Implementation of subjective cognitive decline criteria in research studies

INTRODUCTION

Subjective cognitive decline (SCD) manifesting before clinical impairment could serve as a target population for early intervention trials in Alzheimer's disease (AD). A working group, the Subjective Cognitive Decline Initiative (SCD-I), published SCD research criteria in the context of preclinical AD. To successfully apply them, a number of issues regarding assessment and implementation of SCD needed to be addressed.

METHODS

Members of the SCD-I met to identify and agree on topics relevant to SCD criteria operationalization in research settings. Initial ideas and recommendations were discussed with other SCD-I working group members and modified accordingly.

RESULTS

Topics included SCD inclusion and exclusion criteria, together with the informant's role in defining SCD presence and the impact of demographic factors.

DISCUSSION

Recommendations for the operationalization of SCD in differing research settings, with the aim of harmonization of SCD measurement across studies are proposed, to enhance comparability and generalizability across studies.

Multicenter stability of resting state fMRI in the detection of Alzheimer's disease and amnestic MCI

Background

In monocentric studies, patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia exhibited alterations of functional cortical connectivity in resting-state functional MRI (rs-fMRI) analyses. Multicenter studies provide access to large sample sizes, but rs-fMRI may be particularly sensitive to multiscanner effects.

Methods

We used data from five centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 367 cases, including AD patients, MCI patients and healthy older controls, to assess the influence of the distributed acquisition on the group effects. We calculated accuracy of group discrimination based on whole brain functional connectivity of the posterior cingulate cortex (PCC) using pooled samples as well as second-level analyses across site-specific group contrast maps.

Results

We found decreased functional connectivity in AD patients vs. controls, including clusters in the precuneus, inferior parietal cortex, lateral temporal cortex and medial prefrontal cortex. MCI subjects showed spatially similar, but less pronounced, differences in PCC connectivity when compared to controls. Group discrimination accuracy for AD vs. controls (MCI vs. controls) in the test data was below 76% (72%) based on the pooled analysis, and even lower based on the second level analysis stratified according to scanner. Only a subset of quality measures was useful to detect relevant scanner effects.

Conclusions

Multicenter rs-fMRI analysis needs to employ strict quality measures, including visual inspection of all the data, to avoid seriously confounded group effects. While pending further confirmation in biomarker stratified samples, these findings suggest that multicenter acquisition limits the use of rs-fMRI in AD and MCI diagnosis.

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Diagnostic accuracy of multicenter rs-fMRI in AD and MCI

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Quality metrics for multicenter rs-fMRI that should be used

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Quality metrics for multicenter rs-fMRI that should not be used

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Multicenter rs-fMRI will have limited diagnostic use in clinical routine diagnosis

Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD.

The effect of methylphenidate intake on brain structure in adults with ADHD in a placebo-controlled randomized trial

BACKGROUND

Based on animal research several authors have warned that the application of methylphenidate, the first-line drug for the treatment of attention-deficit/hyperactivity disorder (ADHD), might have neurotoxic effects potentially harming the brain. We investigated whether methylphenidate application, over a 1-year period, results in cerebral volume decrease.

METHODS

We acquired structural MRIs in a double-blind study comparing methylphenidate to placebo. Global and regional brain volumes were analyzed at baseline, after 3 months and after 12 months using diffeomorphic anatomic registration through exponentiated lie algebra.

RESULTS

We included 131 adult patients with ADHD into the baseline sample, 98 into the 3-month sample (54 in the methylphenidate cohort and 44 in the placebo cohort) and 76 into the 1-year sample (37 in the methylphenidate cohort and 29 in the placebo cohort). Methylphenidate intake compared with placebo did not lead to any detectable cerebral volume loss; there was a trend toward bilateral cerebellar grey matter increase.

LIMITATIONS

Detecting possible neurotoxic effects of methylphenidate might require a longer observation period.

CONCLUSION

There is no evidence of grey matter volume loss after 1 year of methylphenidate treatment in adult patients with ADHD.

Modulation of Total Sleep Time by Transcranial Direct Current Stimulation (tDCS)

Arousal and sleep are fundamental physiological processes, and their modulation is of high clinical significance. This study tested the hypothesis that total sleep time (TST) in humans can be modulated by the non-invasive brain stimulation technique transcranial direct current stimulation (tDCS) targeting a 'top-down' cortico-thalamic pathway of sleep-wake regulation. Nineteen healthy participants underwent a within-subject, repeated-measures protocol across five nights in the sleep laboratory with polysomnographic monitoring (adaptation, baseline, three experimental nights). tDCS was delivered via bi-frontal target electrodes and bi-parietal return electrodes before sleep (anodal 'activation', cathodal 'deactivation', and sham stimulation). Bi-frontal anodal stimulation significantly decreased TST, compared with cathodal and sham stimulation. This effect was location specific. Bi-frontal cathodal stimulation did not significantly increase TST, potentially due to ceiling effects in good sleepers. Exploratory resting-state EEG analyses before and after the tDCS protocols were consistent with the notion of increased cortical arousal after anodal stimulation and decreased cortical arousal after cathodal stimulation. The study provides proof-of-concept that TST can be decreased by non-invasive bi-frontal anodal tDCS in healthy humans. Further elucidating the 'top-down' pathway of sleep-wake regulation is expected to increase knowledge on the fundamentals of sleep-wake regulation and to contribute to the development of novel treatments for clinical conditions of disturbed arousal and sleep.

Natural variation in sensory-motor white matter organization influences manifestations of Huntington's disease

While the HTT CAG-repeat expansion mutation causing Huntington's disease (HD) is highly correlated with the rate of pathogenesis leading to disease onset, considerable variance in age-at-onset remains unexplained. Therefore, other factors must influence the pathogenic process. We asked whether these factors were related to natural biological variation in the sensory-motor system. In 243 participants (96 premanifest and 35 manifest HD; 112 controls), sensory-motor structural MRI, tractography, resting-state fMRI, electrophysiology (including SEP amplitudes), motor score ratings, and grip force as sensory-motor performance were measured. Following individual modality analyses, we used principal component analysis (PCA) to identify patterns associated with sensory-motor performance, and manifest versus premanifest HD discrimination. We did not detect longitudinal differences over 12 months. PCA showed a pattern of loss of caudate, grey and white matter volume, cortical thickness in premotor and sensory cortex, and disturbed diffusivity in sensory-motor white matter tracts that was connected to CAG repeat length. Two further major principal components appeared in controls and HD individuals indicating that they represent natural biological variation unconnected to the HD mutation. One of these components did not influence HD while the other non-CAG-driven component of axial versus radial diffusivity contrast in white matter tracts were associated with sensory-motor performance and manifest HD. The first component reflects the expected CAG expansion effects on HD pathogenesis. One non-CAG-driven component reveals an independent influence on pathogenesis of biological variation in white matter tracts and merits further investigation to delineate the underlying mechanism and the potential it offers for disease modification. Hum Brain Mapp 37:4615-4628, 2016. © 2016 Wiley Periodicals, Inc.

Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study

Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC >  0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies

Basal Forebrain and Hippocampus as Predictors of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment - A Multicenter DTI and Volumetry Study

BACKGROUND

Hippocampal grey matter (GM) atrophy predicts conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Pilot data suggests that mean diffusivity (MD) in the hippocampus, as measured with diffusion tensor imaging (DTI), may be a more accurate predictor of conversion than hippocampus volume. In addition, previous studies suggest that volume of the cholinergic basal forebrain may reach a diagnostic accuracy superior to hippocampal volume in MCI.

OBJECTIVE

The present study investigated whether increased MD and decreased volume of the hippocampus, the basal forebrain and other AD-typical regions predicted time to conversion from MCI to AD dementia.

METHODS

79 MCI patients with DTI and T1-weighted magnetic resonance imaging (MRI) were retrospectively included from the European DTI Study in Dementia (EDSD) dataset. Of these participants, 35 converted to AD dementia after 6-46 months (mean: 21 months). We used Cox regression to estimate the relative conversion risk predicted by MD values and GM volumes, controlling for age, gender, education and center.

RESULTS

Decreased GM volume in all investigated regions predicted an increased risk for conversion. Additionally, increased MD in the right basal forebrain predicted increased conversion risk. Reduced volume of the right hippocampus was the only significant predictor in a stepwise model combining all predictor variables.

CONCLUSION

Volume reduction of the hippocampus, the basal forebrain and other AD-related regions was predictive of increased risk for conversion from MCI to AD. In this study, volume was superior to MD in predicting conversion.

Contribution of the Cholinergic System to Verbal Memory Performance in Mild Cognitive Impairment

Acetylcholine is critically involved in modulating learning and memory function, which both decline in neurodegeneration. It remains unclear to what extent structural and functional changes in the cholinergic system contribute to episodic memory dysfunction in mild cognitive impairment (MCI), in addition to hippocampal degeneration. A better understanding is critical, given that the cholinergic system is the main target of current symptomatic treatment in mild to moderate Alzheimer's disease. We simultaneously assessed the structural and functional integrity of the cholinergic system in 20 patients with MCI and 20 matched healthy controls and examined their effect on verbal episodic memory via multivariate regression analyses. Mediating effects of either cholinergic function or hippocampal volume on the relationship between cholinergic structure and episodic memory were computed. In MCI, a less intact structure and function of the cholinergic system was found. A smaller cholinergic structure was significantly correlated with a functionally more active cholinergic system in patients, but not in controls. This association was not modulated by age or disease severity, arguing against compensational processes. Further analyses indicated that neither functional nor structural changes in the cholinergic system influence verbal episodic memory at the MCI stage. In fact, those associations were fully mediated by hippocampal volume. Although the cholinergic system is structurally and functionally altered in MCI, episodic memory dysfunction results primarily from hippocampal neurodegeneration, which may explain the inefficiency of cholinergic treatment at this disease stage.

Effects of Different Analysis Strategies on Paired Associative Stimulation. A Pooled Data Analysis from Three Research Labs

Paired associative stimulation (PAS) is a widely used transcranial magnetic stimulation (TMS) paradigm to non-invasively induce synaptic plasticity in the human brain in vivo. Altered PAS-induced plasticity has been demonstrated for several diseases. However, researchers are faced with a high inter- and intra-subject variability of the PAS response. Here, we pooled original data from nine PAS studies from three centers and analyzed the combined dataset of 190 healthy subjects with regard to age dependency, the role of stimulation parameters and the effect of different statistical methods. We observed no main effect of the PAS intervention over all studies (F(2;362) = 0.44; p = 0.644). The rate of subjects showing the expected increase of motor evoked potential (MEP) amplitudes was 53%. The PAS effect differed significantly between studies as shown by a significant interaction effect (F(16;362) = 1.77; p = 0.034) but post-hoc testing did not reveal significant effects after correction for multiple tests. There was a trend toward increased variability of the PAS effect in older subjects. Acquisition parameters differed across studies but without systematically influencing changes in MEP-size. The use of post/baseline quotients systematically indicated stronger PAS effects than post/baseline difference or the logarithm of the post/baseline quotient. The non-significant PAS effects across studies and a wide range of responder rates between studies indicate a high variability of this method. We were thus not able to replicate findings from a previous meta-analysis showing robust effects of PAS. No pattern emerged regarding acquisition parameters that at this point could guide future studies to reduce variability and help increase response rate. For future studies, we propose to report the responder rate and recommend the use of the logarithmized post/baseline quotient for further analyses to better address the possibility that results are driven by few extreme cases.

The European DTI Study on Dementia - A multicenter DTI and MRI study on Alzheimer's disease and Mild Cognitive Impairment

The European DTI Study on Dementia (EDSD) is a multicenter framework created to study the diagnostic accuracy and inter-site variability of DTI-derived markers in patients with manifest and prodromal Alzheimer's disease (AD). The dynamically growing database presently includes 493 DTI, 512 T1-weighted MRI, and 300 FLAIR scans from patients with AD dementia, patients with Mild Cognitive Impairment (MCI) and matched Healthy Controls, acquired on 13 different scanner platforms. The imaging data is publicly available, along with the subjects' demographic and clinical characterization. Detailed neuropsychological information, cerebrospinal fluid information on biomarkers and clinical follow-up diagnoses are included for a subset of subjects. This paper describes the rationale and structure of the EDSD, summarizes the available data, and explains how to gain access to the database. The EDSD is a useful database for researchers seeking to investigate the contribution of DTI to dementia diagnostics.

Top-down control of arousal and sleep: Fundamentals and clinical implications

Mammalian sleep emerges from attenuated activity in the ascending reticular arousal system (ARAS), the main arousal network of the brain. This system originates in the brainstem and activates the thalamus and cortex during wakefulness via a well-characterized 'bottom-up' pathway. Recent studies propose that a less investigated cortico-thalamic 'top-down' pathway also regulates sleep. The present work integrates the current evidence on sleep regulation with a focus on the 'top-down' pathway and explores the potential to translate this information into clinically relevant interventions. Specifically, we elaborate the concept that arousal and sleep continuity in humans can be modulated by non-invasive brain stimulation (NIBS) techniques that increase or decrease cortical excitability. Based on preclinical studies, the modulatory effects of the stimulation are thought to extend to subcortical arousal networks. Further exploration of the 'top-down' regulation of sleep and its modulation through non-invasive brain stimulation techniques may contribute to the development of novel treatments for clinical conditions of disrupted arousal and sleep, which are among the major health problems worldwide.

Large-scale brain network abnormalities in Huntington's disease revealed by structural covariance

Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.

Detection of Motor Changes in Huntington's Disease Using Dynamic Causal Modeling

Deficits in motor functioning are one of the hallmarks of Huntington's disease (HD), a genetically caused neurodegenerative disorder. We applied functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess changes that occur with disease progression in the neural circuitry of key areas associated with executive and cognitive aspects of motor control. Seventy-seven healthy controls, 62 pre-symptomatic HD gene carriers (preHD), and 16 patients with manifest HD symptoms (earlyHD) performed a motor finger-tapping fMRI task with systematically varying speed and complexity. DCM was used to assess the causal interactions among seven pre-defined regions of interest, comprising primary motor cortex, supplementary motor area (SMA), dorsal premotor cortex, and superior parietal cortex. To capture heterogeneity among HD gene carriers, DCM parameters were entered into a hierarchical cluster analysis using Ward's method and squared Euclidian distance as a measure of similarity. After applying Bonferroni correction for the number of tests, DCM analysis revealed a group difference that was not present in the conventional fMRI analysis. We found an inhibitory effect of complexity on the connection from parietal to premotor areas in preHD, which became excitatory in earlyHD and correlated with putamen atrophy. While speed of finger movements did not modulate the connection from caudal to pre-SMA in controls and preHD, this connection became strongly negative in earlyHD. This second effect did not survive correction for multiple comparisons. Hierarchical clustering separated the gene mutation carriers into three clusters that also differed significantly between these two connections and thereby confirmed their relevance. DCM proved useful in identifying group differences that would have remained undetected by standard analyses and may aid in the investigation of between-subject heterogeneity.

Compensation in Preclinical Huntington's Disease: Evidence From the Track-On HD Study

BACKGROUND

Cognitive and motor task performance in premanifest Huntington's disease (HD) gene-carriers is often within normal ranges prior to clinical diagnosis, despite loss of brain volume in regions involved in these tasks. This indicates ongoing compensation, with the brain maintaining function in the presence of neuronal loss. However, thus far, compensatory processes in HD have not been modeled explicitly. Using a new model, which incorporates individual variability related to structural change and behavior, we sought to identify functional correlates of compensation in premanifest-HD gene-carriers.

METHODS

We investigated the modulatory effects of regional brain atrophy, indexed by structural measures of disease load, on the relationship between performance and brain activity (or connectivity) using task-based and resting-state functional MRI.

FINDINGS

Consistent with compensation, as atrophy increased performance-related activity increased in the right parietal cortex during a working memory task. Similarly, increased functional coupling between the right dorsolateral prefrontal cortex and a left hemisphere network in the resting-state predicted better cognitive performance as atrophy increased. Such patterns were not detectable for the left hemisphere or for motor tasks.

INTERPRETATION

Our findings provide evidence for active compensatory processes in premanifest-HD for cognitive demands and suggest a higher vulnerability of the left hemisphere to the effects of regional atrophy.

Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing

The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group).

The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation.

These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

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The ARMS was accurately identified based on an individual patient's response within a WM network.

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Regional cortical activations were particularly important for group separation.

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Based on WM alterations, FEP and HC or FEP and ARMS could not be accurately separated in small samples.