Basal forebrain volume reliably predicts the cortical spread of Alzheimer\u27s degeneration

© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. Alzheimer\u27s disease neurodegeneration is thought to spread across anatomically and functionally connected brain regions. However, the precise sequence of spread remains ambiguous. The prevailing model used to guide in vivo human neuroimaging and non-human animal research assumes that Alzheimer\u27s degeneration starts in the entorhinal cortices, before spreading to the temporoparietal cortex. Challenging this model, we previously provided evidence that in vivo markers of neurodegeneration within the nucleus basalis of Meynert (NbM), a subregion of the basal forebrain heavily populated by cortically projecting cholinergic neurons, precedes and predicts entorhinal degeneration. There have been few systematic attempts at directly comparing staging models using in vivo longitudinal biomarker data, and none to our knowledge testing if comparative evidence generalizes across independent samples. Here we addressed the sequence of pathological staging in Alzheimer\u27s disease using two independent samples of the Alzheimer\u27s Disease Neuroimaging Initiative (n1 = 284; n2 = 553) with harmonized CSF assays of amyloid-b and hyperphosphorylated tau (pTau), and longitudinal structural MRI data over 2 years. We derived measures of grey matter degeneration in a priori NbM and the entorhinal cortical regions of interest. To examine the spreading of degeneration, we used a predictive modelling strategy that tests whether baseline grey matter volume in a seed region accounts for longitudinal change in a target region. We demonstrated that predictive spread favoured the NbM!entorhinal over the entorhinal!NbM model. This evidence generalized across the independent samples. We also showed that CSF concentrations of pTau/amyloid-b moderated the observed predictive relationship, consistent with evidence in rodent models of an underlying trans-synaptic mechanism of pathophysiological spread. The moderating effect of CSF was robust to additional factors, including clinical diagnosis. We then applied our predictive modelling strategy to an exploratory whole-brain voxel-wise analysis to examine the spatial specificity of the NbM!entorhinal model. We found that smaller baseline NbM volumes predicted greater degeneration in localized regions of the entorhinal and perirhinal cortices. By contrast, smaller baseline entorhinal volumes predicted degeneration in the medial temporal cortex, recapitulating a prior influential staging model. Our findings suggest that degeneration of the basal forebrain cholinergic projection system is a robust and reliable upstream event of entorhinal and neocortical degeneration, calling into question a prevailing view of Alzheimer\u27s disease pathogenesis

Free Water in White Matter Differentiates Mci and ad From Control Subjects

Recent evidence shows that neuroinflammation plays a role in many neurological diseases including mild cognitive impairment (MCI) and Alzheimer\u27s disease (AD), and that free water (FW) modeling from clinically acquired diffusion MRI (DTI-like acquisitions) can be sensitive to this phenomenon. This FW index measures the fraction of the diffusion signal explained by isotropically unconstrained water, as estimated from a bi-tensor model. In this study, we developed a simple but powerful whole-brain FW measure designed for easy translation to clinical settings and potential use as a priori outcome measure in clinical trials. These simple FW measures use a safe white matter (WM) mask without gray matter (GM)/CSF partial volume contamination

The effect of amyloid pathology and glucose metabolism on cortical volume loss over time in Alzheimer’s disease

Purpose: The present multimodal neuroimaging study examined whether amyloid pathology and glucose metabolism are related to cortical volume loss over time in Alzheimer’s disease (AD) patients and healthy elderly controls. Methods: Structural MRI scans of eleven AD patients and ten controls were available at baseline and follow-up (mean interval 2.5 years). Change in brain structure over time was defined as percent change of cortical volume within seven a-priori defined regions that typically show the strongest structural loss in AD. In addition, two PET scans were performed at baseline: [[superscript 11]C]PIB to assess amyloid-β plaque load and [[superscript 18]F]FDG to assess glucose metabolism. [[superscript 11]C]PIB binding and [[superscript 18]F]FDG uptake were measured in the precuneus, a region in which both amyloid deposition and glucose hypometabolism occur early in the course of AD. Results: While amyloid-β plaque load at baseline was not related to cortical volume loss over time in either group, glucose metabolism within the group of AD patients was significantly related to volume loss over time (rho=0.56, p<0.05). Conclusion:The present study shows that in a group of AD patients amyloid-β plaque load as measured by [[superscript 11]C]PIB behaves as a trait marker (i.e., all AD patients showed elevated levels of amyloid, not related to subsequent disease course), whilst hypometabolism as measured by [[superscript 18]F]FDG changed over time indicating that it could serve as a state marker that is predictive of neurodegeneration.Hersenstichting Nederland (KS2011(1)-24)Athinoula A. Martinos Center for Biomedical ImagingInternationale Stichting Alzheimer Onderzoek (Project Number 11539

Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks

Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network

Cortico–Cortical Connections of Primary Sensory Areas and Associated Symptoms in Migraine

Abstract Migraine is a recurring, episodic neurological disorder characterized by headache, nausea, vomiting, and sensory disturbances. These events are thought to arise from the activation and sensitization of neurons along the trigemino–vascular pathway. From animal studies, it is known that thalamocortical projections play an important role in the transmission of nociceptive signals from the meninges to the cortex. However, little is currently known about the potential involvement of cortico–cortical feedback projections from higher-order multisensory areas and/or feedforward projections from principle primary sensory areas or subcortical structures. In a large cohort of human migraine patients (N = 40) and matched healthy control subjects (N = 40), we used resting-state intrinsic functional connectivity to examine the cortical networks associated with the three main sensory perceptual modalities of vision, audition, and somatosensation. Specifically, we sought to explore the complexity of the sensory networks as they converge and become functionally coupled in multimodal systems. We also compared self-reported retrospective migraine symptoms in the same patients, examining the prevalence of sensory symptoms across the different phases of the migraine cycle. Our results show widespread and persistent disturbances in the perceptions of multiple sensory modalities. Consistent with this observation, we discovered that primary sensory areas maintain local functional connectivity but express impaired long-range connections to higher-order association areas (including regions of the default mode and salience network). We speculate that cortico–cortical interactions are necessary for the integration of information within and across the sensory modalities and, thus, could play an important role in the initiation of migraine and/or the development of its associated symptoms

Peripheral electrical stimulation in Alzheimer's Disease: A randomized controlled trial on cognition and behavior

In a number of studies, peripheral electrical nerve stimulation has been applied to Alzheimer's disease (AD) patients who lived in a nursing home. Improvements were observed in memory, verbal fluency, affective behavior, activities of daily living and on the rest-activity rhythm and pupillary light reflex. The aim of the present, randomized, placebo-controlled, parallel-group clinical trial was to examine the effects of electrical stimulation on cognition and behavior in AD patients who still live at home. Repeated measures analyses of variance revealed no effects of the intervention in the verum group (n = 32) compared with the placebo group (n = 30) on any of the cognitive and behavioral outcome measures. However, the majority of the patients and the caregivers evaluated the treatment procedure positively, and applying the daily treatment at home caused minimal burden. The lack of treatment effects calls for reconsideration of electrical stimulation as a symptomatic treatment in A

Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden

Disruption of functional connectivity between brain regions may represent an early functional consequence of β-amyloid pathology prior to clinical Alzheimer's disease. We aimed to investigate if non-demented older individuals with increased amyloid burden demonstrate disruptions of functional whole-brain connectivity in cortical hubs (brain regions typically highly connected to multiple other brain areas) and if these disruptions are associated with neuronal dysfunction as measured with fluorodeoxyglucose-positron emission tomography. In healthy subjects without cognitive symptoms and patients with mild cognitive impairment, we used positron emission tomography to assess amyloid burden and cerebral glucose metabolism, structural magnetic resonance imaging to quantify atrophy and novel resting state functional magnetic resonance imaging processing methods to calculate whole-brain connectivity. Significant disruptions of whole-brain connectivity were found in amyloid-positive patients with mild cognitive impairment in typical cortical hubs (posterior cingulate cortex/precuneus), strongly overlapping with regional hypometabolism. Subtle connectivity disruptions and hypometabolism were already present in amyloid-positive asymptomatic subjects. Voxel-based morphometry measures indicate that these findings were not solely a consequence of regional atrophy. Whole-brain connectivity values and metabolism showed a positive correlation with each other and a negative correlation with amyloid burden. These results indicate that disruption of functional connectivity and hypometabolism may represent early functional consequences of emerging molecular Alzheimer's disease pathology, evolving prior to clinical onset of dementia. The spatial overlap between hypometabolism and disruption of connectivity in cortical hubs points to a particular susceptibility of these regions to early Alzheimer's-type neurodegeneration and may reflect a link between synaptic dysfunction and functional disconnection

Frequency-Dependent Relationship Between Resting-State Functional Magnetic Resonance Imaging Signal Power and Head Motion Is Localized Within Distributed Association Networks

Abstract Recent studies have highlighted the importance of analyzing spectral power in resting-state functional magnetic resonance imaging (rs-fMRI) data. Significant modulation of power has been ascribed to the performance of cognitive tasks and has been ascribed clinical significance. However, the role of confounding factors such as head motion on spectral power is not fully understood. Specifically, the spatial distribution of frequency-dependent associations between rs-fMRI power and motion is unknown. We utilized a large rs-fMRI dataset (n = 1000) to quantify the influence of head motion on spectral power in different frequency bands. We (1) performed regression analyses across the entire sample and (2) computed difference maps between high-and low-motion groups, more consistent with common experimental designs, and both analyses gave similar results. Greater head motion led to reduced spectral power at lower frequencies (0.007-0.05 Hz), but increased power at higher frequencies (0.12-0.167 Hz). Importantly, our whole-brain voxel-wise analysis showed that brain areas in distributed association networks (e.g., default mode and frontoparietal control networks) were most susceptible to head motion. These results were consistent with or without global signal regression (GSR). Additionally, without GSR, we noted a positive correlation with low-frequency power in the pre-and postcentral gyrus (S1/M1), mid-cingulate cortex, and insula and a negative correlation with mid-frequency (0.05-0.12 Hz) power in S1/M1, visual, and lateral temporal cortices. Hence, head motion significantly affects rs-fMRI power and great care must be taken when assigning a diagnostic marker for clinical populations known to present with greater head motion

Exploring functional connectivity in fMRI via clustering

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the k-means and spectral clustering algorithms as alternatives to the commonly used seed-based analysis. To enable clustering of the entire brain volume, we use the Nystrom Method to approximate the necessary spectral decompositions. We apply k-means, spectral clustering and seed-based analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via seed-based analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.National Alliance for Medical Image Computing (U.S.) (NIH NIBIB NAMIC U54-EB005149)Neuroimaging Analysis Center (U.S.) (NIH NCRR NAC P41-RR13218)National Science Foundation (U.S.) (CAREER grant 0642971)Howard Hughes Medical InstituteNational Defense Science and Engineering Graduate Fellowshi

Less head motion during MRI under task than resting-state conditions

Head motion reduces data quality of neuroimaging data. In three functional magnetic resonance imaging (MRI) experiments we demonstrate that people make less head movements under task than resting-state conditions. In Experiment 1, we observed less head motion during a memory encoding task than during the resting-state condition. In Experiment 2, using publicly shared data from the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study, we again found less head motion during several active task conditions than during a resting-state condition, although some task conditions also showed comparable motion. In the healthy controls, we found more head motion in men than in women and more motion with increasing age. When comparing clinical groups, we found that patients with a clinical diagnosis of bipolar disorder, or schizophrenia, move more compared to healthy controls or patients with ADHD. Both these experiments had a fixed acquisition order across participants, and we could not rule out that a first or last scan during a session might be particularly prone to more head motion. Therefore, we conducted Experiment 3, in which we collected several task and resting-state fMRI runs with an acquisition order counter-balanced. The results of Experiment 3 show again less head motion during several task conditions than during rest. Together these experiments demonstrate that small head motions occur during MRI even with careful instruction to remain still and fixation with foam pillows, but that head motion is lower when participants are engaged in a cognitive task. These finding may inform the choice of functional runs when studying difficult-to-scan populations, such as children or certain patient populations. Our findings also indicate that differences in head motion complicate direct comparisons of measures of functional neuronal networks between task and resting-state fMRI because of potential differences in data quality. In practice, a task to reduce head motion might be especially useful when acquiring structural MRI data such as T1/T2-weighted and diffusion MRI in research and clinical settings