Altered functional connectivity in early Alzheimer's disease: a resting-state fMRI study

Resting state basal ganglia network in idiopathic generalized epilepsy

The basal ganglia, a brain structure related to motor control, is implicated in the modulation of epileptic discharges generalization in patients with idiopathic generalized epilepsy (IGE). Using group independent component analysis (ICA) on resting-state fMRI data, this study identified a resting state functional network that predominantly consisted of the basal ganglia in both healthy controls and patients with IGE. In order to gain a better understanding of the basal ganglia network(BGN) in IGE patients, we compared the BGN functional connectivity of controls with that of epilepsy patients, either with interictal epileptic discharges (with-discharge period, WDP) or without epileptic discharge (nondischarge period, NDP) while scanning. Compared with controls, functional connectivity of BGN in IGE patients demonstrated significantly more integration within BGN except cerebellum and supplementary motor area (SMA) during both periods. Compared with the NDP group, the increased functional connectivity was found in bilateral caudate nucleus and the putamen, and decreases were observed in the bilateral cerebellum and SMA in WDP group. In accord with the proposal that the basal ganglia modulates epileptic discharge activity, the results showed that the modulation enhanced the integration in BGN of patients, and modulation during WDP was stronger than that during NDP. Furthermore, reduction of functional connectivity in cerebellum and SMA, the abnormality might be further aggravated during WDP, was consistent with the behavioral manifestations with disturbed motor function in IGE. These resting-state fMRI findings in the current study provided evidence confirming the role of the BGN as an important modulator in IGE.

Multimodal Imaging of Alzheimer Pathophysiology in the Brain's Default Mode Network

The spatial correlations between the brain's default mode network (DMN) and the brain regions known to develop pathophysiology in Alzheimer's disease (AD) have recently attracted much attention. In this paper, we compare results of different functional and structural imaging modalities, including MRI and PET, and highlight different patterns of anomalies observed within the DMN. Multitracer PET imaging in subjects with and without dementia has demonstrated that [C-11]PIB- and [F-18]FDDNP-binding patterns in patients with AD overlap within nodes of the brain's default network including the prefrontal, lateral parietal, lateral temporal, and posterior cingulate cortices, with the exception of the medial temporal cortex (especially, the hippocampus) where significant discrepancy between increased [F-18]FDDNP binding and negligible [C-11]PIB-binding was observed. [F-18]FDDNP binding in the medial temporal cortex—a key constituent of the DMN—coincides with both the presence of amyloid and tau pathology, and also with cortical areas with maximal atrophy as demonstrated by T1-weighted MR imaging of AD patients.

Default network correlations analyzed on native surfaces

Disruptions of interregional correlations in the blood oxygenation level dependent fMRI signal have been reported in multiple diseases, including Alzheimer's disease and mild cognitive impairment. "Default network" regions that overlap with areas of earliest amyloid deposition have been highlighted by these reports, and abnormal default network activity is also observed in unimpaired elderly subjects with high amyloid burden. However, one limitation of current methods for analysis of interregional correlations is that they rely on transformation of functional data to an atlas volume (e.g., Talairach-Tournoux or Montreal Neurological Institute atlases) and may not adequately account for anatomic variation between subjects, particularly in the presence of atrophy. We assessed the utility of the FreeSurfer cortical parcellation to analyze default network functional correlations on the native surfaces of individual subjects. Group-level quantitative analysis was accomplished by comparing correlations between equivalent structures in different subjects. The method was applied to resting-state fMRI data from young, healthy subjects; preliminary results were also obtained from cognitively unimpaired elderly subjects and patients with Alzheimer's disease, Parkinson's disease, Parkinson's disease dementia, and dementia with Lewy bodies.

[Functional cerebral activity in a state of rest: connectivity networks]

Functional connectivity can be measured during task-based functional magnetic resonance imaging (fMRI), or in the absence of specific stimuli or tasks. In either case, the study of low frequency fluctuations in the BOLD signal reveals patterns of synchronization which delineate the intrinsic functional architecture of the brain. The scientific community now has available shared resources to accelerate the exploitation of resting state fMRI with the objectives of improving diagnostic methods and leading to better treatments grounded in neuroscience. Fomenting a collaborative scientific culture will accelerate our understanding of the underlying phenonmemna. Recently, the Spanish Resting State Network (SRSN) has joined this collaborative effort by creating a setting to facilitate collaboration among the various neuroscience research groups working in Spanish (http://www.nitrc.org/projects/srsn).

The search for neuroimaging biomarkers of Alzheimer's disease with advanced MRI techniques

The aim of this review is to examine the recent literature on using advanced magnetic resonance imaging (MRI) techniques for finding neuroimaging biomarkers that are sensitive to the detection of risks for Alzheimer's disease (AD). Since structural MRI techniques, such as brain structural volumetry and voxel-based morphometry (VBM), have been widely used for AD studies and extensively reviewed, we will only briefly touch on the topics of volumetry and morphometry. The focus of the current review is about the more recent developments in the search for AD neuroimaging biomarkers with functional MRI (fMRI), resting-state functional connectivity MRI (fcMRI), diffusion tensor imaging (DTI), arterial spin-labeling (ASL), and magnetic resonance spectroscopy (MRS).

Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease

Recent research on Alzheimer's disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration. However, changes in the topological organization of WM structural network in AD remain largely unknown. Here, we used diffusion tensor image tractography to construct the human brain WM networks of 25 AD patients and 30 age- and sex-matched healthy controls, followed by a graph theoretical analysis. We found that both AD patients and controls had a small-world topology in WM network, suggesting an optimal balance between structurally segregated and integrative organization. More important, the AD patients exhibited increased shortest path length and decreased global efficiency in WM network compared with controls, implying abnormal topological organization. Furthermore, we showed that the WM network contained highly connected hub regions that were predominately located in the precuneus, cingulate cortex, and dorsolateral prefrontal cortex, which was consistent with the previous diffusion-MRI studies. Specifically, AD patients were found to have reduced nodal efficiency predominantly located in the frontal regions. Finally, we showed that the alterations of various network properties were significantly correlated with the behavior performances. Together, the present study demonstrated for the first time that the Alzheimer's brain was associated with disrupted topological organization in the large-scale WM structural networks, thus providing the structural evidence for abnormalities of systematic integrity in this disease. This work could also have implications for understanding how the abnormalities of structural connectivity in AD underlie behavioral deficits in the patients.

Changes in thalamus connectivity in mild cognitive impairment: evidence from resting state fMRI

PURPOSE

The subcortical region such as thalamus was believed to have close relationship with many cerebral cortexes which made it especially interesting in the study of functional connectivity. Here, we used resting state functional MRI (fMRI) to examine changes in thalamus connectivity in mild cognitive impairment (MCI), which presented a neuro-disconnection syndrome.

MATERIALS AND METHODS

Data from 14 patients and 14 healthy age-matched controls were analyzed. Thalamus connectivity was investigated by examination of the correlation between low frequency fMRI signal fluctuations in the thalamus and those in all other brain regions.

RESULTS

We found that functional connectivity between the left thalamus and a set of regions was decreased in MCI; these regions are: bilateral cuneus, middle occipital gyrus (MOG), superior frontal gyrus (SFG), medial prefrontal cortex (MPFC), precuneus, inferior frontal gyrus (IFG) and precentral gyrus (PreCG). There are also some regions showed reduced connectivity to right thalamus; these regions are bilateral cuneus, MOG, fusiform gyrus (FG), MPFC, paracentral lobe (PCL), precuneus, superior parietal lobe (SPL) and IFG. We also found increased functional connectivity between the left thalamus and the right thalamus in MCI.

CONCLUSION

The decreased connectivity between the thalamus and the other brain regions might indicate reduced integrity of thalamus-related cortical networks in MCI. Furthermore, the increased connectivity between the left and right thalamus suggest compensation for the loss of cognitive function. Briefly, impairment and compensation of thalamus connectivity coexist in the MCI patients.

Altered default mode network connectivity in Alzheimer's disease--a resting functional MRI and Bayesian network study

A number of functional magnetic resonance imaging (fMRI) studies reported the existence of default mode network (DMN) and its disruption due to the presence of a disease such as Alzheimer's disease (AD). In this investigation, first, we used the independent component analysis (ICA) technique to confirm the DMN difference between patients with AD and normal control (NC) reported in previous studies. Consistent with the previous studies, the decreased resting-state functional connectivity of DMN in AD was identified in posterior cingulated cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal cortex (IPC), inferior temporal cortex (ITC), and hippocampus (HC). Moreover, we introduced Bayesian network (BN) to study the effective connectivity of DMN and the difference between AD and NC. When compared the DMN effective connectivity in AD with the one in NC using a nonparametric random permutation test, we found that connections from left HC to left IPC, left ITC to right HC, right HC to left IPC, to MPFC and to PCC were all lost. In addition, in AD group, the connection directions between right HC and left HC, between left HC and left ITC, and between right IPC and right ITC were opposite to those in NC group. The connections of right HC to other regions, except left HC, within the BN were all statistically in-distinguishable from 0, suggesting an increased right hippocampal pathological and functional burden in AD. The altered effective connectivity in patients with AD may reveal more characteristics of the disease and may serve as a potential biomarker.

Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging

PURPOSE

To use large-scale network (LSN) analysis to classify subjects with Alzheimer disease (AD), those with amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) subjects.

MATERIALS AND METHODS

The study was conducted with institutional review board approval and was in compliance with HIPAA regulations. Written informed consent was obtained from each participant. Resting-state functional magnetic resonance (MR) imaging was used to acquire the voxelwise time series in 55 subjects with clinically diagnosed AD (n = 20), aMCI (n =15), and normal cognitive function (n = 20). The brains were divided into 116 regions of interest (ROIs). The Pearson product moment correlation coefficients of pairwise ROIs were used to classify these subjects. Error estimation of the classifications was performed with the leave-one-out cross-validation method. Linear regression analysis was performed to analyze the relationship between changes in network connectivity strengths and behavioral scores.

RESULTS

The area under the receiver operating characteristic curve (AUC) yielded 87% classification power, 85% sensitivity, and 80% specificity between the AD group and the non-AD group (subjects with aMCI and CN subjects) in the first-step classification. For differentiation between subjects with aMCI and CN subjects, AUC was 95%; sensitivity, 93%; and specificity, 90%. The decreased network indexes were significantly correlated with the Mini-Mental State Examination score in all tested subjects. Similarly, changes in network indexes significantly correlated with Rey Auditory Verbal Leaning Test delayed recall scores in subjects with aMCI and CN subjects.

CONCLUSION

LSN analysis revealed that interconnectivity patterns of brain regions can be used to classify subjects with AD, those with aMCI, and CN subjects. In addition, the altered connectivity networks were significantly correlated with the results of cognitive tests.

Neural correlates of the interactive relationship between memory deficits and depressive symptoms in nondemented elderly: resting fMRI study

Prospective studies have shown an association between depressive symptoms and cognitive impairment among older adults. However, the neural correlates of this relationship are poorly understood. Our aim was to examine whether interactive effects of memory deficits and depressive symptoms are present in the memory-associated functional networks, in nondemented elderly subjects. Fifteen subjects with amnestic mild cognitive impairment (aMCI) and 20 age-matched normal (CN) elderly subjects participated in this cross-sectional study. Resting-state functional connectivity MRI (R-fMRI) measured the hippocampal functional connectivity (HFC) alterations between the two groups. Voxelwise linear regression analysis was performed to correlate hippocampal network strength with the Rey Auditory Verbal Learning Test delayed recall and the Geriatric Depression Scale scores, after adjusting for age and group effects. Poorer memory performance was associated with decreased positively correlated HFC connectivity in the specific frontal lobe and default mode network (DMN) structures. Poorer memory performance also was associated with decreased anticorrelated HFC connectivity in the bilateral inferior parietal and right dorsolateral prefrontal cortices. In contrast, greater depressive symptom severity was associated with increased HFC connectivity in several frontal lobes and DMN regions. Depressive symptoms and memory functions had interactive effects on the HFC, in the frontal, temporal, and PCC structures. Our findings suggest that the R-fMRI technique can be used to examine the changes in functional neural networks where memory deficits and depressive symptoms coexist in the geriatric population.

Functional connectivity in blood oxygenation level-dependent and cerebral blood volume-weighted resting state functional magnetic resonance imaging in the rat brain

PURPOSE

To directly compare functional connectivity and spatiotemporal dynamics acquired with blood oxygenation level-dependent (BOLD) and cerebral blood volume (CBV)-weighted functional magnetic resonance imaging (fMRI) in anesthetized rats.

MATERIALS AND METHODS

A series of BOLD images were acquired in 10 rats followed by CBV-weighted images created by injection of ultrasmall iron oxide particles. Functional connectivity, spectral information, and spatiotemporal dynamics were compared for the BOLD and CBV-weighted resting state scans.

RESULTS

BOLD scans exhibited higher cross-correlation values compared to CBV-weighted scans, but the spatial patterns of correlation were similar. The BOLD spectrum contains power evenly distributed throughout the low-frequency range while the CBV power spectrum exhibited a high power peak localized to approximately 0.2 Hz. Both BOLD and CBV resting state scans showed similar propagating waves of activity along the cortex from the SII toward MI; however, these waves were detected more often in BOLD scans than in CBV scans.

CONCLUSION

While the power spectrum of the CBV signal is different from that of the BOLD signal, both connectivity maps and spatiotemporal dynamics are similar for the two modalities. Further experiments should address the relationship between spontaneous neural activity, local changes in metabolism, and hemodynamic fluctuations to elucidate the origins of the BOLD and CBV signals.

Mapping the Alzheimer's brain with connectomics

Alzheimer's disease (AD) is the most common form of dementia. As an incurable, progressive, and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques [e.g., structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, and EEG/MEG] and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome) in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring.

Brain Effective Connectivity Modeling for Alzheimer's Disease by Sparse Gaussian Bayesian Network

Recent studies have shown that Alzheimer's disease (AD) is related to alteration in brain connectivity networks. One type of connectivity, called effective connectivity, defined as the directional relationship between brain regions, is essential to brain function. However, there have been few studies on modeling the effective connectivity of AD and characterizing its difference from normal controls (NC). In this paper, we investigate the sparse Bayesian Network (BN) for effective connectivity modeling. Specifically, we propose a novel formulation for the structure learning of BNs, which involves one L1-norm penalty term to impose sparsity and another penalty to ensure the learned BN to be a directed acyclic graph - a required property of BNs. We show, through both theoretical analysis and extensive experiments on eleven moderate and large benchmark networks with various sample sizes, that the proposed method has much improved learning accuracy and scalability compared with ten competing algorithms. We apply the proposed method to FDG-PET images of 42 AD and 67 NC subjects, and identify the effective connectivity models for AD and NC, respectively. Our study reveals that the effective connectivity of AD is different from that of NC in many ways, including the global-scale effective connectivity, intra-lobe, interlobe, and inter-hemispheric effective connectivity distributions, as well as the effective connectivity associated with specific brain regions. These findings are consistent with known pathology and clinical progression of AD, and will contribute to AD knowledge discovery.

Altered resting state networks in epileptic patients with generalized tonic-clonic seizures

Recent functional magnetic resonance imaging (fMRI) studies have demonstrated that the resting state networks (RSNs) are impaired in various neuropsychiatric disorders. However, little is known about the changes in the functional architecture of the RSNs in idiopathic generalized epilepsy patients with generalized tonic-clonic seizures (GTCS). Resting state fMRI data were acquired from 16 GTCS patients and 16 healthy subjects. The data were acquired during interictal without combined EEG confirmation. Functional connectivity among the RSNs was examined using individual-based independent component analysis. Six RSNs were identified in both the GTCS and control groups. Compared with the healthy subjects, decreased functional connectivity in the self-referential, somatosensory, visual, and auditory networks, and both the decreased and increased functional connectivities in the classic default-mode and dorsal attention networks were found in the GTCS patients. Furthermore, the present study revealed a negative correlation between the seizure duration and functional connectivity changes in the medial prefrontal cortex in the GTCS patients. These results indicate that there are impairments in the RSNs and possible reorganization of the default-mode network and dorsal attention network in patients with GTCS. Our findings may also suggest that the medial prefrontal cortex and its associated network play a role in the development of GTCS.

Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease

Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.

Understanding the progression of Alzheimer's disease (AD) is essential. We investigated networks of cortical connectivity along a continuum from normal to AD. Mild Cognitive Impairment (MCI) has been implicated as transitional between normal aging and AD. By investigating the characteristics of cortical networks in these three stages (normal, MCI and AD), we found that all three networks exhibited small-world properties. These properties indicate efficient information transfer in the human brain. We also found that the small-world measures of the MCI network were intermediate to those of the normal controls and the patients with AD. This supports the opinion that MCI is a transitional stage between normal aging and AD. Additionally, we found altered interregional correlations in patients with MCI and AD, which may indicate that a compensatory system interacts with cerebral atrophy. The presence of compensatory mechanisms in patients with MCI and AD may enable them to use additional cognitive resources to function on a more nearly normal level. In future, we need to integrate the multi-level network features obtained with various functional and anatomical brain imaging technologies on different scales to understand the pathophysiological mechanism of MCI and AD. We propose brainnetome to represent such integration framework.

Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer's disease: a resting-state functional MRI study

We used resting-state functional MRI to investigate spatial patterns of spontaneous brain activity in 22 healthy elderly subjects, as well as 16 mild cognitive impairment (MCI) and 16 Alzheimer's disease (AD) patients. The pattern of intrinsic brain activity was measured by examining the amplitude of low-frequency fluctuations (ALFF) of blood oxygen level dependent signal during rest. There were widespread ALFF differences among the three groups throughout the frontal, temporal, and parietal cortices. Both AD and MCI patients showed decreased activity mainly in the medial parietal lobe region and lentiform nucleus, while there was increased activity in the lateral temporal regions and superior frontal and parietal regions as compared with controls. Compared with MCI, the AD patients showed decreased activity in the medial prefrontal cortex and increased activity in the superior frontal gyrus and inferior and superior temporal gyri. Specifically, the most significant ALFF differences among the groups appeared in the posterior cingulate cortex, with a reduced pattern of activity when comparing healthy controls, MCI, and AD patients. Additionally, we also showed that the regions with ALFF changes had significant correlations with the cognitive performance of patients as measured by mini-mental state examination scores. Finally, while taking gray matter volume as covariates, the ALFF results were approximately consistent with those without gray matter correction, implying that the functional analysis could not be explained by regional atrophy. Together, our results demonstrate that there is a specific pattern of ALFF in AD and MCI, thus providing insights into biological mechanisms of the diseases.

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.

Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity

Background

Local network connectivity disruptions in Alzheimer's disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data.

Methodology/Principal Findings

18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions.

Conclusions/Significance

We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.

Risky decisions and their consequences: neural processing by boys with Antisocial Substance Disorder

BACKGROUND

Adolescents with conduct and substance problems ("Antisocial Substance Disorder" (ASD)) repeatedly engage in risky antisocial and drug-using behaviors. We hypothesized that, during processing of risky decisions and resulting rewards and punishments, brain activation would differ between abstinent ASD boys and comparison boys.

METHODOLOGY/PRINCIPAL FINDINGS

We compared 20 abstinent adolescent male patients in treatment for ASD with 20 community controls, examining rapid event-related blood-oxygen-level-dependent (BOLD) responses during functional magnetic resonance imaging. In 90 decision trials participants chose to make either a cautious response that earned one cent, or a risky response that would either gain 5 cents or lose 10 cents; odds of losing increased as the game progressed. We also examined those times when subjects experienced wins, or separately losses, from their risky choices. We contrasted decision trials against very similar comparison trials requiring no decisions, using whole-brain BOLD-response analyses of group differences, corrected for multiple comparisons. During decision-making ASD boys showed hypoactivation in numerous brain regions robustly activated by controls, including orbitofrontal and dorsolateral prefrontal cortices, anterior cingulate, basal ganglia, insula, amygdala, hippocampus, and cerebellum. While experiencing wins, ASD boys had significantly less activity than controls in anterior cingulate, temporal regions, and cerebellum, with more activity nowhere. During losses ASD boys had significantly more activity than controls in orbitofrontal cortex, dorsolateral prefrontal cortex, brain stem, and cerebellum, with less activity nowhere.

CONCLUSIONS/SIGNIFICANCE

Adolescent boys with ASD had extensive neural hypoactivity during risky decision-making, coupled with decreased activity during reward and increased activity during loss. These neural patterns may underlie the dangerous, excessive, sustained risk-taking of such boys. The findings suggest that the dysphoria, reward insensitivity, and suppressed neural activity observed among older addicted persons also characterize youths early in the development of substance use disorders.

Understanding higher level gait disturbances in mild dementia in order to improve rehabilitation: 'last in-first out'

Predicting and anticipating disturbances in higher level gait is particularly relevant for patients with dementia as higher level gait appears to be closely related to higher level cognitive functioning. A phenomenon that could contribute to the understanding and prediction of disturbances in higher level gait and gait-related motor activity in the various subtypes of dementia is paraphrased as 'last in-first out'. 'Last in-first out' refers to the principle that neural circuits that mature late in development are the most vulnerable to neurodegeneration. The strength of relating symptoms to the 'last in-first out' principle is that a future symptom can be predicted and anticipated in a therapeutic way, even if the disease process has not already started. Therefore, the aim of this review is to provide new strategies for rehabilitation of higher level gait disturbances in dementia based upon the 'last in-first out' principle. These new strategies emerge from five neural networks: the superior longitudinal fasciculus, the uncinate fasciculus, the fronto-cerebellar and fronto-striatal connections, and the cingulum.

Spontaneous brain activity observed with functional magnetic resonance imaging as a potential biomarker in neuropsychiatric disorders

As functional magnetic resonance imaging (fMRI) studies have yielded increasing amounts of information about the brain's spontaneous activity, they have revealed fMRI's potential to locate changes in brain hemodynamics that are associated with neuropsychiatric disorders. In this paper, we review studies that support the notion that changes in brain spontaneous activity observed by fMRI can be used as potential biomarkers for diagnosis and treatment evaluation in neuropsychiatric disorders. We first review the methods used to study spontaneous activity from the perspectives of (1) the properties of local spontaneous activity, (2) the spatial pattern of spontaneous activity, and (3) the topological properties of brain networks. We also summarize the major findings associated with major neuropsychiatric disorders obtained using these methods. Then we review the pilot studies that have used spontaneous activity to discriminate patients from normal controls. Finally, we discuss current challenges and potential research directions to further elucidate the clinical use of spontaneous brain activity in neuropsychiatric disorders.

Clinical applications of resting state functional connectivity

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm.

Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease

Functional magnetic resonance imaging (fMRI) of default mode network (DMN) brain activity during resting is recently gaining attention as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease. The aim of this study was to determine which method of data processing provides highest diagnostic power and to define metrics to further optimize the diagnostic value. fMRI was acquired in 21 healthy subjects, 17 subjects with mild cognitive impairment and 15 patients with Alzheimer's disease (AD) and data evaluated both with volumes of interest (VOI)-based signal time course evaluations and independent component analyses (ICA). The first approach determines the amount of DMN region interconnectivity (as expressed with correlation coefficients); the second method determines the magnitude of DMN coactivation. Apolipoprotein E (ApoE) genotyping was available in 41 of the subjects examined. Diagnostic power (expressed as accuracy) of data of a single DMN region in independent component analyses was 64%, that of a single correlation of time courses between 2 DMN regions was 71%, respectively. With multivariate analyses combining both methods of analysis and data from various regions, accuracy could be increased to 97% (sensitivity 100%, specificity 95%). In nondemented subjects, no significant differences in activity within DMN could be detected comparing ApoE ε4 allele carriers and ApoE ε4 allele noncarriers. However, there were some indications that fMRI might yield useful information given a larger sample. Time course correlation analyses seem to outperform independent component analyses in the identification of patients with Alzheimer's disease. However, multivariate analyses combining both methods of analysis by considering the activity of various parts of the DMN as well as the interconnectivity between these regions are required to achieve optimal and clinically acceptable diagnostic power.

Network anticorrelations, global regression, and phase-shifted soft tissue correction

Synchronized low-frequency BOLD fluctuations are observed in dissociable large-scale, distributed networks with functional specialization. Two such networks, referred to as the task-positive network (TPN) and the task-negative network (TNN) because they tend to be active or inactive during cognitively demanding tasks, show reproducible anticorrelation of resting BOLD fluctuations after removal of the global brain signal. Because global signal regression mandates that anticorrelated regions to a given seed region must exist, it is unclear whether such anticorrelations are an artifact of global regression or an intrinsic property of neural activity. In this study, we demonstrate from simulated data that spurious anticorrelations are introduced during global regression for any two networks as a linear function of their size. Using actual resting state data, we also show that both the TPN and TNN become anticorrelated with the orbits when soft tissues are included in the global regression algorithm. Finally, we propose a technique using phase-shifted soft tissue regression (PSTCor) that allows improved correction of global physiological artifacts without global regression that shows improved anatomic specificity to global regression but does not show significant network anticorrelations. These results imply that observed anticorrelations between TNN and TPN may be largely or entirely artifactual in the resting state. These results also imply that differences in network anticorrelations attributed to pathophysiological or behavioral states may be due to differences in network size or recruitment rather than actual anticorrelations.

Decoding brain states from fMRI connectivity graphs

Functional connectivity analysis of fMRI data can reveal synchronised activity between anatomically distinct brain regions. Here, we extract the characteristic connectivity signatures of different brain states to perform classification, allowing us to decode the different states based on the functional connectivity patterns. Our approach is based on polythetic decision trees, which combine powerful discriminative ability with interpretability of results. We also propose to use ensemble of classifiers within specific frequency subbands, and show that they bring systematic improvement in classification accuracy. Exploiting multi-band classification of connectivity graphs is also proposed, and we explain theoretical reasons why the technique could bring further improvement in classification performance. The choice of decision trees as classifier is shown to provide a practical way to identify a subset of connections that distinguishes best between the conditions, permitting the extraction of very compact representations for differences between brain states, which we call discriminative graphs. Our experimental results based on strict train/test separation at all stages of processing show that the method is applicable to inter-subject brain decoding with relatively low error rates for the task considered.

Mapping Alzheimer's disease progression in 1309 MRI scans: power estimates for different inter-scan intervals

Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4+/-7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6+/-7.1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (alpha=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.

Altered connectivity among emotion-related brain regions during short-term memory in Alzheimer's disease

A PET study of patients with Alzheimer's disease (AD) engaged in a delayed match-to-sample face recognition task revealed that performance declines as a function of increasing delay, a pattern accompanied by reduced functional connectivity of prefrontal cortex but increased connectivity of the left amygdala. Here, we characterize the changes in interactions within this amygdalar circuit across the memory delays using structural equation modeling. The magnitude of effective connections was found to be much greater in the patients than in the controls, notably from the left amygdala to left inferior prefrontal cortex, which, in turn, influenced its right homologue. The influence from the amygdala to the left hippocampus, in contrast, was not strong in either group. We interpret this pattern of interactions as possibly reflecting the compensatory recruitment of a dynamic neural network, perhaps involved in implicit emotional processing, in the context of a faulty executive maintenance and retrieval system.

Comparison of alpha-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat

Functional connectivity measures based upon low-frequency blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) signal fluctuations have become a widely used tool for investigating spontaneous brain activity in humans. Still unknown, however, is the precise relationship between neural activity, the hemodynamic response and fluctuations in the MRI signal. Recent work from several groups had shown that correlated low-frequency fluctuations in the BOLD signal can be detected in the anesthetized rat - a first step toward elucidating this relationship. Building on this preliminary work, through this study, we demonstrate that functional connectivity observed in the rat depends strongly on the type of anesthesia used. Power spectra of spontaneous fluctuations and the cross-correlation-based connectivity maps from rats anesthetized with alpha-chloralose, medetomidine or isoflurane are presented using a high-temporal-resolution imaging sequence that ensures minimal contamination from physiological noise. The results show less localized correlation in rats anesthetized with isoflurane as compared with rats anesthetized with alpha-chloralose or medetomidine. These experiments highlight the utility of using different types of anesthesia to explore the fundamental physiological relationships of the BOLD signal and suggest that the mechanisms contributing to functional connectivity involve a complicated relationship between changes in neural activity, neurovascular coupling and vascular reactivity.

Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation

Rapid advances in neuroimaging techniques provide great potentials for study of Alzheimer's disease (AD). Existing findings have shown that AD is closely related to alteration in the functional brain network, i.e., the functional connectivity between different brain regions. In this paper, we propose a method based on sparse inverse covariance estimation (SICE) to identify functional brain connectivity networks from PET data. Our method is able to identify both the connectivity network structure and strength for a large number of brain regions with small sample sizes. We apply the proposed method to the PET data of AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Compared with NC, AD shows decrease in the amount of inter-region functional connectivity within the temporal lobe especially between the area around hippocampus and other regions and increase in the amount of connectivity within the frontal lobe as well as between the parietal and occipital lobes. Also, AD shows weaker between-lobe connectivity than within-lobe connectivity and weaker between-hemisphere connectivity, compared with NC. In addition to being a method for knowledge discovery about AD, the proposed SICE method can also be used for classifying new subjects, which makes it a suitable approach for novel connectivity-based AD biomarker identification. Our experiments show that the best sensitivity and specificity our method can achieve in AD vs. NC classification are 88% and 88%, respectively.

Advances and pitfalls in the analysis and interpretation of resting-state FMRI data

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

Resting state networks change in clinically isolated syndrome

Task-functional magnetic resonance imaging studies have shown that early cortical recruitment exists in multiple sclerosis, which can partly explain the discrepancy between conventional magnetic resonance imaging and clinical disability. The study of the brain 'at rest' may provide additional information, because task-induced metabolic changes are relatively small compared to the energy use of the resting brain. We therefore questioned whether functional changes exist at rest in the early phase of multiple sclerosis, and addressed this question by a network analysis of no-task functional magnetic resonance imaging data. Fourteen patients with symptoms suggestive of multiple sclerosis (clinically isolated syndrome), 31 patients with relapsing remitting multiple sclerosis and 41 healthy controls were included. Resting state functional magnetic resonance imaging data were brought to standard space using non-linear registration, and further analysed using multi-subject independent component analysis and individual time-course regression. Eight meaningful resting state networks were identified in our subjects and compared between the three groups with non-parametric permutation testing, using threshold-free cluster enhancement to correct for multiple comparisons. Additionally, quantitative measures of structural damage were obtained. Grey and white matter volumes, normalized for head size, were measured for each subject. White matter integrity was investigated with diffusion tensor measures that were compared between groups voxel-wise using tract-based spatial statistics. Patients with clinically isolated syndrome showed increased synchronization in six of the eight resting state networks, including the default mode network and sensorimotor network, compared to controls or relapsing remitting patients. No significant decreases were found in patients with clinically isolated syndrome. No significant resting state synchronization differences were found between relapsing remitting patients and controls. Normalized grey matter volume was decreased and white matter diffusivity measures were abnormal in relapsing remitting patients compared to controls, whereas no atrophy or diffusivity changes were found for the clinically isolated syndrome group. Thus, early synchronization changes are found in patients with clinically isolated syndrome that are suggestive of cortical reorganization of resting state networks. These changes are lost in patients with relapsing remitting multiple sclerosis with increasing brain damage, indicating that cortical reorganization of resting state networks is an early and finite phenomenon in multiple sclerosis.

Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk

Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with a variety of cognitive and memory impairments and dysfunction, including Alzheimer's disease. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these decompositions in a functional logistic regression model. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found an indication of alternative connectivity in clinically asymptomatic at-risk subjects when compared to controls, which was not significant in the light of multiple comparisons adjustment. The relevant brain network loads primarily on the temporal lobe and overlaps significantly with the olfactory areas and temporal poles.

Functional integrity of thalamocortical circuits differentiates normal aging from mild cognitive impairment

Resonance in thalamocortical networks is critically involved in sculpting oscillatory behavior in large ensembles of neocortical cells. Neocortical oscillations provide critical information about the integrity of thalamocortical circuits and functional connectivity of cortical networks, which seem to be significantly disrupted by the neuronal death and synapse loss characterizing Alzheimer's disease (AD). By applying a novel analysis methodology to overcome volume conduction effects between scalp electroencephalographic (EEG) measurements, we were able to estimate the temporal activation of EEG-alpha sources in the thalamus and parieto-occipital regions of the cortex. We found that synaptic flow underlying the lower alpha band (7.5-10 Hz) was abnormally facilitated in patients with mild cognitive impairment (MCI) as compared to healthy elderly individuals, particularly from thalamus to cortex (approximately 38% higher). In addition, the thalamic generator of lower alpha oscillations was also abnormally activated in patients with MCI. Regarding the upper alpha subdivision (10.1-12.5 Hz), both controls and patients with MCI showed a bidirectional decrease of thalamocortical synaptic transmission, which was age-dependent only in the control group. Altogether, our results suggest that functional dynamics of thalamocortical networks differentiate individuals at high risk of developing AD from healthy elderly subjects, supporting the hypothesis that neurodegeneration mechanisms are active years before the patient is clinically diagnosed with dementia.

Can structure predict function in the human brain?

Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow.

Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization

Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.

Intellectual enrichment is linked to cerebral efficiency in multiple sclerosis: functional magnetic resonance imaging evidence for cognitive reserve

The cognitive reserve hypothesis helps to explain the incomplete relationship between brain disease and cognitive status in people with neurologic diseases, including Alzheimer's; disease and multiple sclerosis. Lifetime intellectual enrichment (estimated with education or vocabulary knowledge) lessens the negative impact of brain disease on cognition, such that people with greater enrichment are able to withstand more severe neuropathology before suffering cognitive impairment or dementia. The current research is the first to investigate directly the relationship between intellectual enrichment and an index of cerebral activity (the blood oxygen level dependent signal) in a neurologic sample. Multiple sclerosis patients completed a vocabulary-based estimate of lifetime intellectual enrichment. Disease severity was estimated with brain atrophy. Cognitive status was measured with the Symbol Digit Modalities Test. Cerebral activity (functional magnetic resonance imaging blood oxygen level dependent signal) and behavioural performance (accuracy, reaction time) were recorded during the visual N-Back working memory task (three levels of demand: 0-, 1-, 2-Back). All patients produced perfect/nearly perfect accuracy during lower demands (0- and 1-Back), and reaction time was unrelated to intellectual enrichment; however, voxelwise partial correlations controlling for brain atrophy revealed strong positive correlations between intellectual enrichment and cerebral activity within the brain's; default network (e.g. anterior and posterior cingulate corticies), indicating that patients with greater enrichment were able to maintain resting state activity during cognitive processing better. In turn, intellectual enrichment was negatively associated with prefrontal recruitment, suggesting that patients with lesser enrichment required more cerebral resources to perform the same cognitive task as patients with greater enrichment. This same pattern of enrichment-related cerebral activity was observed when cognitive demands increased (2-Back), and intellectual enrichment was negatively associated with reaction time. Principle components analysis revealed a single cognitive reserve network across tasks (greater default network, lesser prefrontal recruitment). Expression of this network almost fully mediated the positive relationship between intellectual enrichment and cognitive status (Symbol Digit Modalities Test). Also, expression of this network was positively associated with brain atrophy when controlling for cognitive status, indicating that patients with greater expression of this network can withstand more severe brain disease before exhibiting cognition similar to patients with lesser network expression. Of note, similar functional magnetic resonance imaging research with healthy adults has not found an association between intelligence and cerebral efficiency. The unique relationship between intellectual enrichment and cerebral efficiency in neurologic patients is consistent with the cognitive reserve hypothesis, which does not posit that enrichment leads to gains in neurocognitive functioning per se; rather, enrichment protects against neurocognitive decline secondarily to disease.

Resting-state BOLD networks versus task-associated functional MRI for distinguishing Alzheimer's disease risk groups

To assess the ability of resting-state functional magnetic resonance imaging to distinguish known risk factors for AD, we evaluated 17 cognitively normal individuals with a family history of AD and at least one copy of the apolipoprotein e4 allele compared to 12 individuals who were not carriers of the APOE4 gene and did not have a family history of AD. Blood oxygen level dependent fMRI was performed evaluating encoding-associated signal and resting-state default mode network signal differences between the two risk groups. Neurocognitive testing revealed that the high risk group performed worse on category fluency testing, but the groups were equivalent on all other cognitive measures. During encoding of novel face-name pairs, there were no regions of encoding-associated BOLD activations that were different in the high risk group. Encoding-associated deactivations were greater in magnitude in the low risk group in the medial and right lateral parietal cortex, similar to findings in AD studies. The resting-state DMN analysis demonstrated nine regions in the prefrontal, orbital frontal, temporal and parietal lobes that distinguished the two risk groups. Resting-state DMN analysis could distinguish risk groups with an effect size of 3.35, compared to an effect size of 1.39 using encoding-associated fMRI techniques. Imaging of the resting state avoids performance related variability seen in activation fMRI, is less complicated to acquire and standardize, does not require radio-isotopes, and may be more effective at identifying functional pathology associated with AD risk compared to non-resting fMRI techniques.

Abnormal resting-state functional connectivity of posterior cingulate cortex in amnestic type mild cognitive impairment

Amnestic type mild cognitive impairment (aMCI) refers to a transitional stage between normal aging and Alzheimer's disease (AD). Whilst posterior cingulate cortex (PCC) is believed to have a key role in a default network and be involved in the pathophysiology of AD, few studies have investigated whole-brain functional connectivity of PCC during resting state, or investigated the relationship between abnormal functional connectivity and disturbance in cognitive function in aMCI patients. Resting-state functional connectivity analysis was used to examine the temporal correlation between PCC and whole-brain regions in 30 aMCI patients and 26 well-matched controls. Further analysis involved evaluation of possible relationships between functional connectivity measures and cognitive function. Regions of decreased functional connectivity were identified in aMCI patients, most notably in temporal cortex, whilst significantly increased functional connectivity was mainly in frontal cortex. In addition, functional connectivity of PCC and temporal cortex was associated with the performance of neuropsychological tests in aMCI patients. This would be consonant with the recruitment of compensatory mechanisms and the process of offset functional impairments appearing as neuropathologic develops, and resting-state connectivity disturbance of PCC-temporal cortex may be a central role in cognitive deficit in aMCI patients.

Addiction related alteration in resting-state brain connectivity

It is widely accepted that addictive drug use is related to abnormal functional organization in the user's brain. The present study aimed to identify this type of abnormality within the brain networks implicated in addiction by resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI). With fMRI data acquired during resting state from 14 chronic heroin users (12 of whom were being treated with methadone) and 13 non-addicted controls, we investigated the addiction related alteration in functional connectivity between the regions in the circuits implicated in addiction with seed-based correlation analysis. Compared with controls, chronic heroin users showed increased functional connectivity between nucleus accumbens and ventral/rostral anterior cingulate cortex (ACC), between nucleus accumbens and orbital frontal cortex (OFC), and between amygdala and OFC and reduced functional connectivity between prefrontal cortex and OFC and between prefrontal cortex and ACC. These observations of altered resting-state functional connectivity suggested abnormal functional organization in the addicted brain and may provide additional evidence supporting the theory of addiction that emphasizes enhanced salience value of a drug and its related cues but weakened cognitive control in the addictive state.

Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer's disease

A sensitive marker for monitoring progression of early Alzheimer's disease would help to develop and test new therapeutic strategies. The present study is aimed at investigating brain metabolism changes over time, as a potential monitoring marker, in patients with amnestic mild cognitive impairment, according to their clinical outcome (converters or non-converters), and in relation to their cognitive decline. Seventeen amnestic mild cognitive impairment patients underwent magnetic resonance imaging and 18FDG-positron emission tomography scans both at inclusion and 18 months later. Baseline and follow-up positron emission tomography data were corrected for partial volume effects and spatially normalized using magnetic resonance imaging data, scaled to the vermis and compared using SPM2. 'PET-PAC' maps reflecting metabolic per cent annual changes were created for correlation analyses with cognitive decline. In the whole sample, the greatest metabolic decrease concerned the posterior cingulate-precuneus area. Converters had significantly greater metabolic decrease than non-converters in two ventro-medial prefrontal areas, the subgenual (BA25) and anterior cingulate (BA24/32). PET-PAC in BA25 and BA24/32 combined allowed complete between-group discrimination. BA25 PET-PAC significantly correlated with both cognitive decline and PET-PAC in the hippocampal region and temporal pole, while BA24/32 PET-PAC correlated with posterior cingulate PET-PAC. Finally, the metabolic change in BA8/9/10 was inversely related to that in BA25 and showed relative increase with cognitive decline, suggesting that compensatory processes may occur in this dorso-medial prefrontal region. The observed ventro-medial prefrontal disruption is likely to reflect disconnection from the hippocampus, both indirectly through the cingulum bundle and posterior cingulate cortex for BA24/32, and directly through the uncinate fasciculus for BA25. Altogether, our findings emphasize the potential of 18FDG-positron emission tomography for monitoring early Alzheimer's disease progression.

Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load

Background

Recent functional MRI (fMRI) studies have demonstrated that there is an intrinsically organized default mode network (DMN) in the resting brain, primarily made up of the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC). Several previous studies have found that the DMN is minimally disturbed during different resting-state conditions with limited cognitive demand. However, this conclusion was drawn from the visual inspection of the functional connectivity patterns within the DMN and no statistical comparison was performed.

Methodology/Principal Findings

Four resting-state fMRI sessions were acquired: 1) eyes-closed (EC) (used to generate the DMN mask); 2) EC; 3) eyes-open with no fixation (EO); and 4) eyes-open with a fixation (EO-F). The 2–4 sessions were counterbalanced across participants (n = 20, 10 males). We examined the statistical differences in both functional connectivity and regional amplitude of low frequency fluctuation (ALFF) within the DMN among the 2–4 resting-state conditions (i.e., EC, EO, and EO-F). Although the connectivity patterns of the DMN were visually similar across these three different conditions, we observed significantly higher functional connectivity and ALFF in both the EO and the EO-F conditions as compared to the EC condition. In addition, the first and second resting EC conditions showed significant differences within the DMN, suggesting an order effect on the DMN activity.

Conclusions/Significance

Our findings of the higher DMN connectivity and regional spontaneous activities in the resting state with the eyes open suggest that the participants might have more non-specific or non-goal-directed visual information gathering and evaluation, and mind wandering or daydreaming during the resting state with the eyes open as compared to that with the eyes closed, thus providing insights into the understanding of unconstrained mental activity within the DMN. Our results also suggest that it should be cautious when choosing the type of a resting condition and designating the order of the resting condition in multiple scanning sessions in experimental design.

Neuronal networks in Alzheimer's disease

Alzheimer's disease (AD) is a progressive, neurodegenerative disease that can be clinically characterized by impaired memory and many other cognitive functions. Previous studies have demonstrated that the impairment is accompanied by not only regional brain abnormalities but also changes in neuronal connectivity between anatomically distinct brain regions. Specifically, using neurophysiological and neuroimaging techniques as well as advanced graph theory-based computational approaches, several recent studies have suggested that AD patients have disruptive neuronal integrity in large-scale structural and functional brain systems underlying high-level cognition, as demonstrated by a loss of small-world network characteristics. Small world is an attractive model for the description of complex brain networks because it can support both segregated and integrated information processing. The altered small-world organization thus reflects aberrant neuronal connectivity in the AD brain that is most likely to explain cognitive deficits caused by this disease. In this review, we will summarize recent advances in the brain network research on AD, focusing mainly on the large-scale structural and functional descriptions. The literature reviewed here suggests that AD patients are associated with integrative abnormalities in the distributed neuronal networks, which could provide new insights into the disease mechanism in AD and help us to uncover an imaging-based biomarker for the diagnosis and monitoring of the disease.

Effects of model-based physiological noise correction on default mode network anti-correlations and correlations

Previous studies have reported that the spontaneous, resting-state time course of the default-mode network is negatively correlated with that of the "task-positive network", a collection of regions commonly recruited in demanding cognitive tasks. However, all studies of negative correlations between the default-mode and task-positive networks have employed some form of normalization or regression of the whole-brain average signal ("global signal"); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the task-positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and positive correlations with the default-mode network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162-167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode network and regions of the task-positive network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within task-positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in positive correlations within the default-mode network, reducing apparent false positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.

Criteria for the neuropathological diagnosis of dementing disorders: routes out of the swamp?

There are several consensus criteria for both the clinical and neuropathological diagnosis of different types of dementias. The clinical diagnostic accuracy using revised research criteria and newly developed biomarkers (MRI, PET, CSF analysis, genetic markers) ranges from 65 to 96% (for Alzheimer disease) with a specificity of diagnostic criteria versus other dementias of 23-88%. Neuropathological assessment of dementing disorders using immunohistochemistry, molecular biologic and genetic methods can achieve a diagnosis/classification, based on the homogeneous definitions, harmonized inter-laboratory methods and standards for the assessment of nervous system lesions, in about 99%, without, however, being able to clarify the causes/etiology of most of these disorders. Further prospective and concerted clinicopathological studies using revised methodological and validated protocols and uniform techniques are required to establish the nature, distribution pattern and grades of lesions and; thus, to overcome the limitations of the current diagnostic framework. By data fusion this my allow their more uniform application and correlation with the clinical data in order to approach a diagnostic "gold standard", and to create generally accepted criteria for differentiating cognitive disorders from healthy brain aging. The detection of disease-specific pathologies will be indispensable to determinate the efficacy of new therapy options.