Alleles of the apolipoprotein E (ApoE) gene are known to modulate the genetic risk for developing late-onset Alzheimer's disease (AD) and have been associated with hippocampal volume differences in AD. However, the effect of these alleles on hippocampal volume in younger subjects has yet to be clearly established. Using a large cohort of more than 1,400 adolescents, this study found no hippocampal volume or hippocampal asymmetry differences between carriers and non-carriers of the ApoE ?4 or ?2 alleles, nor dose-dependent effects of either allele, suggesting that regionally specific effects of these polymorphisms may only become apparent in later life.
New therapies with disease-modifying effects are urgently needed for treating Alzheimer's disease (AD). Nerve growth factor (NGF) protein has demonstrated regenerative and neuroprotective effects on basal forebrain cholinergic neurons in animal studies. In addition, AD patients treated with NGF have previously shown improved cognition, EEG activity, nicotinic binding, and glucose metabolism. However, no study to date has analyzed brain atrophy in patients treated with NGF producing cells. In this study we present MRI results of the first clinical trial in patients with AD using encapsulated NGF biodelivery to the basal forebrain. Six AD patients received the treatment during twelve months. Patients were grouped as responders and non-responders according to their twelve-months change in MMSE. Normative values were created from 131 AD patients from ADNI, selecting 36 age- and MMSE-matched patients for interpreting the longitudinal changes in MMSE and brain atrophy. Results at baseline indicated that responders showed better clinical status and less pathological levels of cerebrospinal fluid (CSF) A?1-42. However, they showed more brain atrophy, and neuronal degeneration as evidenced by higher CSF levels of T-tau and neurofilaments. At follow-up, responders showed less brain shrinkage and better progression in the clinical variables and CSF biomarkers. Noteworthy, two responders showed less brain shrinkage than the normative ADNI group. These results together with previous evidence supports the idea that encapsulated biodelivery of NGF might have the potential to become a new treatment strategy for AD with both symptomatic and disease-modifying effects.
We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (? =? -0.64, ADNI and ? = ?-0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.
A blood-based protein biomarker, or set of protein biomarkers, that could predict onset and progression of Alzheimer's disease (AD) would have great utility; potentially clinically, but also for clinical trials and especially in the selection of subjects for preventative trials. We reviewed a comprehensive list of 21 published discovery or panel-based (> 100 proteins) blood proteomics studies of AD, which had identified a total of 163 candidate biomarkers. Few putative blood-based protein biomarkers replicate in independent studies but we found that some proteins do appear in multiple studies; for example, four candidate biomarkers are found to associate with AD-related phenotypes in five independent research cohorts in these 21 studies: ?-1-antitrypsin, ?-2-macroglobulin, apolipoprotein E, and complement C3. Using SomaLogic's SOMAscan proteomics technology, we were able to conduct a large-scale replication study for 94 of the 163 candidate biomarkers from these 21 published studies in plasma samples from 677 subjects from the AddNeuroMed (ANM) and the Alzheimer's Research UK/Maudsley BRC Dementia Case Registry at King's Health Partners (ARUK/DCR) research cohorts. Nine of the 94 previously reported candidates were found to associate with AD-related phenotypes (False Discovery Rate (FDR) q-value < 0.1). These proteins show sufficient replication to be considered for further investigation as a biomarker set. Overall, we show that there are some signs of a replicable signal in the range of proteins identified in previous studies and we are able to further replicate some of these. This suggests that AD pathology does affect the blood proteome with some consistency.
The Alzheimer's Disease Neuroimaging Initiative 3.0T MRI image acquisition scheme changed between the original ADNI-1 grant and the two subsequent grants (ADNI-GO and ADNI-2). The aim of the current study was to investigate the compatibility of the 3.0T ADNI-1 and ADNI-2 T1-weighted volumes using voxel-based morphometry (VBM).
Previous studies have shown that hippocampal subfields may be differentially affected by Alzheimer's disease (AD). This study used an automated analysis technique and two large cohorts to (1) investigate patterns of subfield volume loss in mild cognitive impairment (MCI) and AD, (2) determine the pattern of subfield volume loss due to age, gender, education, APOE ?4 genotype, and neuropsychological test scores, (3) compare combined subfield volumes to hippocampal volume alone at discriminating between AD and healthy controls (HC), and predicting future MCI conversion to AD at 12 months. 1,069 subjects were selected from the AddNeuroMed and Alzheimer's disease neuroimaging initiative (ADNI) cohorts. Freesurfer was used for automated segmentation of the hippocampus and hippocampal subfields. Orthogonal partial least squares to latent structures (OPLS) was used to train models on AD and HC subjects using one cohort for training and the other for testing and the combined cohort was used to predict MCI conversion. MANCOVA and linear regression analyses showed multiple subfield volumes including Cornu Ammonis 1 (CA1), subiculum and presubiculum were atrophied in AD and MCI and were related to age, gender, education, APOE ?4 genotype, and neuropsychological test scores. For classifying AD from HC, combined subfield volumes achieved comparable classification accuracy (81.7 %) to total hippocampal (80.7 %), subiculum (81.2 %) and presubiculum (80.6 %) volume. For predicting MCI conversion to AD combined subfield volumes and presubiculum volume were more accurate (81.1 %) than total hippocampal volume. (76.7 %).
Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimer's disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate-specific antigen complexed to ?1-antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation.
Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.
Patients with non-small cell lung carcinoma (NSCLC) with activating mutations in epidermal growth factor receptor (EGFR) initially respond well to the EGFR inhibitors erlotinib and gefitinib. However, all patients relapse because of the emergence of drug-resistant mutations, with T790M mutations accounting for approximately 60% of all resistance. Second-generation irreversible EGFR inhibitors are effective against T790M mutations in vitro, but retain affinity for wild-type EGFR (EGFR(WT)). These inhibitors have not provided compelling clinical benefit in T790M-positive patients, apparently because of dose-limiting toxicities associated with inhibition of EGFR(WT). Thus, there is an urgent clinical need for therapeutics that overcome T790M drug resistance while sparing EGFR(WT). Here, we describe a lead optimization program that led to the discovery of four potent irreversible 2,4-diaminopyrimidine compounds that are EGFR mutant (EGFR(mut)) selective and have been designed to have low affinity for EGFR(WT). Pharmacokinetic and pharmacodynamic studies in H1975 tumor-bearing mice showed that exposure was dose proportional resulting in dose-dependent EGFR modulation. Importantly, evaluation of normal lung tissue from the same animals showed no inhibition of EGFR(WT). Of all the compounds tested, compound 3 displayed the best efficacy in EGFR(L858R/T790M)-driven tumors. Compound 3, now renamed CO-1686, is currently in a phase I/II clinical trial in patients with EGFR(mut)-advanced NSCLC that have received prior EGFR-directed therapy.
The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia.
Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) share brain function abnormalities during cognitive flexibility. Serotonin is involved in both disorders, and selective serotonin reuptake inhibitors (SSRIs) can modulate cognitive flexibility and improve behavior in both disorders. Thus, this study investigates shared and disorder-specific brain dysfunctions in these 2 disorders during reward reversal, and the acute effects of an SSRI on these. Age-matched boys with ADHD (15), ASD (18), and controls (21) were compared with functional magnetic resonance imaging (fMRI) during a reversal task. Patients were scanned twice, under either an acute dose of Fluoxetine or placebo in a double-blind, placebo-controlled randomized design. Repeated-measures analyses within patients assessed drug effects. Patients under each drug condition were compared with controls to assess normalization effects. fMRI data showed that, under placebo, ASD boys underactivated medial prefrontal cortex (mPFC), compared with control and ADHD boys. Both patient groups shared decreased precuneus activation. Under Fluoxetine, mPFC activation was up-regulated and normalized in ASD boys relative to controls, but down-regulated in ADHD boys relative to placebo, which was concomitant with worse task performance in ADHD. Fluoxetine therefore has inverse effects on mPFC activation in ASD and ADHD during reversal learning, suggesting dissociated underlying serotonin abnormalities.
Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed on the basis of subjective measures, despite evidence for multi-systemic structural and neurofunctional deficits. A consistently observed neurofunctional deficit is in fine-temporal discrimination (TD). The aim of this proof-of-concept study was to examine the feasibility of distinguishing patients with ADHD from controls using multivariate pattern recognition analyses of functional magnetic resonance imaging (fMRI) data of TD.
To examine neuroanatomical changes associated with depressive symptoms in Alzheimer's disease (AD) and the relationship between brain structure and cerebrospinal fluid (CSF) AD biomarkers in depressed and non-depressed patients.
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative.
Although often clinically indistinguishable in the early stages, Parkinson's disease (PD), Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) have distinct neuropathological changes. The aim of the current study was to identify white matter tract neurodegeneration characteristic of each of the three syndromes. Tract-based spatial statistics (TBSS) was used to perform a whole-brain automated analysis of diffusion tensor imaging (DTI) data to compare differences in fractional anisotropy (FA) and mean diffusivity (MD) between the three clinical groups and healthy control subjects. Further analyses were conducted to assess the relationship between these putative indices of white matter microstructure and clinical measures of disease severity and symptoms. In PSP, relative to controls, changes in DTI indices consistent with white matter tract degeneration were identified in the corpus callosum, corona radiata, corticospinal tract, superior longitudinal fasciculus, anterior thalamic radiation, superior cerebellar peduncle, medial lemniscus, retrolenticular and anterior limb of the internal capsule, cerebral peduncle and external capsule bilaterally, as well as the left posterior limb of the internal capsule and the right posterior thalamic radiation. MSA patients also displayed differences in the body of the corpus callosum corticospinal tract, cerebellar peduncle, medial lemniscus, anterior and superior corona radiata, posterior limb of the internal capsule external capsule and cerebral peduncle bilaterally, as well as the left anterior limb of the internal capsule and the left anterior thalamic radiation. No significant white matter abnormalities were observed in the PD group. Across groups, MD correlated positively with disease severity in all major white matter tracts. These results show widespread changes in white matter tracts in both PSP and MSA patients, even at a mid-point in the disease process, which are not found in patients with PD.
In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV) is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total), the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI) data were analyzed from two large cohorts, the population-based PIVUS cohort (N = 406, all subjects age 75) and the Alzheimer disease Neuroimaging Initiative (ADNI) cohort (N = 724). Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimer's disease, mild cognitive impairment, and healthy individuals). The ability of raw vs. adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most gray matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.
Cross sectional studies of patients at risk of developing Alzheimer disease (AD) have identified several brain regions known to be prone to degeneration suitable as biomarkers, including hippocampal, ventricular, and whole brain volume. The aim of this study was to longitudinally evaluate an index based on morphometric measures derived from MRI data that could be used for classification of AD and healthy control subjects, as well as prediction of conversion from mild cognitive impairment (MCI) to AD. Patients originated from the AddNeuroMed project at baseline (119 AD, 119 MCI, 110 controls (CTL)) and 1-year follow-up (62 AD, 73 MCI, 79 CTL). Data consisted of 3D T1-weighted MR images, demographics, MMSE, ADAS-Cog, CERAD and CDR scores, and APOE e4 status. We computed an index using a multivariate classification model (AD vs. CTL), using orthogonal partial least squares to latent structures (OPLS). Sensitivity, specificity and AUC were determined. Performance of the classifier (AD vs. CTL) was high at baseline (10-fold cross-validation, 84% sensitivity, 91% specificity, 0.93 AUC) and at 1-year follow-up (92% sensitivity, 74% specificity, 0.93 AUC). Predictions of conversion from MCI to AD were good at baseline (77% of MCI converters) and at follow-up (91% of MCI converters). MCI carriers of the APOE e4 allele manifested more atrophy and presented a faster cognitive decline when compared to non-carriers. The derived index demonstrated a steady increase in atrophy over time, yielding higher accuracy in prediction at the time of clinical conversion. Neuropsychological tests appeared less sensitive to changes over time. However, taking the average of the two time points yielded better correlation between the index and cognitive scores as opposed to using cross-sectional data only. Thus, multivariate classification seemed to detect patterns of AD changes before conversion from MCI to AD and including longitudinal information is of great importance.
Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges in that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i) the construction of summary networks, such as how to compute and visualize the summary network from a sample of network-valued data points; and (ii) how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN). In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.
Cognitive impairment is a common non-motor feature of Parkinson's disease (PD). Understanding the neural mechanisms of this deficit is crucial for the development of efficient methods for treatment monitoring and augmentation of cognitive functions in PD patients. The current study aimed to investigate resting state fMRI correlates of cognitive impairment in PD from a large-scale network perspective, and to assess the impact of dopamine deficiency on these networks. Thirty PD patients with resting state fMRI were included from the Parkinson's Progression Marker Initiative (PPMI) database. Eighteen patients from this sample were also scanned with (123)I-FP-CIT SPECT. A standardized neuropsychological battery was administered, evaluating verbal memory, visuospatial, and executive cognitive domains. Image preprocessing was performed using an SPM8-based workflow, obtaining time-series from 90 regions-of-interest (ROIs) defined from the AAL brain atlas. The Brain Connectivity Toolbox (BCT) was used to extract nodal strength from all ROIs, and modularity of the cognitive circuitry determined using the meta-analytical software Neurosynth. Brain-behavior covariance patterns between cognitive functions and nodal strength were estimated using Partial Least Squares. Extracted latent variable (LV) scores were matched with the performances in the three cognitive domains (memory, visuospatial, and executive) and striatal dopamine transporter binding ratios (SBR) using linear modeling. Finally, influence of nigrostriatal dopaminergic deficiency on the modularity of the "cognitive network" was analyzed. For the range of deficits studied, better executive performance was associated with increased dorsal fronto-parietal cortical processing and inhibited subcortical and primary sensory involvement. This profile was also characterized by a relative preservation of nigrostriatal dopaminergic function. The profile associated with better memory performance correlated with increased prefronto-limbic processing, and was not associated with presynaptic striatal dopamine uptake. SBR ratios were negatively correlated with modularity of the "cognitive network," suggesting integrative effects of the preserved nigrostriatal dopamine system on this circuitry.
The integrity of brain white matter connections is central to a patients ability to respond to pharmacological interventions. This study tested this hypothesis using a specific measure of white matter integrity, and examining its relationship to treatment response using a prospective design in patients within their first episode of psychosis. Diffusion tensor imaging data were acquired in 63 patients with first episode psychosis and 52 healthy control subjects (baseline). Response was assessed after 12 weeks and patients were classified as responders or non-responders according to treatment outcome. At this second time-point, they also underwent a second diffusion tensor imaging scan. Tract-based spatial statistics were used to assess fractional anisotropy as a marker of white matter integrity. At baseline, non-responders showed lower fractional anisotropy than both responders and healthy control subjects (P < 0.05; family-wise error-corrected), mainly in the uncinate, cingulum and corpus callosum, whereas responders were indistinguishable from healthy control subjects. After 12 weeks, there was an increase in fractional anisotropy in both responders and non-responders, positively correlated with antipsychotic exposure. This represents one of the largest, controlled investigations of white matter integrity and response to antipsychotic treatment early in psychosis. These data, together with earlier findings on cortical grey matter, suggest that grey and white matter integrity at the start of treatment is an important moderator of response to antipsychotics. These findings can inform patient stratification to anticipate care needs, and raise the possibility that antipsychotics may restore white matter integrity as part of the therapeutic response.
Patients with non-small cell lung cancer (NSCLC) with activating EGF receptor (EGFR) mutations initially respond to first-generation reversible EGFR tyrosine kinase inhibitors. However, clinical efficacy is limited by acquired resistance, frequently driven by the EGFR(T790M) mutation. CO-1686 is a novel, irreversible, and orally delivered kinase inhibitor that specifically targets the mutant forms of EGFR, including T790M, while exhibiting minimal activity toward the wild-type (WT) receptor. Oral administration of CO-1686 as single agent induces tumor regression in EGFR-mutated NSCLC tumor xenograft and transgenic models. Minimal activity of CO-1686 against the WT EGFR receptor was observed. In NSCLC cells with acquired resistance to CO-1686 in vitro, there was no evidence of additional mutations or amplification of the EGFR gene, but resistant cells exhibited signs of epithelial-mesenchymal transition and demonstrated increased sensitivity to AKT inhibitors. These results suggest that CO-1686 may offer a novel therapeutic option for patients with mutant EGFR NSCLC.
BackgroundRecent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms.MethodsWe compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features.ResultsFor smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study.
At present, no reliable predictors exist to distinguish future responders from nonresponders to treatment during the first episode of psychosis. Among potential neuroimaging predictors of treatment response, gyrification represents an important marker of the integrity of normal cortical development that may characterize, already at illness onset, a subgroup of patients with particularly poor outcome.
Downs syndrome (DS) is the most common genetic cause of intellectual disability. People with DS are at an increased risk of Alzheimers disease (AD) compared to the general population. Neuroimaging studies of AD have focused on medial temporal structures; however, to our knowledge, no in vivo case-control study exists comparing the anatomy of dementia in DS to people with AD in the general population. We therefore compared the in vivo brain anatomy of people with DS and dementia (DS+) to those with AD in the general population.
We examined the maturation of decision-making from early adolescence to mid-adulthood using fMRI of a variant of the Iowa gambling task. We have previously shown that performance in this task relies on sensitivity to accumulating negative outcomes in ventromedial PFC and dorsolateral PFC. Here, we further formalize outcome evaluation (as driven by prediction errors [PE], using a reinforcement learning model) and examine its development. Task performance improved significantly during adolescence, stabilizing in adulthood. Performance relied on greater impact of negative compared with positive PEs, the relative impact of which matured from adolescence into adulthood. Adolescents also showed increased exploratory behavior, expressed as a propensity to shift responding between options independently of outcome quality, whereas adults showed no systematic shifting patterns. The correlation between PE representation and improved performance strengthened with age for activation in ventral and dorsal PFC, ventral striatum, and temporal and parietal cortices. There was a medial-lateral distinction in the prefrontal substrates of effective PE utilization between adults and adolescents: Increased utilization of negative PEs, a hallmark of successful performance in the task, was associated with increased activation in ventromedial PFC in adults, but decreased activation in ventrolateral PFC and striatum in adolescents. These results suggest that adults and adolescents engage qualitatively distinct neural and psychological processes during decision-making, the development of which is not exclusively dependent on reward-processing maturation.
Functional inhibitory neural networks mature progressively with age. However, nothing is known about the impact of gender on their development. This study employed functional magnetic resonance imaging (fMRI) to investigate the effects of age, sex, and sex by age interactions on the brain activation of 63 healthy males and females, between 13 and 38 years, performing a Stop task. Increasing age was associated with progressively increased activation in typical response inhibition areas of right inferior and dorsolateral prefrontal and temporo-parietal regions. Females showed significantly enhanced activation in left inferior and superior frontal and striatal regions relative to males, while males showed increased activation relative to females in right inferior and superior parietal areas. Importantly, left frontal and striatal areas that showed increased activation in females, also showed significantly increased functional maturation in females relative to males, while the right inferior parietal activation that was increased in males showed significantly increased functional maturation relative to females. The findings demonstrate for the first time that sex-dimorphic activation patterns of enhanced left fronto-striatal activation in females and enhanced right parietal activation in males during motor inhibition appear to be the result of underlying gender differences in the functional maturation of these brain regions.
Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimers disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters.
The catecholamine agonists methylphenidate and atomoxetine effectively treat attention-deficit/hyperactivity disorder (ADHD). Furthermore, dopamine agonists have shown to improve time estimation in ADHD, a core cognitive deficit. However, few have compared the effects of methylphenidate and atomoxetine on brain function in ADHD, and none during time estimation. Using single dose challenges, we investigated shared and drug-specific effects in ADHD adolescents on the neural substrates of time discrimination (TD).
Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering and independent component analysis (ICA) have been used to identify such spatio-temporal networks. While these approaches have been useful for estimating these networks at the subject-level, comparisons over groups or experimental conditions require further methodological development. In this paper, we tackle this problem by showing how self-organizing maps (SOMs) can be compared within a Frechean inferential framework. Here, we summarize the mean SOM in each group as a Frechet mean with respect to a metric on the space of SOMs. The advantage of this approach is twofold. Firstly, it allows the visualization of the mean SOM in each experimental condition. Secondly, this Frechean approach permits one to draw inference on group differences, using permutation of the group labels. We consider the use of different distance functions, and introduce one extension of the classical sum of minimum distance (SMD) between two SOMs, which take into account the spatial pattern of the fMRI data. The validity of these methods is illustrated on synthetic data. Through these simulations, we show that the two distance functions of interest behave as expected, in the sense that the ones capturing temporal and spatial aspects of the SOMs are more likely to reach significance under simulated scenarios characterized by temporal, spatial [and spatio-temporal] differences, respectively. In addition, a re-analysis of a classical experiment on visually-triggered emotions demonstrates the usefulness of this methodology. In this study, the multivariate functional patterns typical of the subjects exposed to pleasant and unpleasant stimuli are found to be more similar than the ones of the subjects exposed to emotionally neutral stimuli. In this re-analysis, the group-level SOM output units with the smallest sample Jaccard indices were compared with standard GLM group-specific z-score maps, and provided considerable levels of agreement. Taken together, these results indicate that our proposed methods can cast new light on existing data by adopting a global analytical perspective on functional MRI paradigms.
In brain volumetric studies, intracranial volume (ICV) is often used as an estimate of pre-morbid brain size as well as to compensate for inter-subject variations in head size. However, if the estimated ICV is biased by for example gender or atrophy, it could introduce errors in study results. To evaluate how two commonly used methods for ICV estimation perform, computer assisted reference segmentations were created and evaluated. Segmentations were created for 399 MRI volumes from 75-year-old subjects, with 53 of these subjects having an additional scan and segmentation created at age 80. ICV estimates from Statistical Parametric Mapping (SPM, version 8) and Freesurfer (FS, version 5.1.0) were compared to the reference segmentations, and bias related to skull size (approximated with the segmentation measure), gender or atrophy were tested for. The possible ICV related effect on associations between normalized hippocampal volume and factors gender, education and cognition was evaluated by normalizing hippocampal volume with different ICV measures. Excellent agreement was seen for inter- (r=0.999) and intra- (r=0.999) operator reference segmentations. Both SPM and FS overestimated ICV. SPM showed bias associated with gender and atrophy while FS showed bias dependent on skull size. All methods showed good correlation between time points in the longitudinal data (reference: 0.998, SPM: 0.962, FS: 0.995). Hippocampal volume showed different associations with cognition and gender depending on which ICV measure was used for hippocampal volume normalization. These results show that the choice of method used for ICV estimation can bias results in studies including brain volume measurements.
Markers of Alzheimers disease (AD) are being widely sought with a number of studies suggesting blood measures of inflammatory proteins as putative biomarkers. Here we report findings from a panel of 27 cytokines and related proteins in over 350 subjects with AD, subjects with Mild Cognitive Impairment (MCI) and elderly normal controls where we also have measures of longitudinal change in cognition and baseline neuroimaging measures of atrophy. In this study, we identify five inflammatory proteins associated with evidence of atrophy on MR imaging data particularly in whole brain, ventricular and entorhinal cortex measures. In addition, we observed six analytes that showed significant change (over a period of one year) in people with fast cognitive decline compared to those with intermediate and slow decline. One of these (IL-10) was also associated with brain atrophy in AD. In conclusion, IL-10 was associated with both clinical and imaging evidence of severity of disease and might therefore have potential to act as biomarker of disease progression.
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The studys aim was to apply Gaussian process classification (GPC) to grey matter (GM) volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD).
The behavioural literature in anorexia nervosa and autism spectrum disorders has indicated an overlap in cognitive profiles. One such domain is the enhancement of local processing over global processing. While functional imaging studies of autism spectrum disorder have revealed differential neural patterns compared to controls in response to tests of local versus global processing, no studies have explored such effects in anorexia nervosa. This study uses functional magnetic resonance imaging in conjunction with the embedded figures test, to explore the neural correlates of this enhanced attention to detail in the largest anorexia nervosa cohort to date. On the embedded figures tests participants are required to indicate which of two complex figures contains a simple geometrical shape. The findings indicate that whilst healthy controls showed greater accuracy on the task than people with anorexia nervosa, different brain regions were recruited. Healthy controls showed greater activation in the precuneus whilst people with anorexia nervosa showed greater activation in the fusiform gyrus. This suggests that different cognitive strategies were used to perform the task, i.e. healthy controls demonstrated greater emphasis on visuospatial searching and people with anorexia nervosa employed a more object recognition-based approach. This is in accordance with previous findings in autism spectrum disorder using a similar methodology and has implications for therapies addressing the appropriate adjustment of cognitive strategies in anorexia nervosa.
Peripheral biomarkers of Alzheimers disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N?=?27) and MCI (N?=?17). In the current report, we validated this finding in a large independent cohort of AD (N?=?79), MCI (N?=?88) and control (N?=?95) subjects using alternative complementary methods-quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, ?-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis.
This systematic review summarises and critically appraises the literature on structural magnetic resonance imaging in people with a current or past eating disorder. Studies using voxel-based morphometry image analysis were included. Ten studies reported on a total of 236 people with a current or past eating disorder and 257 healthy controls. Sample heterogeneity prohibited a meta-analytic approach. The findings do not unequivocally indicate grey or white matter volume abnormalities in people with an eating disorder. Nevertheless, these preliminary data suggest that, compared with healthy controls, people with anorexia nervosa have decreased grey matter in a range of brain regions and that those with bulimia nervosa have increased grey matter volumes in frontal and ventral striatal areas. Research in the recovery phase and longitudinal studies suggest that potential brain tissue abnormalities may recover with clinical improvement. Overall, as the available data are inconclusive, further efforts in this field are warranted.
Vitamin E includes 8 natural compounds (4 tocopherols, 4 tocotrienols) with potential neuroprotective activity. ?-Tocopherol has mainly been investigated in relation to cognitive impairment. We examined the relation of all plasma vitamin E forms and markers of vitamin E damage (?-tocopherylquinone, 5-nitro-?-tocopherol) to mild cognitive impairment (MCI) and Alzheimers disease (AD). Within the AddNeuroMed-Project, plasma tocopherols, tocotrienols, ?-tocopherylquinone, and 5-nitro-?-tocopherol were assessed in 168 AD cases, 166 MCI, and 187 cognitively normal (CN) people. Compared with cognitively normal subjects, AD and MCI had lower levels of total tocopherols, total tocotrienols, and total vitamin E. In multivariable-polytomous-logistic regression analysis, both MCI and AD cases had 85% lower odds to be in the highest tertile of total tocopherols and total vitamin E, and they were, respectively, 92% and 94% less likely to be in the highest tertile of total tocotrienols than the lowest tertile. Further, both disorders were associated with increased vitamin E damage. Low plasma tocopherols and tocotrienols levels are associated with increased odds of MCI and AD.
Compared to our understanding of the functional maturation of executive functions, little is known about the neurofunctional development of perceptive functions. Time perception develops during late adolescence, underpinning many functions including motor and verbal processing, as well as late maturing higher order cognitive skills such as forward planning and future-related decision making. Nothing, however, is known about the neurofunctional changes associated with time perception from childhood to adulthood. Using functional magnetic resonance imaging we explored the effects of age on the brain activation and functional connectivity of 32 male participants from 10 to 53?years of age during a time discrimination task that required the discrimination of temporal intervals of seconds differing by several hundred milliseconds. Increasing development was associated with progressive activation increases within left lateralized dorsolateral and inferior fronto-parieto-striato-thalamic brain regions. Furthermore, despite comparable task performance, adults showed increased functional connectivity between inferior/dorsolateral interhemispheric fronto-frontal activation as well as between inferior fronto-parietal regions compared with adolescents. Activation in caudate, specifically, was associated with both increasing age and better temporal discrimination. Progressive decreases in activation with age were observed in ventromedial prefrontal cortex, limbic regions, and cerebellum. The findings demonstrate age-dependent developmentally dissociated neural networks for time discrimination. With increasing age there is progressive recruitment of later maturing left hemispheric and lateralized fronto-parieto-striato-thalamic networks, known to mediate time discrimination in adults, while earlier developing brain regions such as ventromedial prefrontal cortex, limbic and paralimbic areas, and cerebellum subserve fine-temporal processing functions in children and adolescents.
Although differences in clinical characteristics exist between major depressive disorder (MDD) and bipolar disorder (BD), consistent structural brain abnormalities that distinguish the disorders have not been identified.
A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.
This paper presents a novel approach for improved diffusion tensor fibre tractography, aiming to tackle a number of the limitations of current fibre tracking algorithms, and describes a quantitative analysis tool for probabilistic tracking algorithms. We consider the sampled random paths generated by a probabilistic tractography algorithm from a seed point as a set of curves, and develop a statistical framework for analysing the curve-set geometrically that finds the average curve and dispersion measures of the curve-set statistically. This study is motivated firstly by the goal of developing a robust fibre tracking algorithm, combining the power of both deterministic and probabilistic tracking methods using average curves. These typical curves produce strong connections to every anatomically distinct fibre tract from a seed point and also convey important information about the underlying probability distribution. These single well-defined trajectories overcome a number of the limitations of deterministic and probabilistic approaches. A new clustering algorithm for branching curves is employed to separate fibres into branches before applying the averaging methods. Secondly, a quantitative analysis tool for probabilistic tracking methods is introduced using statistical measures of curve-sets. Results on phantom and in vivo data confirm the efficiency and effectiveness of the proposed approach for the tracking algorithm and the quantitative analysis of the probabilistic methods.
To describe the AddNeuroMed imaging framework for multi-centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimers disease and report on early results from the first 24 months of the project.
Visual assessment rating scales for medial temporal lobe (MTL) atrophy have been used by neuroradiologists in clinical practice to aid the diagnosis of Alzheimers disease (AD). Recently multivariate classification methods for magnetic resonance imaging (MRI) data have been suggested as alternative tools. If computerized methods are to be implemented in clinical practice they need to be as good as, or better than experienced neuroradiologists and carefully validated. The aims of this study were: (1) To compare the ability of MTL atrophy visual assessment rating scales, a multivariate MRI classification method and manually measured hippocampal volumes to distinguish between subjects with AD and healthy elderly controls (CTL). (2) To assess how well the three techniques perform when predicting future conversion from mild cognitive impairment (MCI) to AD.
Right hemisphere dominance for visuospatial attention is characteristic of most humans, but its anatomical basis remains unknown. We report the first evidence in humans for a larger parieto-frontal network in the right than left hemisphere, and a significant correlation between the degree of anatomical lateralization and asymmetry of performance on visuospatial tasks. Our results suggest that hemispheric specialization is associated with an unbalanced speed of visuospatial processing.
Although a wide range of approaches have been developed to automatically assess the volume of brain regions from MRI, the reproducibility of these algorithms across different scanners and pulse sequences, their accuracy in different clinical populations and sensitivity to real changes in brain volume have not always been comprehensively examined. Firstly we present a comprehensive testing protocol which comprises 312 freely available MR images to assess the accuracy, reproducibility and sensitivity of automated brain segmentation techniques. Accuracy is assessed in infants, young adults and patients with Alzheimers disease in comparison to gold standard measures by expert observers using a manual technique based on Cavalieris principle. The protocol determines the reliability of segmentation between scanning sessions, different MRI pulse sequences and 1.5T and 3T field strengths and examines their sensitivity to small changes in volume using a large longitudinal dataset. Secondly we apply this testing protocol to a novel algorithm for segmenting the lateral ventricles and compare its performance to the widely used FSL FIRST and FreeSurfer methods. The testing protocol produced quantitative measures of accuracy, reliability and sensitivity of lateral ventricle volume estimates for each segmentation method. The novel algorithm showed high accuracy in all populations (intraclass correlation coefficient, ICC>0.95), good reproducibility between MRI pulse sequences (ICC>0.99) and was sensitive to age related changes in longitudinal data. FreeSurfer demonstrated high accuracy (ICC>0.95), good reproducibility (ICC>0.99) and sensitivity whilst FSL FIRST showed good accuracy in young adults and infants (ICC>0.90) and good reproducibility (ICC=0.98), but was unable to segment ventricular volume in patients with Alzheimers disease or healthy subjects with large ventricles. Using the same computer system, the novel algorithm and FSL FIRST processed a single MRI image in less than 10min while FreeSurfer took approximately 7h. The testing protocol presented enables the accuracy, reproducibility and sensitivity of different algorithms to be compared. We also demonstrate that the novel segmentation algorithm and FreeSurfer are both effective in determining lateral ventricular volume and are well suited for multicentre and longitudinal MRI studies.
It is poorly understood why people with Down syndrome (DS) are at extremely high-risk of developing Alzheimers disease (AD) compared to the general population. One explanation may be related to their extra copy of risk factors modulated by chromosome 21. Myo-inositol (mI), whose transporter gene is located on chromosome 21, has been associated with dementia in the non-DS population; however, nobody has contrasted brain mI in DS with (DS+) and without (DS-) dementia to other non-DS groups. Our primary aim was to compare the hippocampal concentration of mI ([mI]) and other brain metabolites such as N-acetylaspartate (NAA; a proxy measure of neuronal density and mitochondrial function) in DS+, DS-, and age-matched healthy controls using proton Magnetic Resonance Spectroscopy (((1))H-MRS). We compared hippocampal [mI] and other metabolites in 35 individuals with genetically-confirmed DS [DS+ (n=17, age=53±6) and DS- (n=18, age=47±8)] to age-matched healthy controls (n=13, age=51±10) adjusting for proportion of the MRS voxel occupied by cerebrospinal spinal fluid, and gray/white matter. DS+ had a significantly higher [mI] than both DS- and healthy controls. In contrast neither DS+ nor DS- differed significantly from controls in [NAA] (although NAA in DS+ was significantly lower than DS-). Our secondary aim of comparing brain metabolites in DS+ and DS- to Alzheimers disease (AD; n=39; age=77±5) revealed that the DS+ group had significantly elevated [mI] compared to AD or DS-. [mI] may modify risk for dementia in this vulnerable population.
The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimers disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.
Human voices play a fundamental role in social communication, and areas of the adult "social brain" show specialization for processing voices and their emotional content (superior temporal sulcus, inferior prefrontal cortex, premotor cortical regions, amygdala, and insula). However, it is unclear when this specialization develops. Functional magnetic resonance (fMRI) studies suggest that the infant temporal cortex does not differentiate speech from music or backward speech, but a prior study with functional near-infrared spectroscopy revealed preferential activation for human voices in 7-month-olds, in a more posterior location of the temporal cortex than in adults. However, the brain networks involved in processing nonspeech human vocalizations in early development are still unknown. To address this issue, in the present fMRI study, 3- to 7-month-olds were presented with adult nonspeech vocalizations (emotionally neutral, emotionally positive, and emotionally negative) and nonvocal environmental sounds. Infants displayed significant differential activation in the anterior portion of the temporal cortex, similarly to adults. Moreover, sad vocalizations modulated the activity of brain regions involved in processing affective stimuli such as the orbitofrontal cortex and insula. These results suggest remarkably early functional specialization for processing human voice and negative emotions.
The safe operation of both clinical and pre-clinical MR systems is critical. There are a wide range of potential MR hazards. This chapter covers both the theoretical background to issues of MR safety and the guidance on more practical issues. The main sources of information on national and international MR safety guidance and advice are discussed, as well as local safety policies which are required for all MR installations. The projectile effect and other MR safety issues due to static and time-varying magnetic fields are considered, such as peripheral nerve stimulation, tissue heating and RF burns. Finally, contrast agents, auditory effects and medical implants and devices are discussed, as well as the less thought about issue of biological safety of clinical and pre-clinical MR systems.
Proton magnetic resonance spectroscopy ((1)H-MRS) studies have previously reported reduced brain N-acetyl aspartate (NAA) and increased myo-inositol (mI) in people with established Alzheimers disease (AD). The earliest structure affected by AD is the hippocampus but relatively few studies have examined its neuronal integrity by MRS in AD and fewer still in people with amnestic mild cognitive impairment (MCI). We measured the hippocampal concentration of NAA, mI, choline (Cho) and creatine + phosphocreatine (Cr + PCr) in 39 patients with AD, 21 subjects with MCI and 38 age matched healthy elderly controls. Patients with AD had a significantly lower hippocampal [NAA] than controls, with subjects with MCI intermediate between the other two groups. [NAA] was positively correlated with memory in the impaired groups. Using mean hippocampal [NAA] and [Cr + PCr] we correctly classified 72% of people with AD, and 75% of controls. Reductions in [NAA] can be detected in the hippocampi of subjects with MCI and hippocampal [NAA] and [Cr + PCr] can distinguish between mild AD and normal elderly controls.
The hippocampus is involved at the onset of the neuropathological pathways leading to Alzheimers disease (AD). Individuals with mild cognitive impairment (MCI) are at increased risk of AD. Hippocampal volume has been shown to predict which MCI subjects will convert to AD. Our aim in the present study was to produce a fully automated prognostic procedure, scalable to high throughput clinical and research applications, for the prediction of MCI conversion to AD using 3D hippocampal morphology. We used an automated analysis for the extraction and mapping of the hippocampus from structural magnetic resonance scans to extract 3D hippocampal shape morphology, and we then applied machine learning classification to predict conversion from MCI to AD. We investigated the accuracy of prediction in 103 MCI subjects (mean age 74.1 years) from the longitudinal AddNeuroMed study. Our model correctly predicted MCI conversion to dementia within a year at an accuracy of 80% (sensitivity 77%, specificity 80%), a performance which is competitive with previous predictive models dependent on manual measurements. Categorization of MCI subjects based on hippocampal morphology revealed more rapid cognitive deterioration in MMSE scores (p<0.01) and CERAD verbal memory (p<0.01) in those subjects who were predicted to develop dementia relative to those predicted to remain stable. The pattern of atrophy associated with increased risk of conversion demonstrated initial degeneration in the anterior part of the cornus ammonis 1 (CA1) hippocampal subregion. We conclude that automated shape analysis generates sensitive measurements of early neurodegeneration which predates the onset of dementia and thus provides a prognostic biomarker for conversion of MCI to AD.
The role of the septohippocampal cholinergic system in memory disorders is well established. The effects of cholinergic challenge in animals have been extensively studied using the Morris Water Maze (MWM) which engages allocentric spatial memory. The present study investigated the effect of the centrally active muscarinic antagonist scopolamine on allocentric spatial memory in humans using a virtual reality analogue of the MWM task, the Arena task. Twenty right-handed healthy male adults with a mean age of 28 years (range 23-35 years) were studied using functional MRI in a randomized double-blind cross-over design with scopolamine bromide (0.4?mg i.m.) or placebo (saline) administered 70-90?min before the beginning of the functional scan. Scopolamine induced a significant reduction in the activation of the hippocampus/parahippocampal gyrus compared with placebo. Furthermore, there was dissociation between hippocampus-based and striatal-based memory systems, which were significantly more activated in the placebo and scopolamine conditions, respectively. The activation of the striatal system under scopolamine challenge was accompanied by the activation of the amygdala. In conclusion, the study extends the well-documented finding in animals of the attenuating effect of scopolamine on hippocampal activity during allocentric spatial memory to humans. Furthermore, the results call for further investigation of the dissociation between the hippocampal and neostriatal memory systems during allocentric spatial processing under cholinergic blockade in humans.
We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimers disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.
Network analysis has become a tool of choice for the study of functional and structural Magnetic Resonance Imaging (MRI) data. Little research, however, has investigated connectivity dynamics in relation to varying cognitive load. In fMRI, correlations among slow (<0.1 Hz) fluctuations of blood oxygen level dependent (BOLD) signal can be used to construct functional connectivity networks. Using an anatomical parcellation scheme, we produced undirected weighted graphs linking 90 regions of the brain representing major cortical gyri and subcortical nuclei, in a population of healthy adults (n=43). Topological changes in these networks were investigated under different conditions of a classical working memory task - the N-back paradigm. A mass-univariate approach was adopted to construct statistical parametric networks (SPNs) that reflect significant modifications in functional connectivity between N-back conditions. Our proposed method allowed the extraction of lost and gained functional networks, providing concise graphical summaries of whole-brain network topological changes. Robust estimates of functional networks are obtained by pooling information about edges and vertices over subjects. Graph thresholding is therefore here supplanted by inference. The analysis proceeds by firstly considering changes in weighted cost (i.e. mean between-region correlation) over the different N-back conditions and secondly comparing small-world topological measures integrated over network cost, thereby controlling for differences in mean correlation between conditions. The results are threefold: (i) functional networks in the four conditions were all found to satisfy the small-world property and cost-integrated global and local efficiency levels were approximately preserved across the different experimental conditions; (ii) weighted cost considerably decreased as working memory load increased; and (iii) subject-specific weighted costs significantly predicted behavioral performances on the N-back task (Wald F=13.39,df(1)=1,df(2)=83,p<0.001), and therefore conferred predictive validity to functional connectivity strength, as measured by weighted cost. The results were found to be highly sensitive to the frequency band used for the computation of the between-region correlations, with the relationship between weighted cost and behavioral performance being most salient at very low frequencies (0.01-0.03 Hz). These findings are discussed in relation to the integration/specialization functional dichotomy. The pruning of functional networks under increasing cognitive load may permit greater modular specialization, thereby enhancing performance.
We determined predictors of conversion to Alzheimers disease (AD) from mild cognitive impairment (MCI) with automated magnetic resonance imaging (MRI) regional cortical volume and thickness measures. One hundred amnestic MCI subjects, 118 AD patients, and 94 age-matched healthy controls were selected from AddNeuroMed study. Twenty-four regional cortical volumes and 34 cortical thicknesses were measured with automated image processing software at baseline. Twenty-one subjects converted from MCI to AD determined with the cognitive tests at baseline and 1 year later. The hippocampus, amygdala, and caudate volumes were significantly smaller in progressive MCI subjects than in controls and stable MCI subjects. The cortical volumes achieved higher predictive accuracy than did cognitive tests or cortical thickness. Combining the volumes, thicknesses, and cognitive tests did not improve the accuracy. The volume of amygdala and caudate were independent variables in predicting conversion from MCI to AD. We conclude that regional cortical volume measures are more powerful than those common cognitive tests we used in identifying AD patients at the very earliest stage of the disease.
Here we describe the AddNeuroMed multicenter magnetic resonance imaging (MRI) study for longitudinal assessment in Alzheimers disease (AD). The study is similar to a faux clinical trial and has been established to assess longitudinal MRI changes in AD, mild cognitive impairment (MCI), and healthy control subjects using an image acquisition protocol compatible with the Alzheimers Disease Neuroimaging Initiative (ADNI). The approach consists of a harmonized MRI acquisition protocol across centers, rigorous quality control, a central data analysis hub, and an automated image analysis pipeline. Comprehensive quality control measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline. A total of 378 subjects were recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the quality control criteria. Measurements of normalized whole brain volume, whole brain cortical thickness, and point-by-point group-based cortical thickness measurements, demonstrating the power of the automated image analysis techniques employed, are reported.
There is an urgent need for Alzheimers disease (AD) biomarkers-especially in the context of clinical trials. Biomarkers for early diagnosis, disease progression, and prediction are most critical, and disease-modification therapy development may depend on the discovery and validation of such markers. AddNeuroMed is a cross European, public/private consortium developed for AD biomarker discovery. We report here the development and design of AddNeuroMed and the progress toward the development of plasma markers. Despite the obstacles to such markers, we have identified a range of markers including CFH and A2M, both of which have been independently replicated. The experience of AddNeuroMed leads us to three overall conclusions. First, collaboration is essential. Second, design is paramount and combining modalities, such as imaging and proteomics, may be informative. Third, animal models are valuable in biomarker research. Most importantly, we have learned that plasma markers are feasible.
To study the ability of neuropsychological tests, manual MRI hippocampal volume measures, regional volume and cortical thickness measures to identify subjects with Alzheimers disease (AD), mild cognitive impairment (MCI), and healthy age-matched controls. Neuropsychological tests, manual hippocampal volume, automated regional volume and regional cortical thickness measures were performed in 120 AD patients, 120 MCI subjects, and 111 controls. The regional cortical thickness and volumes in MCI subjects were significantly decreased in limbic/paralimbic areas and temporal lobe compared to controls. Atrophy was much more extensive in the AD patients compared to MCI subjects and controls. The combination of neuropsychological tests and volumes revealed the highest accuracy (82% AD vs. MCI; 94% AD vs. control; 83% MCI vs. control). Adding regional cortical thicknesses into the discriminate analysis did not improve accuracy. We conclude that regional cortical thickness and volume measures provide a panoramic view of brain atrophy in AD and MCI subjects. A combination of neuropsychological tests and regional volumes are important when discriminating AD from healthy controls and MCI.
Responsiveness to cognitive behaviour therapy (CBT) in psychosis may have a neurological basis. This study aimed to determine whether improvement in symptoms following CBT for psychosis (CBTp) in people with schizophrenia is positively associated with pre-therapy grey matter volume in brain regions involved in cognitive processing.
Spherical deconvolution methods have been applied to diffusion MRI to improve diffusion tensor tractography results in brain regions with multiple fibre crossing. Recent developments, such as the introduction of non-negative constraints on the solution, allow a more accurate estimation of fibre orientations by reducing instability effects due to noise robustness. Standard convolution methods do not, however, adequately model the effects of partial volume from isotropic tissue, such as gray matter, or cerebrospinal fluid, which may degrade spherical deconvolution results. Here we use a newly developed spherical deconvolution algorithm based on an adaptive regularization (damped version of the Richardson-Lucy algorithm) to reduce isotropic partial volume effects. Results from both simulated and in vivo datasets show that, compared to a standard non-negative constrained algorithm, the damped Richardson-Lucy algorithm reduces spurious fibre orientations and preserves angular resolution of the main fibre orientations. These findings suggest that, in some brain regions, non-negative constraints alone may not be sufficient to reduce spurious fibre orientations. Considering both the speed of processing and the scan time required, this new method has the potential for better characterizing white matter anatomy and the integrity of pathological tissue.
Clinical studies of cell-based immunotherapies have included both patient-specific (autologous) and non-patient-specific (allogeneic) approaches. Major concerns in using allogeneic immunotherapies are that the induced immune responses may be predominantly directed against the allogeneic HLA molecules of the cellular immunotherapy and not against its potential tumor antigens and that only the allogeneic responses will be enhanced when the immunotherapies are combined with immune checkpoint regulators in an effort to enhance overall immunotherapy potency. To evaluate these possibilities, studies were performed using the GM-CSF-secreting B16F1 cell line as autologous immunotherapy (Auto) and the same cell line modified to over-express the MHC molecule K(d) to generate an immunotherapy that expresses an allogeneic component (Allo) when injected into C57/Bl6 mice. The goal was to compare the specific anti-tumor immune responses induced by these two immunotherapies, which share an identical antigen repertoire, with the exception of the allogeneic MHC class I molecule expressed by the Allo cells, and have identical GM-CSF-secretion levels. Both immunotherapies provided similar therapeutic benefit to tumor-bearing animals with a trend towards a more pronounced tumor growth delay in animals injected with the Allo immunotherapy. This correlated with a significant increase in the number of activated DCs and T-cells in the DLN of Allo-treated animals. In addition, persistent infiltration of effector CD8(+) T-cells was detected in the tumors of animals treated with the Allo immunotherapy, which correlated with a trend towards a greater antigen-specific T-cell response in these animals. When combined with the immune checkpoint regulator anti-PD-1, tumor-specific and allogeneic immune responses were equally enhanced. Thus, the ability of an allogeneic tumor cell immunotherapy to induce a therapeutic anti-tumor immune response is comparable, if not superior, to an autologous tumor cell immunotherapy and its anti-tumor potency can be enhanced when combined with immunomodulatory compounds.
Regions affected late in neurodegenerative disease are thought to be anatomically connected to regions affected earlier. The subcallosal medial prefrontal cortex (SMPC) has connections with the dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), and hippocampus (HC), which are regions that may become atrophic in frontotemporal lobar degeneration (FTLD) and Alzheimers disease (AD). We hypothesized that the SMPC is a common site of frontal atrophy in the FTLD subtypes and in AD. The volume of the SMPC, DLPFC, OFC, HC, and entorhinal cortex (EC) were manually delineated for 12 subjects with frontotemporal dementia (FTD), 13 with semantic dementia (SD), 9 with progressive nonfluent aphasia (PNFA), 10 AD cases, and 13 controls. Results revealed significant volume loss in the left SMPC in FTD, SD, and PNFA, while the right SMPC was also atrophied in SD and FTD. In AD a non significant tendency of volume loss in the left SMPC was found (p = 0.08), with no volume loss on the right side. Results indicated that volume loss reflected the degree of brain connectivity. In SD and AD temporal regions displayed most atrophy. Among the frontal regions, the SMPC (which receives the strongest temporal projections) demonstrated most volume loss, the OFC (which receives less temporal projections) less volume loss, while the DLPFC (which is at multisynaptic distance from the temporal regions) demonstrated no volume loss. In PNFA, the left SMPC was atrophic, possibly reflecting progression from the left anterior insula, while FTD patients may have had SMPC atrophy at the initial stages of the disease. Atrophy of the SMPC may thus be affected by either initial temporal or initial frontal atrophy, making it a common site of frontal atrophy in the dementia subtypes investigated.
Alzheimers Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven omics approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD.
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