Objective

The Trail making test (TMT) is culture-loaded because of reliance on the Latin alphabet, limiting its application in Eastern populations. The Shape Trail Test (STT) has been developed as a new variant. This study is to examine the applicability of the STT in a senile Chinese population and to evaluate its potential advantages and disadvantages.

Method

A total of 2470 participants were recruited, including 1151 cognitively normal control (NC), 898 amnestic mild cognitive impairment (aMCI), and 421 mild Alzheimer disease (AD) patients. Besides the STT, the Mini mental state examination and a comprehensive neuropsychological battery involving memory, language, attention, executive function and visuospatial ability were administered to all the participants. In a subgroup of 100 NC and 50 AD patients, both the STT and the Color Trail Test (CTT) were performed.

Results

In NC, the time consumed for Part A and B (STT-A and STT-B) significantly correlated with age and negatively correlated with education (p<0.01). STT-A and B significantly differed among the AD, aMCI and NC. The number that successfully connected within one minute in Part B (STT-B-1 min) correlated well with STT-B (r = 0.71, p<0.01) and distinguished well among NC, aMCI and AD. In the receiver operating characteristic curve analysis, the AUCs (area under the curve) for STT-A, STT-B, and STT-B-1min in identifying AD were 0.698, 0.694 and 0.709, respectively. The STT correlated with the CTT, but the time for completion was longer.

Conclusion

The TMT is a sensitive test of visual search and sequencing. The STT is a meaningful attempt to develop a “culture-fair” variant of the TMT in addition to the CTT.

Introduction

We aimed at dissociating the neural correlates of memory disorders in Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD).

Methods

We included patients with AD (n = 19, 11 female, mean age 61 years) and FTLD (n = 11, 5 female, mean age 61 years) in early stages of their diseases. Memory performance was assessed by means of verbal and visual memory subtests from the Wechsler Memory Scale (WMS-R), including forgetting rates. Brain glucose utilization was measured by [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and brain atrophy by voxel-based morphometry (VBM) of T1-weighted magnetic resonance imaging (MRI) scans. Using a whole brain approach, correlations between test performance and imaging data were computed separately in each dementia group, including a group of control subjects (n = 13, 6 female, mean age 54 years) in both analyses. The three groups did not differ with respect to education and gender.

Results

Patients in both dementia groups generally performed worse than controls, but AD and FTLD patients did not differ from each other in any of the test parameters. However, memory performance was associated with different brain regions in the patient groups, with respect to both hypometabolism and atrophy: Whereas in AD patients test performance was mainly correlated with changes in the parieto-mesial cortex, performance in FTLD patients was correlated with changes in frontal cortical as well as subcortical regions. There were practically no overlapping regions associated with memory disorders in AD and FTLD as revealed by a conjunction analysis.

Conclusion

Memory test performance may not distinguish between both dementia syndromes. In clinical practice, this may lead to misdiagnosis of FTLD patients with poor memory performance. Nevertheless, memory problems are associated with almost completely different neural correlates in both dementia syndromes. Obviously, memory functions are carried out by distributed networks which break down in brain degeneration.

Purpose

To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers.

Methods

Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1–42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later.

Results

The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician’s prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037).

Conclusion

With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria.

Classification studies are widely applied, e.g. in biomedical research to classify objects/patients into predefined groups. The goal is to find a classification function/rule which assigns each object/patient to a unique group with the greatest possible accuracy (classification error). Especially in gene expression experiments often a lot of variables (genes) are measured for only few objects/patients. A suitable approach is the well-known method PLS-DA, which searches for a transformation to a lower dimensional space. Resulting new components are linear combinations of the original variables. An advancement of PLS-DA leads to PPLS-DA, introducing a so called ‘power parameter’, which is maximized towards the correlation between the components and the group-membership. We introduce an extension of PPLS-DA for optimizing this power parameter towards the final aim, namely towards a minimal classification error. We compare this new extension with the original PPLS-DA and also with the ordinary PLS-DA using simulated and experimental datasets. For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA. A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error. On the contrary, for the data set with strong between-feature collinearity and a low proportion of differentially expressed genes and a large total number of genes, the prediction error of PPLS-DA and the extensions is clearly lower than for PLS-DA. Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.

The cerebrospinal fluid (CSF) levels of the proapoptotic kinase R (PKR) and its phosphorylated PKR (pPKR) are increased in Alzheimer’s disease (AD), but whether CSF PKR concentrations are associated with cognitive decline in AD patients remain unknown. In this study, 41 consecutive patients with AD and 11 patients with amnestic mild cognitive impairment (aMCI) from our Memory Clinic were included. A lumbar puncture was performed during the following month of the clinical diagnosis and Mini-Mental State Examination (MMSE) evaluations were repeated every 6 months during a mean follow-up of 2 years. In AD patients, linear mixed models adjusted for age and sex were used to assess the cross-sectional and longitudinal associations between MMSE scores and baseline CSF levels of Aβ peptide (Aβ 1-42), Tau, phosphorylated Tau (p-Tau 181), PKR and pPKR. The mean (SD) MMSE at baseline was 20.5 (6.1) and MMSE scores declined over the follow-up (-0.12 point/month, standard error [SE] = 0.03). A lower MMSE at baseline was associated with lower levels of CSF Aβ 1–42 and p-Tau 181/Tau ratio. pPKR level was associated with longitudinal MMSE changes over the follow-up, higher pPKR levels being related with an exacerbated cognitive deterioration. Other CSF biomarkers were not associated with MMSE changes over time. In aMCI patients, mean CSF biomarker levels were not different in patients who converted to AD from those who did not convert.These results suggest that at the time of AD diagnosis, a higher level of CSF pPKR can predict a faster rate of cognitive decline.

Positron emission tomography (PET) is an important imaging modality in both clinical usage and research studies. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads, which produce more than 224 million lines of response and thus request dramatic computational demands. In this work, we employed a state-of-the-art graphics processing unit (GPU), NVIDIA Tesla C2070, to yield an efficient reconstruction process. Our approaches ingeniously integrate the distinguished features of the symmetry properties of the imaging system and GPU architectures, including block/warp/thread assignments and effective memory usage, to accelerate the computations for ordered subset expectation maximization (OSEM) image reconstruction. The OSEM reconstruction algorithms were implemented employing both CPU-based and GPU-based codes, and their computational performance was quantitatively analyzed and compared. The results showed that the GPU-accelerated scheme can drastically reduce the reconstruction time and thus can largely expand the applicability of the dual-head PET system.

Background

MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods.

Objective

To compare FTD with Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy.

Methods

Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups.

Results

We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55).

Conclusion

Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.

Summary

Background

Fibrillar amyloid-β (Aβ) is thought to begin accumulating in the brain many years before the onset of clinical impairment in patients with Alzheimer’s disease. By assessing the accumulation of Aβ in people at risk of genetic forms of Alzheimer’s disease, we can identify how early preclinical changes start in individuals certain to develop dementia later in life. We sought to characterise the age-related accumulation of Aβ deposition in presenilin 1 (PSEN1) E280A mutation carriers across the spectrum of preclinical disease.

Methods

Between Aug 1 and Dec 6, 2011, members of the familial Alzheimer’s disease Colombian kindred aged 18–60 years were recruited from the Alzheimer’s Prevention Initiative’s registry at the University of Antioquia, Medellín, Colombia. Cross-sectional assessment using florbetapir PET was done in symptomatic mutation carriers with mild cognitive impairment or mild dementia, asymptomatic carriers, and asymptomatic non-carriers. These assessments were done at the Banner Alzheimer’s Institute in Phoenix, AZ, USA. A cortical grey matter mask consisting of six predefined regions. was used to measure mean cortical florbetapir PET binding. Cortical-to-pontine standard-uptake value ratios were used to characterise the cross-sectional accumulation of fibrillar Aβ deposition in carriers and non-carriers with regression analysis and to estimate the trajectories of fibrillar Aβ deposition.

Findings

We enrolled a cohort of 11 symptomatic individuals, 19 presymptomatic mutation carriers, and 20 asymptomatic non-carriers, ranging in age from 20 to 56 years. There was greater florbetapir binding in asymptomatic PSEN1 E280A mutation carriers than in age matched non-carriers. Fibrillar Aβ began to accumulate in PSEN 1E280A mutation carriers at a mean age of 28·2 years (95% CI 27·3–33·4), about 16 years and 21 years before the predicted median ages at mild cognitive impairment and dementia onset, respectively. 18F florbetapir binding rose steeply over the next 9·4 years and plateaued at a mean age of 37·6 years (95% CI 35·3–40·2), about 6 and 11 years before the expected respective median ages at mild cognitive impairment and dementia onset. Prominent florbetapir binding was seen in the anterior and posterior cingulate, precuneus, and parietotemporal and frontal grey matter, as well as in the basal ganglia. Binding in the basal ganglia was not seen earlier or more prominently than in other regions.

Interpretation

These findings contribute to the understanding of preclinical familial Alzheimer’s disease and help set the stage for assessment of amyloid-modifying treatments in the prevention of familial Alzheimer’s disease.

Funding

Avid Radiopharmaceuticals, Banner Alzheimer’s Foundation, Nomis Foundation, Anonymous Foundation, Forget Me Not Initiative, Colciencias, National Institute on Aging, and the State of Arizona.

Background

Gastric carcinoma and primary gastric lymphoma (PGL) are the two most common malignancies in stomach. The purpose of this study was to screen and validate a biomarker of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for distinguishing advanced gastric carcinoma (AGC) from PGL for clinical applications.

Methodology/Principal Findings

We reviewed PET/CT scans collected from January 2008 to April 2012 of 69 AGC and 38 PGL (14 low-grade mucosa-associated lymphoid tissue [MALT], 24 non-MALT aggressive non-Hodgkin lymphoma [ANHL]) with a focus on FDG intensity (maximum standardized uptake value [SUVmax]) of primary lesions and its CT-detected abnormalities, including maximal gastrointestinal wall thickness (THKmax) and mucosal ulcerations. Gastric FDG uptake was found in 69 (100%) patients with AGC and 36 (95%, 12 MALT vs. 24 ANHL)with PGL. The presence of CT-detected abnormalities of AGC and PGL were 97% (67/69) and 89% (12 MALT vs. 22 ANHL), respectively. After controlling for THKmax, SUVmax was higher with ANHL than AGC (17.10±8.08 vs. 9.65±5.24, p<0.05) and MALT (6.20±3.60, p<0.05). THKmax did not differ among MALT, ANHL and AGC. Mucosal ulceration was more common with AGC (n = 9) than PGL (n = 2),but the difference was not statistically significant (p>0.05). Cross-validation analysis showed that for distinguishing ANHL from AGC, the classifier with SUVmax as a feature achieved a correct classification rate of 81% with thresholds 13.40±1.12 and the classifier with SUVmax/THKmax as a feature achieved a correct classification rate of 83% with thresholds 7.51±0.63.

Conclusions/Significance

SUVmax/THKmax may be as a promising biomarker of FDG-PET/CT for distinguishing ANHL from AGC. Structural CT abnormalities alone may not be reliable but can help with PET assessment of gastric malignancies. 18F-FDG PET/CT have potential for distinguishing AGC from PGL at the individual level.

Dynamic connectivity networks identify directed interregional interactions between modeled brain regions in neuroimaging. However, problems arise when the regions involved in a task and their interconnections are not fully known a priori. Objective measures of model adequacy are necessary to validate such models. We present a connectivity formalism, the Switching Linear Dynamic System (SLDS), that is capable of identifying both Granger-Geweke and instantaneous connectivity that vary according to experimental conditions. SLDS explicitly models the task condition as a Markov random variable. The series of task conditions can be estimated from new data given an identified model providing a means to validate connectivity patterns. We use SLDS to model functional magnetic resonance imaging data from five regions during a finger alternation task. Using interregional connectivity alone, the identified model predicted the task condition vector from a different subject with a different task ordering with high accuracy. In addition, important regions excluded from a model can be identified by augmenting the model state space. A motor task model excluding primary motor cortices was augmented with a new neural state constrained by its connectivity with the included regions. The augmented variable time series, convolved with a hemodynamic kernel, was compared to all brain voxels. The right primary motor cortex was identified as the best region to add to the model. Our results suggest that the SLDS model framework is an effective means to address several problems with modeling connectivity including measuring overall model adequacy and identifying important regions missing from models.

Top-down attention to spatial and temporal cues has been thoroughly studied in the visual domain. However, because the neural systems that are important for auditory top-down temporal attention (i.e., attention based on time interval cues) remain undefined, the differences in brain activity between directed attention to auditory spatial location (compared with time intervals) are unclear. Using fMRI (magnetic resonance imaging), we measured the activations caused by cue-target paradigms by inducing the visual cueing of attention to an auditory target within a spatial or temporal domain. Imaging results showed that the dorsal frontoparietal network (dFPN), which consists of the bilateral intraparietal sulcus and the frontal eye field, responded to spatial orienting of attention, but activity was absent in the bilateral frontal eye field (FEF) during temporal orienting of attention. Furthermore, the fMRI results indicated that activity in the right ventrolateral prefrontal cortex (VLPFC) was significantly stronger during spatial orienting of attention than during temporal orienting of attention, while the DLPFC showed no significant differences between the two processes. We conclude that the bilateral dFPN and the right VLPFC contribute to auditory spatial orienting of attention. Furthermore, specific activations related to temporal cognition were confirmed within the superior occipital gyrus, tegmentum, motor area, thalamus and putamen.

Tract-based spatial statistics (TBSS) is a popular method for the analysis of diffusion tensor imaging data. TBSS focuses on differences in white matter voxels with high fractional anisotropy (FA), representing the major fibre tracts, through registering all subjects to a common reference and the creation of a FA skeleton. This work considers the effect of choice of reference in the TBSS pipeline, which can be a standard template, an individual subject from the study, a study-specific template or a group-wise average. While TBSS attempts to overcome registration error by searching the neighbourhood perpendicular to the FA skeleton for the voxel with maximum FA, this projection step may not compensate for large registration errors that might occur in the presence of pathology such as atrophy in neurodegenerative diseases. This makes registration performance and choice of reference an important issue. Substantial work in the field of computational anatomy has shown the use of group-wise averages to reduce biases while avoiding the arbitrary selection of a single individual. Here, we demonstrate the impact of the choice of reference on: (a) specificity (b) sensitivity in a simulation study and (c) a real-world comparison of Alzheimer's disease patients to controls. In (a) and (b), simulated deformations and decreases in FA were applied to control subjects to simulate changes of shape and WM integrity similar to what would be seen in AD patients, in order to provide a “ground truth” for evaluating the various methods of TBSS reference. Using a group-wise average atlas as the reference outperformed other references in the TBSS pipeline in all evaluations.

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 current investigation, firstly, 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 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. Compared the DMN effective connectivity in AD to the one in NC using a non-parametric 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.

Background

Down syndrome appears to be associated with a virtually certain risk of fibrillar amyloid-β (Aβ) pathology by the age of 40 and a very high risk of dementia at older ages. The positron emission tomography (PET) ligand florbetapir F18 has been shown to characterize fibrillar Aβ in the living human brain and to provide a close correlation with subsequent Aβ neuropathology in individuals proximate to and after the end of life. The extent to which the most frequently used PET ligands can be used to detect fibrillar Aβ in patients with Down syndrome remains to be determined.

Objectives

To characterize PET estimates of fibrillar Aβ burden in a Down syndrome patient very close to the end of life and to compare them with neuropathologic assessment made after his death.

Design/Methods

With the family’s informed consent, florbetapir PET was used to study a 55-year-old Down syndrome patient with Alzheimer disease near the end of life; his brain was donated for neuropathologic assessment when he died 14 days later. Visual ratings of cerebral florbetapir uptake were performed by trained readers who were masked to the patient’s diagnosis as part of a larger study, and an automated algorithm was used to characterize regional-to-cerebellar standard uptake value ratios in 6 cerebral regions of interest. Neuropathologic assessments were performed masked to the patient’s diagnosis or PET measurements.

Results

Visual ratings and automated analyses of the PET image revealed a heavy fibrillar Aβ burden in cortical, striatal, and thalamic regions, similar to that reported for patients with late-onset Alzheimer disease. This matched neuropathologic findings of frequent neuritic and diffuse plaques, as well as frequent amyloid angiopathy, except for neuropathologically demonstrated frequent cerebellar diffuse plaques and amyloid angiopathy that were not detected by the PET scan.

Conclusions

Florbetapir PET can be used to detect increased cerebral-to-cerebellar fibrillar Aβ burden in a Down syndrome patient with Alzheimer disease, even in the presence of frequent amyloid angiopathy and diffuse plaques in the cerebellum. Additional studies are needed to determine the extent to which PET could be used to detect and to track fibrillar Aβ and to evaluate investigational Aβ-modifying treatments in the presymptomatic and symptomatic stages of Alzheimer disease.

Background

Alzheimer’s disease (AD) dementia is a consequence of heterogeneous and complex interactions of age-related neurodegeneration and vascular-associated pathologies. Evidence has accumulated that there is increased atherosclerosis/arteriosclerosis of the intracranial arteries in AD and that this may be additive or synergistic with respect to the generation of hypoxia/ischemia and cognitive dysfunction. The effectiveness of pharmacologic therapies and lifestyle modification in reducing cardiovascular disease has prompted a reconsideration of the roles that cardiovascular disease and cerebrovascular function play in the pathogenesis of dementia.

Methods

Using two-dimensional phase-contrast magnetic resonance imaging, we quantified cerebral blood flow within the internal carotid, basilar, and middle cerebral arteries in a group of individuals with mild to moderate AD (n = 8) and compared the results with those from a group of age-matched nondemented control (NDC) subjects (n = 9). Clinical and psychometric testing was performed on all individuals, as well as obtaining their magnetic resonance imaging-based hippocampal volumes.

Results

Our experiments reveal that total cerebral blood flow was 20% lower in the AD group than in the NDC group, and that these values were directly correlated with pulse pressure and cognitive measures. The AD group had a significantly lower pulse pressure (mean AD 48, mean NDC 71; P = 0.0004). A significant group difference was also observed in their hippocampal volumes. Composite z-scores for clinical, psychometric, hippocampal volume, and hemodynamic data differed between the AD and NDC subjects, with values in the former being significantly lower (t = 12.00, df = 1, P = 0.001) than in the latter.

Conclusion

These results indicate an association between brain hypoperfusion and the dementia of AD. Cardiovascular disease combined with brain hypoperfusion may participate in the pathogenesis/pathophysiology of neurodegenerative diseases. Future longitudinal and larger-scale confirmatory investigations measuring multidomain parameters are warranted.

By restoring mitochondrial function, methylene blue (MB) is an effective neuroprotectant in many neurological disorders (e.g., Parkinson’s and Alzheimer’s diseases). MB has also been proposed as a brain metabolic enhancer because of its action on mitochondrial cytochrome c oxidase. We used in vitro and in vivo approaches to determine how MB affects brain metabolism and hemodynamics. For in vitro, we evaluated the effect of MB on brain mitochondrial function, oxygen consumption, and glucose uptake. For in vivo, we applied neuroimaging and intravenous measurements to determine MB’s effect on glucose uptake, cerebral blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO2) under normoxic and hypoxic conditions in rats. MB significantly increases mitochondrial complex I–III activity in isolated mitochondria and enhances oxygen consumption and glucose uptake in HT-22 cells. Using positron emission tomography and magnetic resonance imaging (MRI), we observed significant increases in brain glucose uptake, CBF, and CMRO2 under both normoxic and hypoxic conditions. Further, MRI revealed that MB dramatically increased CBF in the hippocampus and in the cingulate, motor, and frontoparietal cortices, areas of the brain affected by Alzheimer’s and Parkinson’s diseases. Our results suggest that MB can enhance brain metabolism and hemodynamics, and multimetric neuroimaging systems offer a noninvasive, nondestructive way to evaluate treatment efficacy.

Quantitative positron emission tomography (PET) brain studies often require that the input function be measured, typically via arterial cannulation. Image-derived input function (IDIF) is an elegant and attractive noninvasive alternative to arterial sampling. However, IDIF is also a very challenging technique associated with several problems that must be overcome before it can be successfully implemented in clinical practice. As a result, IDIF is rarely used as a tool to reduce invasiveness in patients. The aim of the present review was to identify the methodological problems that hinder widespread use of IDIF in PET brain studies. We conclude that IDIF can be successfully implemented only with a minority of PET tracers. Even in those cases, it only rarely translates into a less-invasive procedure for the patient. Finally, we discuss some possible alternative methods for obtaining less-invasive input function.

Although low executive functioning is a risk factor for vehicle crashes among elderly drivers, the neural basis of individual differences in this cognitive ability remains largely unknown. Here we aimed to examine regional frontal gray matter volume associated with executive functioning in normal aging individuals, using voxel-based morphometry (VBM). To this end, 39 community-dwelling elderly volunteers who drove a car on a daily basis participated in structural magnetic resonance imaging, and completed two questionnaires concerning executive functioning and risky driving tendencies in daily living. Consequently, we found that participants with low executive function capacity were prone to risky driving. Furthermore, VBM analysis revealed that lower executive function capacity was associated with smaller gray matter volume in the supplementary motor area (SMA). Thus, the current data suggest that SMA volume is a reliable predictor of individual differences in executive function capacity as a risk factor for vehicle crashes among elderly persons. The implication of our results is that regional frontal gray matter volume might underlie the variation in driving tendencies among elderly drivers. Therefore, detailed driving behavior assessments might be able to detect early neurodegenerative changes in the frontal lobe in normal aging adults.

Amblyopia, also known as lazy eye, usually occurs during early childhood and results in poor or blurred vision. Recent neuroimaging studies have found cortical structural/functional abnormalities in amblyopia. However, until now, it was still not known whether the spontaneous activity of the brain changes in amblyopia subjects. In the present study, regional homogeneity (ReHo), a measure of the homogeneity of functional magnetic resonance imaging signals, was used for the first time to investigate changes in resting-state local spontaneous brain activity in individuals with anisometropic amblyopia. Compared with age- and gender-matched subjects with normal vision, the anisometropic amblyopia subjects showed decreased ReHo of spontaneous brain activity in the right precuneus, the left medial prefrontal cortex, the left inferior frontal gyrus, and the left cerebellum, and increased ReHo of spontaneous brain activity was found in the bilateral conjunction area of the postcentral and precentral gyri, the left paracentral lobule, the left superior temporal gyrus, the left fusiform gyrus, the conjunction area of the right insula, putamen and the right middle occipital gyrus. The observed decreases in ReHo may reflect decreased visuo-motor processing ability, and the increases in ReHo in the somatosensory cortices, the motor areas and the auditory area may indicate compensatory plasticity in amblyopia.

This paper responds to Thompson and Holland (2011), who challenged our tensor-based morphometry (TBM) method for estimating rates of brain changes in serial MRI from 431 subjects scanned every 6 months, for 2 years. Thompson and Holland noted an unexplained jump in our atrophy rate estimates: an offset between 0-6 months that may bias clinical trial power calculations. We identified why this jump occurs and propose a solution. By enforcing inverse-consistency in our TBM method, the offset dropped from 1.4% to 0.28%, giving plausible anatomical trajectories. Transitivity error accounted for the minimal remaining offset. Drug trial sample size estimates with the revised TBM-derived metrics are highly competitive with other methods, though higher than previously reported sample size estimates by a factor of 1.6 to 2.4. Importantly, estimates are far below those given in the critique. To demonstrate a 25% slowing of atrophic rates with 80% power, 62 AD and 129 MCI subjects would be required for a 2-year trial, and 91 AD and 192 MCI subjects for a 1-year trial.

Background

The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.

Methodology/Principal Findings

The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.

Conclusions/Significance

The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

Previous PET and MRI studies have indicated that the degree to which pathology translates into clinical symptoms is strongly dependent on sex with women more likely to express pathology as a diagnosis of AD, whereas men are more resistant to clinical symptoms in the face of the same degree of pathology. Here we use DTI to investigate the difference between male and female white matter tracts in healthy older participants (24 women, 16 men) and participants with mild cognitive impairment (21 women, 12 men). Differences between control and MCI participants were found in fractional anisotropy (FA), radial diffusion (DR), axial diffusion (DA) and mean diffusion (MD). A significant main effect of sex was also reported for FA, MD and DR indices, with male control and male MCI participants having significantly more microstructural damage than their female counterparts. There was no sex by diagnosis interaction. Male MCIs also had significantly less normalised grey matter (GM) volume than female MCIs. However, in terms of absolute brain volume, male controls had significantly more brain volume than female controls. Normalised GM and WM volumes were found to decrease significantly with age with no age by sex interaction. Overall, these data suggest that the same degree of cognitive impairment is associated with greater structural damage in men compared with women.

The aim of our study was to assess changes in body-weight in relation to active electrode contact position in the subthalamic nucleus. Regular body weight measurements were done in 20 patients with advanced Parkinson's disease within a period of 18 months after implantation. T1-weighted (1.5T) magnetic resonance images were used to determine electrode position in the subthalamic nucleus and the Unified Parkinson's disease rating scale (UPDRS-III) was used for motor assessment. The distance of the contacts from the wall of the third ventricle in the mediolateral direction inversely correlated with weight gain (r = −0.55, p<0.01) and with neurostimulation-related motor condition expressed as the contralateral hemi-body UPDRS-III (r = −0.42, p<0.01). Patients with at least one contact within 9.3 mm of the wall experienced significantly greater weight gain (9.4±(SD)4.4 kg, N = 11) than those with both contacts located laterally (3.9±2.7 kg, N = 9) (p<0.001). The position of the active contact is critical not only for motor outcome but is also associated with weight gain, suggesting a regional effect of subthalamic stimulation on adjacent structures involved in the central regulation of energy balance, food intake or reward.

Background

Minimal hepatic encephalopathy (MHE) is a neuro-cognitive dysfunction characterized by impairment in attention, vigilance and integrative functions, while the sensorimotor function was often unaffected. Little is known, so far, about the exact neuro-pathophysiological mechanisms of aberrant cognition function in this disease.

Methodology/Principal Findings

To investigate how the brain function is changed in MHE, we applied a resting-state fMRI approach with independent component analysis (ICA) to assess the differences of resting-state networks (RSNs) between MHE patients and healthy controls. Fourteen MHE patients and 14 age-and sex-matched healthy subjects underwent resting-state fMRI scans. ICA was used to identify six RSNs [dorsal attention network (DAN), default mode network (DMN), visual network (VN), auditory network (AN), sensorimotor network (SMN), self-referential network (SRN)] in each subject. Group maps of each RSN were compared between the MHE and healthy control groups. Pearson correlation analysis was performed between the RSNs functional connectivity (FC) and venous blood ammonia levels, and neuropsychological tests scores for all patients. Compared with the healthy controls, MHE patients showed significantly decreased FC in DAN, both decreased and increased FC in DMN, AN and VN. No significant differences were found in SRN and SMN between two groups. A relationship between FC and blood ammonia levels/neuropsychological tests scores were found in specific regions of RSNs, including middle and medial frontal gyrus, inferior parietal lobule, as well as anterior and posterior cingulate cortex/precuneus.

Conclusions/Significance

MHE patients have selective impairments of RSNs intrinsic functional connectivity, with aberrant functional connectivity in DAN, DMN, VN, AN, and spared SMN and SRN. Our fMRI study might supply a novel way to understand the neuropathophysiological mechanism of cognition function changes in MHE.