PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (1225602)

Clipboard (0)
None

Related Articles

1.  Resting-state fMRI as a biomarker for Alzheimer's disease? 
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.
doi:10.1186/alzrt106
PMCID: PMC3334541  PMID: 22423634
2.  Fast transient networks in spontaneous human brain activity 
eLife  2014;3:e01867.
To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.
DOI: http://dx.doi.org/10.7554/eLife.01867.001
eLife digest
When subjects lie motionless inside scanners without any particular task to perform, their brains show stereotyped patterns of activity across regions known as resting state networks. Each network consists of areas with a common function, such as the ‘motor’ network or the ‘visual’ network. The role of resting state networks is unclear, but these spontaneous activity patterns are altered in disorders including autism, schizophrenia, and Alzheimer’s disease.
One puzzling feature of resting state networks is that they seem to last for relatively long times. However, the majority of studies into resting state networks have used fMRI brain scans, in which changes in the level of oxygen in the blood are used as a proxy for the activity of a given brain region. Since changes in blood oxygen occur relatively slowly, the ability of fMRI to detect rapid changes in activity is limited: it is thus possible that the long-lived nature of resting state networks is an artefact of the use of fMRI.
Now, Baker et al. have used a different type of brain scan known as an MEG scan to show that the activity of resting state networks is shorter lived than previously thought. MEG scanners measure changes in the magnetic fields generated by electrical currents in the brain, which means that they can detect alterations in brain activity much more rapidly than fMRI.
MEG recordings from the brains of nine healthy subjects revealed that individual resting state networks were typically stable for only 100 ms to 200 ms. Moreover, transitions between different networks did not occur randomly; instead, certain networks were much more likely to become active after others. The work of Baker et al. suggests that the resting brain is constantly changing between different patterns of activity, which enables it to respond quickly to any given situation.
DOI: http://dx.doi.org/10.7554/eLife.01867.002
doi:10.7554/eLife.01867
PMCID: PMC3965210  PMID: 24668169
magnetoencephalography; resting state; connectivity; non-stationary; hidden Markov model; microstates; human
3.  Resting state functional connectivity in the human spinal cord 
eLife  2014;3:e02812.
Functional magnetic resonance imaging using blood oxygenation level dependent (BOLD) contrast is well established as one of the most powerful methods for mapping human brain function. Numerous studies have measured how low-frequency BOLD signal fluctuations from the brain are correlated between voxels in a resting state, and have exploited these signals to infer functional connectivity within specific neural circuits. However, to date there have been no previous substantiated reports of resting state correlations in the spinal cord. In a cohort of healthy volunteers, we observed robust functional connectivity between left and right ventral (motor) horns, and between left and right dorsal (sensory) horns. Our results demonstrate that low-frequency BOLD fluctuations are inherent in the spinal cord as well as the brain, and by analogy to cortical circuits, we hypothesize that these correlations may offer insight into the execution and maintenance of sensory and motor functions both locally and within the cerebrum.
DOI: http://dx.doi.org/10.7554/eLife.02812.001
eLife digest
Brain imaging methods such as functional magnetic resonance imaging (fMRI) can provide us with a picture of what the brain is doing when a person is carrying out a specific task. For example, an fMRI scan recorded whilst someone is reading is likely to show activity in regions in the left hemisphere of the brain that are known to be involved in language comprehension. fMRI can also be used to measure patterns of neuronal activity when someone is awake but not engaged in a specific task. This approach, known as resting state fMRI, can be used to examine which regions of the resting brain are active at the same time. Researchers are interested in these patterns of brain activity because they reflect neural circuits that work together to produce different functions and behaviors.
Over 4000 papers have used resting state fMRI to study the human brain. However, to date there has been no conclusive investigation of resting state activity in the spinal cord. This is largely because the spinal cord is much smaller than the brain, and most fMRI scanners are not sensitive enough to study it in detail. Consequently, little is known about intrinsic neural circuits in the resting spinal cord.
Now Barry et al. have used advances in fMRI technology to show that resting state functional connectivity does indeed exist in the spinal cord. Correlations were found in the resting levels of activity between spatially distinct areas of the cord, specifically between the ventral horns and between the dorsal horns. The ventral horns relay motor signals to the body, whilst the dorsal horns receive sensory signals from the body.
These findings also have clinical applications. Some patients with incomplete spinal cord injuries can recover near normal function, but the mechanisms responsible for this recovery are unclear because clinicians have not been able to probe neuronal connections in the spinal cord in a non-invasive manner. The work of Barry et al. should help with efforts to understand the neuronal changes that support recovery from spinal cord injury.
DOI: http://dx.doi.org/10.7554/eLife.02812.002
doi:10.7554/eLife.02812
PMCID: PMC4120419  PMID: 25097248
fMRI; spinal cord; 7 Tesla; resting state; functional connectivity; human
4.  Prediction of Task-Related BOLD fMRI with Amplitude Signatures of Resting-State fMRI 
Blood oxygen contrast-functional magnetic resonance imaging (fMRI) signals are a convolution of neural and vascular components. Several studies indicate that task-related (T-fMRI) or resting-state (R-fMRI) responses linearly relate to hypercapnic task responses. Based on the linearity of R-fMRI and T-fMRI with hypercapnia demonstrated by different groups using different study designs, we hypothesized that R-fMRI and T-fMRI signals are governed by a common physiological mechanism and that resting-state fluctuation of amplitude (RSFA) should be linearly related to T-fMRI responses. We tested this prediction in a group of healthy younger humans where R-fMRI, T-fMRI, and hypercapnic (breath hold, BH) task measures were obtained form the same scan session during resting state and during performance of motor and BH tasks. Within individual subjects, significant linear correlations were observed between motor and BH task responses across voxels. When averaged over the whole brain, the subject-wise correlation between the motor and BH tasks showed a similar linear relationship within the group. Likewise, a significant linear correlation was observed between motor-task activity and RSFA across voxels and subjects. The linear rest–task (R–T) relationship between motor activity and RSFA suggested that R-fMRI and T-fMRI responses are governed by similar physiological mechanisms. A practical use of the R–T relationship is its potential to estimate T-fMRI responses in special populations unable to perform tasks during fMRI scanning. Using the R–T relationship determined from the first group of 12 healthy subjects, we predicted the T-fMRI responses in a second group of 7 healthy subjects. RSFA in both the lower and higher frequency ranges robustly predicted the magnitude of T-fMRI responses at the subject and voxel levels. We propose that T-fMRI responses are reliably predictable to the voxel level in situations where only R-fMRI measures are possible, and may be useful for assessing neural activity in task non-compliant clinical populations.
doi:10.3389/fnsys.2012.00007
PMCID: PMC3294272  PMID: 22408609
breath hold; resting-state fluctuations; BOLD; fMRI; hypercapnia; motor cortex; prediction; vascular
5.  Advances in the application of MRI to amyotrophic lateral sclerosis 
Importance of the field
With the emergence of therapeutic candidates for the incurable and rapidly progressive neurodegenerative condition of amyotrophic lateral sclerosis (ALS), it will be essential to develop easily obtainable biomarkers for diagnosis, as well as monitoring, in a disease where clinical examination remains the predominant diagnostic tool. Magnetic resonance imaging (MRI) has greatly developed over the past thirty years since its initial introduction to neuroscience. With multi-modal applications, MRI is now offering exciting opportunities to develop practical biomarkers in ALS.
Areas covered in this review
The historical application of MRI to the field of ALS, its state-of-the-art and future aspirations will be reviewed. Specifically, the significance and limitations of structural MRI to detect gross morphological tissue changes in relation to clinical presentation will be discussed. The more recent application of diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), functional and resting-state MRI (fMRI & R-fMRI) will be contrasted in relation to these more conventional MRI assessments. Finally, future aspirations will be sketched out in providing a more disease mechanism-based molecular MRI.
What the reader will gain
This review will equip the reader with an overview of the application of MRI to ALS and illustrate its potential to develop biomarkers. This discussion is exemplified by key studies, demonstrating the strengths and limitations of each modality. The reader will gain an expert opinion on both the current and future developments of MR imaging in ALS.
Take home message
MR imaging generates potential diagnostic, prognostic and therapeutic monitoring biomarkers of ALS. The emerging fusion of structural, functional and potentially molecular imaging will improve our understanding of wider cerebral connectivity and holds the promise of biomarkers sensitive to the earliest changes.
doi:10.1517/17530059.2010.536836
PMCID: PMC3080036  PMID: 21516259
6.  Stability of Whole Brain and Regional Network Topology within and between Resting and Cognitive States 
PLoS ONE  2013;8(8):e70275.
Background
Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well.
Methodology/Principal Findings
fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state.
Conclusions/Significance
These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.
doi:10.1371/journal.pone.0070275
PMCID: PMC3734135  PMID: 23940554
7.  Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation 
PLoS Biology  2008;6(12):e315.
Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.
Author Summary
Our understanding of how the brain works relies on the development of neuropsychological models, which describe how brain activity is coordinated among different regions during the execution of a given task. Knowing the directionality of information transfer between connected regions, and in particular distinguishing neural drivers, or the source of forward connections in the brain, from other brain regions, is critical to refine models of the brain. However, whether functional magnetic resonance imaging (fMRI), the most common technique for imaging brain function, allows one to identify neural drivers remains an open question. Here, we used a rat model of absence epilepsy, a form of nonconvulsive epilepsy that occurs during childhood in humans, showing spontaneous spike-and-wave discharges (nonconvulsive seizures) originating from the first somatosensory cortex, to validate several functional connectivity measures derived from fMRI. Standard techniques estimating interactions directly from fMRI data failed because blood flow dynamics varied between regions. However, we were able to identify the neural driver of spike-and-wave discharges when hemodynamic effects were explicitly removed using appropriate modelling. This study thus provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on connectivity in the functional neuroimaging literature.
Neural long-range interactions can be distinguished from hemodynamic confounds in functional magnetic resonance imaging using new data analysis techniques that will allow experimental validation of models of brain function.
doi:10.1371/journal.pbio.0060315
PMCID: PMC2605917  PMID: 19108604
8.  Reduced functional brain connectivity prior to and after disease onset in Huntington's disease☆☆☆ 
NeuroImage : Clinical  2013;2:377-384.
Background
Huntington's disease (HD) is characterised by both regional and generalised neuronal cell loss in the brain. Investigating functional brain connectivity patterns in rest in HD has the potential to broaden the understanding of brain functionality in relation to disease progression. This study aims to establish whether brain connectivity during rest is different in premanifest and manifest HD as compared to controls.
Methods
At the Leiden University Medical Centre study site of the TRACK-HD study, 20 early HD patients (disease stages 1 and 2), 28 premanifest gene carriers and 28 healthy controls underwent 3 T MRI scanning. Standard and high-resolution T1-weighted images and a resting state fMRI scan were acquired. Using FSL, group differences in resting state connectivity were examined for eight networks of interest using a dual regression method. With a voxelwise correction for localised atrophy, group differences in functional connectivity were examined.
Results
Brain connectivity of the left middle frontal and pre-central gyrus, and right post central gyrus with the medial visual network was reduced in premanifest and manifest HD as compared to controls (0.05 > p > 0.0001). In manifest HD connectivity of numerous widespread brain regions with the default mode network and the executive control network were reduced (0.05 > p > 0.0001).
Discussion
Brain regions that show reduced intrinsic functional connectivity are present in premanifest gene carriers and to a much larger extent in manifest HD patients. These differences are present even when the potential influence of atrophy is taken into account. Resting state fMRI could potentially be used for early disease detection in the premanifest phase of HD and for monitoring of disease modifying compounds.
Highlights
•We applied resting state fMRI in premanifest and manifest Huntington's disease.•Reduced functional brain connectivity was present in premanifest HD gene carriers.•Large regions demonstrate reduced functional brain connectivity in manifest HD.•Medial visual, default mode and executive control networks were affected.
doi:10.1016/j.nicl.2013.03.001
PMCID: PMC3778251  PMID: 24179791
Huntington's disease; Resting state fMRI; Premanifest gene carriers; Functional connectivity
9.  Effects of fMRI-EEG Mismatches in Cortical Current Density Estimation Integrating fMRI and EEG: A Simulation Study 
Objective
Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has been studied to achieve high-resolution reconstruction of the spatiotemporal cortical current density (CCD) distribution. However, mismatches between these two imaging modalities may occur due to their different underlying mechanisms. The aim of the present study is to investigate the effects of different types of fMRI-EEG mismatches, including fMRI invisible sources, fMRI extra regions and fMRI displacement, on the fMRI-constrained cortical imaging in a computer simulation based on realistic-geometry boundary-element-method (BEM) model.
Methods
Two methods have been adopted to integrate the synthetic fMRI and EEG data for CCD imaging. In addition to the well-known 90% fMRI-constrained Wiener filter approach (Liu AK, Belliveau JW and Dale AM, PNAS, 95: 8945–8950, 1998), we propose a novel two-step algorithm (referred to as “Twomey algorithm”) for fMRI-EEG integration. In the first step, a “hard” spatial prior derived from fMRI is imposed to solve the EEG inverse problem with a reduced source space; in the second step, the fMRI constraint is removed and the source estimate from the first step is re-entered as the initial guess of the desired solution into an EEG least squares fitting procedure with Twomey regularization. Twomey regularization is a modified Tikhonov technique that attempts to simultaneously minimize the distance between the desired solution and the initial estimate, and the residual errors of fitness to EEG data. The performance of the proposed Twomey algorithm has been evaluated both qualitatively and quantitatively along with the lead-field normalized minimum norm (WMN) and the 90% fMRI-weighted Wiener filter approach, under repeated and randomized source configurations. Point spread function (PSF) and localization error (LE) are used to measure the performance of different imaging approaches with or without a variety of fMRI-EEG mismatches.
Results
The results of the simulation show that the Twomey algorithm can successfully reduce the PSF of fMRI invisible sources compared to the Wiener estimation, without losing the merit of having much lower PSF of fMRI visible sources relative to the WMN solution. In addition, the existence of fMRI extra sources does not significantly affect the accuracy of the fMRI-EEG integrated CCD estimation for both the Wiener filter method and the proposed Twomey algorithm, while the Twomey algorithm may further reduce the chance of occurring spurious sources in the extra fMRI regions. The fMRI displacement away from the electrical source causes enlarged localization error in the imaging results of both the Twomey and Wiener approaches, while Twomey gives smaller LE than Wiener with the fMRI displacement ranging from 1-cm to 2-cm. With less than 2-cm fMRI displacement, the LEs for the Twomey and Wiener approaches are still smaller than in the WMN solution.
Conclusions
The present study suggests that the presence of fMRI invisible sources is the most problematic factor responsible for the error of fMRI-EEG integrated imaging based on the Wiener filter approach, whereas this approach is relatively robust against the fMRI extra regions and small displacement between fMRI activation and electrical current sources. While maintaining the above advantages possessed by the Wiener filter approach, the Twomey algorithm can further effectively alleviate the underestimation of fMRI invisible sources, suppress fMRI spurious sources and improve the robustness against fMRI displacement. Therefore, the Twomey algorithm is expected to improve the reliability of multimodal cortical source imaging against fMRI-EEG mismatches.
Significance
The proposed method promises to provide a useful alternative for multimodal neuroimaging integrating fMRI and EEG.
doi:10.1016/j.clinph.2006.03.031
PMCID: PMC1945186  PMID: 16765085
multimodal neuroimaging; EEG; fMRI; lead-field normalized minimum norm; point spread function; Twomey regularization; Wiener estimation; boundary element method
10.  Acupuncture Modulates Resting State Hippocampal Functional Connectivity in Alzheimer Disease 
PLoS ONE  2014;9(3):e91160.
Our objective is to clarify the effects of acupuncture on hippocampal connectivity in patients with Alzheimer disease (AD) using functional magnetic resonance imaging (fMRI). Twenty-eight right-handed subjects (14 AD patients and 14 healthy elders) participated in this study. Clinical and neuropsychological examinations were performed on all subjects. MRI was performed using a SIEMENS verio 3-Tesla scanner. The fMRI study used a single block experimental design. We first acquired baseline resting state data during the initial 3 minutes and then performed acupuncture stimulation on the Tai chong and He gu acupoints for 3 minutes. Last, we acquired fMRI data for another 10 minutes after the needle was withdrawn. The preprocessing and data analysis were performed using statistical parametric mapping (SPM5) software. Two-sample t-tests were performed using data from the two groups in different states. We found that during the resting state, several frontal and temporal regions showed decreased hippocampal connectivity in AD patients relative to control subjects. During the resting state following acupuncture, AD patients showed increased connectivity in most of these hippocampus related regions compared to the first resting state. In conclusion, we investigated the effect of acupuncture on AD patients by combing fMRI and traditional acupuncture. Our fMRI study confirmed that acupuncture at Tai chong and He gu can enhance the hippocampal connectivity in AD patients.
doi:10.1371/journal.pone.0091160
PMCID: PMC3946345  PMID: 24603951
11.  The Contribution of Resting State Networks to the Study of Cortical Reorganization in MS 
Resting State fMRI (RS-fMRI) represents an emerging and powerful tool to explore brain functional connectivity (FC) changes associated with neurologic disorders. Compared to activation/task-related fMRI, RS-fMRI has the advantages that (i) BOLD fMRI signals are self-generated and independent of subject's performance during the task and (ii) a single dataset is sufficient to extract a set of RS networks (RSNs) that allows to explore whole brain FC. According to these features RS-fMRI appears particularly suitable for the study of FC changes related to multiple sclerosis (MS). In the present review we will first give a brief description of RS-fMRI methodology and then an overview of most relevant studies conducted so far in MS by using this approach. The most interesting results, in particular, regard the default-mode network (DMN), whose FC changes have been correlated with cognitive status of MS patients, and the visual RSN (V-RSN) whose FC changes have been correlated with visual recovery after optic neuritis. The executive control network (ECN), the lateralized frontoparietal network (FPN), and the sensory motor network (SMN) have also been investigated in MS, showing significant FC rearrangements. All together, RS-fMRI studies conducted so far in MS suggest that prominent RS-FC changes can be detected in many RSNs and correlate with clinical and/or structural MRI measures. Future RS-fMRI studies will further clarify the dynamics and clinical impact of RSNs changes in MS.
doi:10.1155/2013/857807
PMCID: PMC3833123  PMID: 24288613
12.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI 
Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for “pipeline” data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for “pipeline” data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.
doi:10.3389/fnsys.2010.00013
PMCID: PMC2889691  PMID: 20577591
data analysis; DPARSF; REST; resting-state fMRI; SPM
13.  Increased resting functional connectivity in spike-wave epilepsy in WAG/Rij rats 
Epilepsia  2013;54(7):1214-1222.
Purpose: Functional magnetic resonance imaging (fMRI)-based resting functional connectivity is well suited for measuring slow correlated activity throughout brain networks. Epilepsy involves chronic changes in normal brain networks, and recent work demonstrated enhanced resting fMRI connectivity between the hemispheres in childhood absence epilepsy. An animal model of this phenomenon would be very valuable for investigating fundamental mechanisms and testing therapeutic interventions. Methods: We used fMRI-based resting functional connectivity for studying brain networks involved in absence epilepsy. Wistar Albino Glaxo rats from Rijswijk (WAG/Rij) exhibit spontaneous episodes of staring and unresponsiveness accompanied by spike-wave discharges (SWD) resembling human absence seizures in behavior and electroencephalography (EEG). Simultaneous EEG–fMRI data in epileptic WAG/Rij rats in comparison to non-epileptic Wistar controls were acquired at 9.4 T. Regions showing cortical fMRI increases during SWDs were used to define reference regions for connectivity analysis to investigate whether chronic seizure activity is associated with changes in network resting functional connectivity. Key findings: We observed high degrees of cortical-cortical correlations in all WAG/Rij rats at rest (when no SWD were present), but not in non-epileptic controls. Strongest connectivity was seen between regions most intensely involved in seizures, mainly in the bilateral somatosensory and adjacent cortices. Group statistics revealed that resting interhemispheric cortical-cortical correlations were significantly higher in WAG/Rij rats compared to non-epileptic controls. Significance: These findings suggest that activity-dependent plasticity may lead to long-term changes in epileptic networks even at rest. The results show a marked difference between the epileptic and non-epileptic animals in cortical-cortical connectivity, indicating that this may be a useful interictal biomarker associated with the epileptic state.
doi:10.1111/epi.12227
PMCID: PMC3703864  PMID: 23815571
Resting functional connectivity; spike-wave seizure; fMRI; cortex; thalamus
14.  Longitudinal Changes of Resting-State Functional Connectivity during Motor Recovery after Stroke 
Background and Purpose
Functional magnetic resonance imaging (fMRI) studies could provide crucial information on the neural mechanisms of motor recovery in stroke patients. Resting-state fMRI is applicable to stroke patients who are not capable of proper performance of the motor task. In this study, we explored neural correlates of motor recovery in stroke patients by investigating longitudinal changes in resting-state functional connectivity of the ipsilesional primary motor cortex (M1).
Methods
A longitudinal observational study using repeated fMRI experiments was conducted in 12 patients with stroke. Resting-state fMRI data were acquired four times over a period of 6 months. Patients participated in the first session of fMRI shortly after onset, and thereafter in subsequent sessions at 1, 3, and 6 months after onset. Resting-state functional connectivity of the ipsilesional M1 was assessed and compared with that of healthy subjects.
Results
Compared with healthy subjects, patients demonstrated higher functional connectivity with the ipsilesional frontal and parietal cortices, bilateral thalamus, and cerebellum. Instead, functional connectivity with the contralesional M1 and occipital cortex were decreased in stroke patients. Functional connectivity between the ipsilesional and contralesional M1 showed the most asymmetry at 1 month after onset to the ipsilesional side. Functional connectivity of the ipsilesional M1 with the contralesional thalamus, supplementary motor area, and middle frontal gyrus at onset was positively correlated with motor recovery at 6 months after stroke.
Conclusions
Resting-state fMRI elicited distinctive but comparable results with previous task-based fMRI, presenting complementary and practical values for use in the study of stroke patients.
doi:10.1161/STROKEAHA.110.596155
PMCID: PMC3589816  PMID: 21441147
Resting-state fMRI; Stroke; Motor recovery; Functional connectivity
15.  Local GABA concentration is related to network-level resting functional connectivity 
eLife  2014;3:e01465.
Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.
DOI: http://dx.doi.org/10.7554/eLife.01465.001
eLife digest
Even when your body is at rest, your brain remains active. Subjects lying in brain scanners without any specific task to perform show coordinated and reproducible patterns of brain activity. Areas of the brain with similar functions, such as those involved in vision or in movement, tend to increase or decrease their activity in sync, and these coordinated patterns are referred to as resting state networks.
The functions of these networks are unclear—they may support introspection, memory recall or planning for the future, or they may help to strengthen newly acquired skills by enabling the brain to replay previous learning episodes. There is evidence that resting state networks are altered in disorders such as Alzheimer’s disease, autism and schizophrenia, but little is known about how these changes arise or what they might mean.
Now, Stagg et al. have used a type of brain scan called magnetic resonance spectroscopy to gain insights into the mechanisms by which one particular network—the resting motor network—is generated. This network consists of areas involved in planning, monitoring and executing movements, and includes the primary motor cortex, which initiates movements by sending instructions to the spinal cord.
The levels of a chemical called GABA—a neurotransmitter molecule that tends to inhibit the activity of nerve cells—were measured in the primary motor cortex of young healthy volunteers as they lay idle in a scanner. GABA levels were negatively correlated with the amount of coordinated activity within the resting motor network. By contrast, no relation was seen between coordinated activity and the levels of the neurotransmitter glutamate, which tends to increase the activity of nerve cells. Furthermore, when a weak electric current was applied through the subjects’ scalp to their primary motor cortex—a technique previously shown to lower levels of GABA in the region—the resting motor network became stronger.
In addition to providing new information on how the rhythmic patterns of activity seen in the resting brain arise, the work of Stagg et al. contributes to the more general effort to understand the complex patterns of connections within the human brain.
DOI: http://dx.doi.org/10.7554/eLife.01465.002
doi:10.7554/eLife.01465
PMCID: PMC3964822  PMID: 24668166
magnetic resonance spectroscopy; GABA; resting state fMRI; human
16.  Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease 
PLoS Computational Biology  2008;4(6):e1000100.
Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.
Author Summary
Alzheimer's disease (AD) is a brain disorder characterized by progressive impairment of episodic memory and other cognitive domains resulting in dementia and, ultimately, death. Functional neuroimaging studies have identified brain regions that show abnormal brain function in AD. Although there is converging evidence about the identity of these regions, it is not clear how this abnormality affects the functional organization of the whole brain. In order to characterize the functional organization of the brain, our approach uses small-world measures, which have also been used to study systems such as social networks and the internet. We use graph analytical methods to compute these measures of functional connectivity brain networks, which are derived from fMRI data obtained from healthy elderly controls and AD patients. The AD patients had significantly lower regional connectivity, and showed disrupted global functional organization, when compared to healthy controls. Moreover, our results indicate that cognitive decline in Alzheimer's disease patients is associated with disrupted functional connectivity in the entire brain. Our findings further suggest that small-world measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.
doi:10.1371/journal.pcbi.1000100
PMCID: PMC2435273  PMID: 18584043
17.  Functional MRI of Mnemonic Networks across the spectrum of Normal Aging, Mild Cognitive Impairment and Alzheimer’s Disease 
Journal of Alzheimer's disease : JAD  2012;31(0 3):S155-S167.
Functional magnetic resonance imaging (fMRI) is a non-invasive technique that has come into common use to examine neural network function in normal and impaired cognitive states. Using this promising type of analysis, researchers have identified the presence of anatomically distributed regions operating as large-scale neural networks, which are observed both during the performance of associative memory tasks and in the resting state. The assembly of these anatomically distinct regions into functional ensembles and their choreographed activation and deactivation sets the stage for complex behaviors such as the formation and retrieval of associative memories. We review progress in the use of task-related and task-free MRI to elucidate the changes in neural activity in normal older individuals, patients with mild cognitive impairment (MCI), and those with Alzheimer’s disease (AD) dementia, focusing on the altered activity of the default mode network (DMN) and medial temporal lobe (MTL). We place task-free fMRI studies into the larger context of more traditional, task-based fMRI studies of human memory, which have firmly established the critical role of the MTL in associative encoding. Lastly, we discuss the data from our group and others that suggests task-free MRI and task-based fMRI may prove useful as non-invasive biomarkers in studying the progression of memory failure over the course of AD.
doi:10.3233/JAD-2012-120730
PMCID: PMC3736339  PMID: 22890098
Alzheimer’s disease; dementia; aging; functional MRI; functional connectivity; default mode network; memory
18.  Functional Brain Imaging 
Executive Summary
Objective
The objective of this analysis is to review a spectrum of functional brain imaging technologies to identify whether there are any imaging modalities that are more effective than others for various brain pathology conditions. This evidence-based analysis reviews magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI) for the diagnosis or surgical management of the following conditions: Alzheimer’s disease (AD), brain tumours, epilepsy, multiple sclerosis (MS), and Parkinson’s disease (PD).
Clinical Need: Target Population and Condition
Alzheimer’s disease is a progressive, degenerative, neurologic condition characterized by cognitive impairment and memory loss. The Canadian Study on Health and Aging estimated that there will be 97,000 incident cases (about 60,000 women) of dementia (including AD) in Canada in 2006.
In Ontario, there will be an estimated 950 new cases and 580 deaths due to brain cancer in 2006. Treatments for brain tumours include surgery and radiation therapy. However, one of the limitations of radiation therapy is that it damages tissue though necrosis and scarring. Computed tomography (CT) and magnetic resonance imaging (MRI) may not distinguish between radiation effects and resistant tissue, creating a potential role for functional brain imaging.
Epilepsy is a chronic disorder that provokes repetitive seizures. In Ontario, the rate of epilepsy is estimated to be 5 cases per 1,000 people. Most people with epilepsy are effectively managed with drug therapy; but about 50% do not respond to drug therapy. Surgical resection of the seizure foci may be considered in these patients, and functional brain imaging may play a role in localizing the seizure foci.
Multiple sclerosis is a progressive, inflammatory, demyelinating disease of the central nervous system (CNS). The cause of MS is unknown; however, it is thought to be due to a combination of etiologies, including genetic and environmental components. The prevalence of MS in Canada is 240 cases per 100,000 people.
Parkinson’s disease is the most prevalent movement disorder; it affects an estimated 100,000 Canadians. Currently, the standard for measuring disease progression is through the use of scales, which are subjective measures of disease progression. Functional brain imaging may provide an objective measure of disease progression, differentiation between parkinsonian syndromes, and response to therapy.
The Technology Being Reviewed
Functional Brain Imaging
Functional brain imaging technologies measure blood flow and metabolism. The results of these tests are often used in conjunction with structural imaging (e.g., MRI or CT). Positron emission tomography and MRS identify abnormalities in brain tissues. The former measures abnormalities through uptake of radiotracers in the brain, while the latter measures chemical shifts in metabolite ratios to identify abnormalities. The potential role of functional MRI (fMRI) is to identify the areas of the brain responsible for language, sensory and motor function (sensorimotor cortex), rather than identifying abnormalities in tissues. Magnetoencephalography measures magnetic fields of the electric currents in the brain, identifying aberrant activity. Magnetoencephalography may have the potential to localize seizure foci and to identify the sensorimotor cortex, visual cortex and auditory cortex.
In terms of regulatory status, MEG and PET are licensed by Health Canada. Both MRS and fMRI use a MRI platform; thus, they do not have a separate licence from Health Canada. The radiotracers used in PET scanning are not licensed by Health Canada for general use but can be used through a Clinical Trials Application.
Review Strategy
The literature published up to September 2006 was searched in the following databases: MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, Cochrane Database of Systematic Reviews, CENTRAL, and International Network of Agencies for Health Technology Assessment (INAHTA). The database search was supplemented with a search of relevant Web sites and a review of the bibliographies of selected papers.
General inclusion criteria were applied to all conditions. Those criteria included the following:
Full reports of systematic reviews, randomized controlled trials (RCTs), cohort-control studies, prospective cohort studies (PCS’), and retrospective studies.
Sample sizes of at least 20 patients (≥ 10 with condition being reviewed).
English-language studies.
Human studies.
Any age.
Studying at least one of the following: fMRI, PET, MRS, or MEG.
Functional brain imaging modality must be compared with a clearly defined reference standard.
Must report at least one of the following outcomes: sensitivity, specificity, accuracy, positive predictive value (PPV), receiver operating characteristic curve, outcome measuring impact on diagnostic testing, treatment, patient health, or cost.
Summary of Findings
There is evidence to indicate that PET can accurately diagnose AD; however, at this time, there is no evidence to suggest that a diagnosis of AD with PET alters the clinical outcomes of patients.
The addition of MRS or O-(2-18F-Fluoroethyl)-L-Tyrosine (FET)-PET to gadolinium (Gd)-enhanced MRI for distinguishing malignant from benign tumours during primary diagnosis may provide a higher specificity than Gd-enhanced MRI alone. The clinical utility of additional imaging in patients to distinguish malignant from benign tumours is unclear, because patients with a suspected brain tumour will likely undergo a biopsy despite additional imaging results.
The addition of MRS, FET-PET, or MRI T2 to Gd-enhanced MRI for the differentiation of recurrence from radiation necrosis may provide a higher specificity than Gd-enhanced MRI alone. The clinical utility of additional imaging in patients with a suspected recurrence is in the monitoring of patients. Based on the evidence available, it is unclear if one of the imaging modalities (MRS, FET-PET, or MRI T2) offers significantly improved specificity over another.
There may be a role for fMRI in the identification of surgical candidates for tumour resection; however, this requires further research.
Based on the studies available, it is unclear if MEG has similar accuracy in localizing seizure foci to intracranial electroencephalogram (ICEEG). More high-quality research is needed to establish whether there is a difference in accuracy between MEG and ICEEG.
The results of the studies comparing PET to noninvasive electroencephalogram (EEG) did not demonstrate that PET was more accurate at localizing seizure foci; however, there may be some specific conditions, such as tuberous sclerosis, where PET may be more accurate than noninvasive EEG.
There may be some clinical utility for MEG or fMRI in presurgical functional mapping; however, this needs further investigation involving comparisons with other modalities. The clinical utility of MRS has yet to be established for patients with epilepsy.
Positron emission tomography has high sensitivity and specificity in the diagnosis of PD and the differential diagnosis of parkinsonian syndromes; however, it is unclear at this time if the addition of PET in the diagnosis of these conditions contributes to the treatment and clinical outcomes of patients.
There is limited clinical utility of functional brain imaging in the management of patients with MS at this time. Diagnosis of MS is established through clinical history, evoked potentials, and MRI. Magnetic resonance imaging can identify the multifocal white lesions and other structural characteristics of MS.
PMCID: PMC3379170  PMID: 23074493
19.  Disrupted modular organization of resting-state cortical functional connectivity in U.S. military personnel following concussive ‘mild’ blast-related traumatic brain injury† 
NeuroImage  2013;84:10.1016/j.neuroimage.2013.08.017.
Blast-related traumatic brain injury (TBI) has been one of the “signature injuries” of the wars in Iraq and Afghanistan. However, neuroimaging studies in concussive ‘mild’ blast-related TBI have been challenging due to the absence of abnormalities in computed tomography or conventional magnetic resonance imaging (MRI) and the heterogeneity of the blast-related injury mechanisms. The goal of this study was to address these challenges utilizing single-subject, module-based graph theoretic analysis of resting-state functional MRI (fMRI) data. We acquired 20 minutes of resting-state fMRI in 63 U.S. military personnel clinically diagnosed with concussive blast-related TBI and 21 U.S. military controls who had blast exposures but no diagnosis of TBI. All subjects underwent an initial scan within 90 days post-injury and 65 subjects underwent a follow-up scan 6 to 12 months later. A second independent cohort of 40 U.S. military personnel with concussive blast-related TBI patients served as a validation dataset. The second independent cohort underwent an initial scan within 30 days post-injury. 75% of scans were of good quality, with exclusions primarily due to excessive subject motion. Network analysis of the subset of these subjects in the first cohort with good quality scans revealed spatially localized reductions in participation coefficient, a measure of between-module connectivity, in the TBI patients relative to the controls at the time of the initial scan. These group differences were less prominent on the follow-up scans. The 15 brain areas with the most prominent reductions in participation coefficient were next used as regions of interest (ROIs) for single-subject analyses. In the first TBI cohort, more subjects than would be expected by chance (27/47 versus 2/47 expected, p < 0.0001) had 3 or more brain regions with abnormally low between-module connectivity relative to the controls on the initial scans. On the follow-up scans, more subjects than expected by chance (5/37, p = 0.044) but fewer subjects than on the initial scans had 3 or more brain regions with abnormally low between-module connectivity. Analysis of the second TBI cohort validation dataset with no free parameters provided a partial replication; again more subjects than expected by chance (8/31, p = 0.006) had 3 or more brain regions with abnormally low between-module connectivity on the initial scans, but the numbers were not significant (2/27, p = 0.276) on the follow-up scans. A single-subject, multivariate analysis by probabilistic principal component analysis of the between-module connectivity in the 15 identified ROIs, showed that 31/47 subjects in the first TBI cohort were found to be abnormal relative to the controls on the initial scans. In the second TBI cohort, 9/31 patients were found to be abnormal in identical multivariate analysis with no free parameters. Again, there were not substantial differences on the follow-up scans. Taken together, these results indicate that single-subject, module-based graph theoretic analysis of resting-state fMRI provides potentially useful information for concussive blast-related TBI if high quality scans can be obtained. The underlying biological mechanisms and consequences of disrupted between-module connectivity are unknown, thus further studies are required.
doi:10.1016/j.neuroimage.2013.08.017
PMCID: PMC3849319  PMID: 23968735
functional connectivity; traumatic brain injury; graph theory; modularity; functional magnetic resonance imaging (fMRI); blast injury
20.  Use of Functional Magnetic Resonance Imaging in the Early Identification of Alzheimer’s Disease 
Neuropsychology review  2007;17(2):127-143.
A growing body of evidence suggests that a preclinical phase of Alzheimer’s disease (AD) exists several years or more prior to the overt manifestation of clinical symptoms and is characterized by subtle neuropsychological and brain changes. Identification of individuals prior to the development of significant clinical symptoms is imperative in order to have the greatest treatment impact by maintaining cognitive abilities and preserving quality of life. Functional magnetic resonance imaging (fMRI) offers considerable promise as a non-invasive tool for detecting early functional brain changes in asymptomatic adults. In fact, evidence to date indicates that functional brain decline precedes structural decline in preclinical samples. Therefore, fMRI may offer the unique ability to capture the dynamic state of change in the degenerating brain. This review examines the clinical utility of blood oxygen level dependent (BOLD) fMRI in those at risk for AD as well as in early AD. We provide an overview of fMRI findings in at-risk groups by virtue of genetic susceptibility or mild cognitive decline followed by an appraisal of the methodological issues concerning the diagnostic usefulness of fMRI in early AD. We conclude with a discussion of future directions and propose that BOLD-fMRI in combination with cerebral blood flow or diffusion techniques will provide a more complete accounting of the neurovascular changes that occur in preclinical AD and thus improve our ability to reliably detect early brain changes prior to disease onset.
doi:10.1007/s11065-007-9025-y
PMCID: PMC2084460  PMID: 17476598
BOLD-fMRI; Preclinical Alzheimer’s disease; APOE ε4; Mild cognitive impairment; Arterial spin labeling; Cerebral blood perfusion
21.  Resting state activity in patients with disorders of consciousness  
Functional Neurology  2011;26(1): 37 - 43 .
Summary
Recent advances in the study of spontaneous brain activity have demonstrated activity patterns that emerge with no task performance or sensory stimulation; these discoveries hold promise for the study of higher-order associative network functionality. Additionally, such advances are argued to be relevant in pathological states, such as disorders of consciousness (DOC), i.e., coma, vegetative and minimally conscious states. Recent studies on resting state activity in DOC, measured with functional magnetic resonance imaging (fMRI) techniques, show that functional connectivity is disrupted in the task-negative or the default mode network. However, the two main approaches employed in the analysis of resting state functional connectivity data (i.e., hypothesis-driven seed-voxel and data-driven independent component analysis) present multiple methodological difficulties, especially in non-collaborative DOC patients. Improvements in motion artifact removal and spatial normalization are needed before fMRI resting state data can be used as proper biomarkers in severe brain injury. However, we anticipate that such developments will boost clinical resting state fMRI studies, allowing for easy and fast acquisitions and ultimately improve the diagnosis and prognosis in the absence of DOC patients’ active collaboration in data acquisition.
PMCID: PMC3814510  PMID: 21693087
coma ;  consciousness ;  default network ;  functional magnetic resonance imaging ;  resting state ;  spontaneous activity
22.  Comprehensive in vivo Mapping of the Human Basal Ganglia and Thalamic Connectome in Individuals Using 7T MRI 
PLoS ONE  2012;7(1):e29153.
Basal ganglia circuits are affected in neurological disorders such as Parkinson's disease (PD), essential tremor, dystonia and Tourette syndrome. Understanding the structural and functional connectivity of these circuits is critical for elucidating the mechanisms of the movement and neuropsychiatric disorders, and is vital for developing new therapeutic strategies such as deep brain stimulation (DBS). Knowledge about the connectivity of the human basal ganglia and thalamus has rapidly evolved over recent years through non-invasive imaging techniques, but has remained incomplete because of insufficient resolution and sensitivity of these techniques. Here, we present an imaging and computational protocol designed to generate a comprehensive in vivo and subject-specific, three-dimensional model of the structure and connections of the human basal ganglia. High-resolution structural and functional magnetic resonance images were acquired with a 7-Tesla magnet. Capitalizing on the enhanced signal-to-noise ratio (SNR) and enriched contrast obtained at high-field MRI, detailed structural and connectivity representations of the human basal ganglia and thalamus were achieved. This unique combination of multiple imaging modalities enabled the in-vivo visualization of the individual human basal ganglia and thalamic nuclei, the reconstruction of seven white-matter pathways and their connectivity probability that, to date, have only been reported in animal studies, histologically, or group-averaged MRI population studies. Also described are subject-specific parcellations of the basal ganglia and thalamus into sub-territories based on their distinct connectivity patterns. These anatomical connectivity findings are supported by functional connectivity data derived from resting-state functional MRI (R-fMRI). This work demonstrates new capabilities for studying basal ganglia circuitry, and opens new avenues of investigation into the movement and neuropsychiatric disorders, in individual human subjects.
doi:10.1371/journal.pone.0029153
PMCID: PMC3250409  PMID: 22235267
23.  Modulation of Temporally Coherent Brain Networks Estimated Using ICA at Rest and During Cognitive Tasks 
Human brain mapping  2008;29(7):828-838.
Brain regions which exhibit temporally coherent fluctuations, have been increasingly studied using functional magnetic resonance imaging (fMRI). Such networks are often identified in the context of an fMRI scan collected during rest (and thus are called “resting state networks”); however, they are also present during (and modulated by) the performance of a cognitive task. In this article, we will refer to such networks as temporally coherent networks (TCNs). Although there is still some debate over the physiological source of these fluctuations, TCNs are being studied in a variety of ways. Recent studies have examined ways TCNs can be used to identify patterns associated with various brain disorders (e.g. schizophrenia, autism or Alzheimer’s disease). Independent component analysis (ICA) is one method being used to identify TCNs. ICA is a data driven approach which is especially useful for decomposing activation during complex cognitive tasks where multiple operations occur simultaneously. In this article we review recent TCN studies with emphasis on those that use ICA. We also present new results showing that TCNs are robust, and can be consistently identified at rest and during performance of a cognitive task in healthy individuals and in patients with schizophrenia. In addition, multiple TCNs show temporal and spatial modulation during the cognitive task versus rest. In summary, TCNs show considerable promise as potential imaging biological markers of brain diseases, though each network needs to be studied in more detail.
doi:10.1002/hbm.20581
PMCID: PMC2649823  PMID: 18438867
fMRI; auditory oddball; independent component analysis; P3; schizophrenia
24.  Effect of Acupuncture in Mild Cognitive Impairment and Alzheimer Disease: A Functional MRI Study 
PLoS ONE  2012;7(8):e42730.
We aim to clarify the mechanisms of acupuncture in treating mild cognitive impairment (MCI) and Alzheimer disease (AD) by using functional magnetic resonance imaging (fMRI). Thirty-six right-handed subjects (8 MCI patients, 14 AD patients, and 14 healthy elders) participated in this study. Clinical and neuropsychological examinations were performed on all the subjects. MRI data acquisition was performed on a SIEMENS verio 3-Tesla scanner. The fMRI study used a single block experimental design. We first acquired the baseline resting state data in the initial 3 minutes; we then acquired the fMRI data during the procession of acupuncture stimulation on the acupoints of Tai chong and Hegu for the following 3 minutes. Last, we acquired fMRI data for another 10 minutes after the needle was withdrawn. The preprocessing and data analysis were performed using the statistical parametric mapping (SPM8) software. Then the two-sample t-tests were performed between each two groups of different states. We found that during the resting state, brain activities in AD and MCI patients were different from those of control subjects. During the acupuncture and the second resting state after acupuncture, when comparing to resting state, there are several regions showing increased or decreased activities in MCI, AD subjects compared to normal subjects. Most of the regions were involved in the temporal lobe and the frontal lobe, which were closely related to the memory and cognition. In conclusion, we investigated the effect of acupuncture in AD and MCI patients by combing fMRI and traditional acupuncture. Our fMRI study confirmed that acupuncture at Tai chong (Liv3) and He gu (LI4) can activate certain cognitive-related regions in AD and MCI patients.
doi:10.1371/journal.pone.0042730
PMCID: PMC3423412  PMID: 22916152
25.  Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations 
Resting-state investigations based on the evaluation of intrinsic low-frequency fluctuations of the BOLD fMRI signal have been extensively utilized to map the structure and dynamics of large-scale functional network organization in humans. In addition to increasing our knowledge of normal brain connectivity, disruptions of the spontaneous hemodynamic fluctuations have been suggested as possible diagnostic indicators of neurological and psychiatric disease states. Though the non-invasive technique has been received with much acclamation, open questions remain regarding the origin, organization, phylogenesis, as well as the basis of disease-related alterations underlying the signal patterns. Experimental work utilizing animal models, including the use of neurophysiological recordings and pharmacological manipulations, therefore, represents a critical component in the understanding and successful application of resting-state analysis, as it affords a range of experimental manipulations not possible in human subjects. In this article, we review recent rodent and non-human primate studies and based on the examination of the homologous brain architecture propose the latter to be the best-suited model for exploring these unresolved resting-state concerns. Ongoing work examining the correspondence of functional and structural connectivity, state-dependency and the neuronal correlates of the hemodynamic oscillations are discussed. We then consider the potential experiments that will allow insight into different brain states and disease-related network disruptions that can extend the clinical applications of resting-state fMRI (RS-fMRI).
doi:10.3389/fnana.2012.00029
PMCID: PMC3405297  PMID: 22855672
resting-state; non-human primate; functional connectivity; macaque; animal model; spontaneous activity; functional MRI (fMRI)

Results 1-25 (1225602)