparing it to intraoperative cortical stimulation (DCS) mapping. Material and Methods. Eighty-seven patients, with presumed glioma diagnosis, underwent preoperative fMRI and intraoperative DCS for cortical mapping during tumor resection. Findings of fMRI and DCS were considered concordant if the identified cortical centers were less than 5 mm apart. Pre and postoperative Karnofsky Performance Scale and Spitzer scores were recorded. A postoperative MRI was obtained for assessing the extent of resection. Results. The areas of interest were identified by fMRI and DCS in all participants. The concordance between fMRI and DCS was 91.9% regarding sensory-motor cortex, 100% for visual cortex, and 85.4% for language. Data analysis showed that patients with better functional condition demonstrated higher concordance rates, while there also was a weak association between tumor grade and concordance rate. The mean extent of tumor resection was 96.7%. Conclusions. Functional MRI is a highly accurate preoperative methodology for sensory-motor mapping. However...
Previous work indicates that resting-state functional magnetic resonance imaging (fMRI) is sensitive to functional brain changes related to Alzheimer's disease (AD) pathology across the clinical spectrum. Cross-sectional studies have found functional connectivity differences in the brain's default mode network in aging, mild cognitive impairment, and AD. In addition, two recent longitudinal studies have shown that functional connectivity changes track AD progression. This earlier work suggests that resting-state fMRI may be a promising biomarker for AD. However, some key issues still need to be addressed before resting-state fMRI can be successfully applied clinically. In a previous issue of Alzheimer's Research & Therapy, Vemuri and colleagues discuss the use of resting-state fMRI in the study of AD. In this commentary, I will highlight and expand upon some of their main conclusions.
The auditory oddball task is a well-studied stimulus paradigm used to investigate the neural correlates of simple target detection. It elicits several classic event-related potentials (ERPs), the most prominent being the P300 which is seen as a neural correlate of subjects' detection of rare (target) stimuli. Though trial-averaging is typically used to identify and characterize such ERPs, their latency and amplitude can vary on a trial-to-trial basis reflecting variability in the underlying neural information processing. Here we simultaneously recorded EEG and fMRI during an auditory oddball task and identified cortical areas correlated with the trial-to-trial variability of task-discriminating EEG components. Unique to our approach is a linear multivariate method for identifying task-discriminating components within specific stimulus- or response- locked time windows. We find fMRI activations indicative of distinct processes that contribute to the single-trial variability during target detection. These regions are different from those found using standard, including trial-averaged, regressors. Of particular note is strong activation of the lateral occipital complex (LOC). The LOC was not seen when using traditional event-related regressors. Though LOC is typically associated with visual/spatial attention...
Traditionally, complex cultural symbols like brands are investigated with psychological approaches. Often this is done by using semantic differentials, in which participants are asked to rate a brand regarding different pairs of adjectives. Only recently, functional magnetic resonance imaging (fMRI) has been used to examine brands. In the current work we used fMRI in combination with a semantic differential to cross-validate both methods and to improve the characterization of the basic factors constituting the semantic space. To this end we presented pictures of brands while recording subject's brain activity during an fMRI experiment. Results of the semantic differential arranged the brands in a semantic space illustrating their relationships to other cultural symbols. FMRI results revealed activation of the medial prefrontal cortex for brands that loaded high on the factor ‘social competence’, suggesting an involvement of a cortical network associated with social cognitions. In contrast, brands closely related to the factor ‘potency’ showed decreased activity in the superior frontal gyri, possibly related to working memory during task performance. We discuss the results as a different engagement of the prefrontal cortex when perceiving brands as cultural symbols.
Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool
for localizing and analyzing brain activity. This study focuses on one very
important aspect of the functional properties of human brain, specifically the
estimation of the level of parallelism when performing complex cognitive tasks.
Using fMRI as the main modality, the human brain activity is investigated
through a purely data-driven signal processing and dimensionality analysis
approach. Specifically, the fMRI signal is treated as a multi-dimensional data
space and its intrinsic `complexity' is studied via dataset fractal analysis
and blind-source separation (BSS) methods. One simulated and two real fMRI
datasets are used in combination with Independent Component Analysis (ICA) and
fractal analysis for estimating the intrinsic (true) dimensionality, in order
to provide data-driven experimental evidence on the number of independent brain
processes that run in parallel when visual or visuo-motor tasks are performed.
Although this number is can not be defined as a strict threshold but rather as
a continuous range, when a specific activation level is defined, a
corresponding number of parallel processes or the casual equivalent of `cpu
cores' can be detected in normal human brain activity.; Comment: 27 pages...
Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI)
data based on a standard General Linear Model (GLM)and spectral clustering was
recently proposed as a means to alleviate the issues associated with spatial
normalization in fMRI. However, for all its appeal, a GLM-based parcellation
approach introduces its own biases, in the form of a priori knowledge about the
shape of Hemodynamic Response Function (HRF) and task-related signal changes,
or about the subject behaviour during the task. In this paper, we introduce a
data-driven version of the spectral clustering parcellation, based on
Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of
the GLM. First, a number of independent components are automatically selected.
Seed voxels are then obtained from the associated ICA maps and we compute the
PLS latent variables between the fMRI signal of the seed voxels (which covers
regional variations of the HRF) and the principal components of the signal
across all voxels. Finally, we parcellate all subjects data with a spectral
clustering of the PLS latent variables. We present results of the application
of the proposed method on both single-subject and multi-subject fMRI datasets.
Preliminary experimental results...
The most widely used task fMRI analyses use parametric methods that depend on
a variety of assumptions. While individual aspects of these fMRI models have
been evaluated, they have not been evaluated in a comprehensive manner with
empirical data. In this work, a total of 2 million random task fMRI group
analyses have been performed using resting state fMRI data, to compute
empirical familywise error rates for the software packages SPM, FSL and AFNI,
as well as a standard non-parametric permutation method. While there is some
variation, for a nominal familywise error rate of 5% the parametric statistical
methods are shown to be conservative for voxel-wise inference and invalid for
cluster-wise inference; in particular, cluster size inference with a cluster
defining threshold of p = 0.01 generates familywise error rates up to 60%. We
conduct a number of follow up analyses and investigations that suggest the
cause of the invalid cluster inferences is spatial auto correlation functions
that do not follow the assumed Gaussian shape. By comparison, the
non-parametric permutation test, which is based on a small number of
assumptions, is found to produce valid results for voxel as well as cluster
wise inference. Using real task data, we compare the results between one
parametric method and the permutation test...