Diederick Stoffers

Contrasting gray and white matter changes in preclinical Huntington disease An MRI study

Background: In Huntington disease (HD), substantial striatal atrophy precedes clinical motor symptoms. Accordingly, neuroprotection should prevent major cell loss before such symptoms arise. To evaluate neuroprotection, biomarkers such as MRI measures are needed. This requires first establishing the best imaging approach. Methods: Using a cross-sectional design, we acquired T1-weighted and diffusion-weighted scans in 39 preclinical (pre-HD) individuals and 25 age-matched controls. T1-weighted scans were analyzed with gross whole-brain segmentation and voxel-based morphometry. Analysis of diffusion-weighted scans used skeleton-based tractography. For all imaging measures, we compared pre-HD and control groups and within the pre-HD group we examined correlations with estimated years to clinical onset. Results: Pre-HD individuals had lower gross gray matter (GM) and white matter (WM) volume. Voxel-wise analysis demonstrated local GM volume loss, most notably in regions consistent with basal ganglia–thalamocortical pathways. By contrast, pre-HD individuals showed widespread reductions in WM integrity, probably due to a loss of axonal barriers. Both GM and WM imaging measures correlated with estimated years to onset. Conclusions: Using automated, observer-independent methods, we found that GM loss in pre-HD was regionally specific, while WM deterioration was much more general and probably the result of demyelination rather then axonal degeneration. These findings provide important information about the nature, relative staging, and topographic specificity of brain changes in pre-HD and suggest that combining GM and WM imaging may be the best biomarker approach. The empirically derived group difference images from this study are provided as regions-of-interest masks for improved sensitivity in future longitudinal studies.

The Amsterdam resting-state questionnaire reveals multiple phenotypes of resting-state cognition.

Resting-state neuroimaging is a dominant paradigm for studying brain function in health and disease. It is attractive for clinical research because of its simplicity for patients, straightforward standardization, and sensitivity to brain disorders. Importantly, non-sensory experiences like mind wandering may arise from ongoing brain activity. However, little is known about the link between ongoing brain activity and cognition, as phenotypes of resting-state cognition—and tools to quantify them—have been lacking. To facilitate rapid and structured measurements of resting-state cognition we developed a 50-item self-report survey, the Amsterdam Resting-State Questionnaire (ARSQ). Based on ARSQ data from 813 participants assessed after 5 min eyes-closed rest in their home, we identified seven dimensions of resting-state cognition using factor analysis: Discontinuity of Mind, Theory of Mind, Self, Planning, Sleepiness, Comfort, and Somatic Awareness. Further, we showed that the structure of cognition was similar during resting-state fMRI and EEG, and that the test-retest correlations were remarkably high for all dimensions. To explore whether inter-individual variation of resting-state cognition is related to health status, we correlated ARSQ-derived factor scores with psychometric scales measuring depression, anxiety, and sleep quality. Mental health correlated positively with Comfort and negatively with Discontinuity of Mind. Finally, we show that sleepiness may partially explain a resting-state EEG profile previously associated with Alzheimers disease. These findings indicate that the ARSQ readily provides information about cognitive phenotypes and that it is a promising tool for research on the neural correlates of resting-state cognition in health and disease.

Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques

The development of MRI measures as biomarkers for neurodegenerative disease could prove extremely valuable for the assessment of neuroprotective therapies. Much current research is aimed at developing such biomarkers for use in people who are gene-positive for Huntingtons disease yet exhibit few or no clinical symptoms of the disease (pre-HD). We acquired structural (T1), diffusion weighted and functional MRI (fMRI) data from 39 pre-HD volunteers and 25 age-matched controls. To determine whether it was possible to decode information about disease state from neuroimaging data, we applied multivariate pattern analysis techniques to several derived voxel-based and segmented region-based datasets. We found that different measures of structural, diffusion weighted, and functional MRI could successfully classify pre-HD and controls using support vector machines (SVM) and linear discriminant analysis (LDA) with up to 76% accuracy. The model producing the highest classification accuracy used LDA with a set of six volume measures from the basal ganglia. Furthermore, using support vector regression (SVR) and linear regression models, we were able to generate quantitative measures of disease progression that were significantly correlated with established measures of disease progression (estimated years to clinical onset, derived from age and genetic information) from several different neuroimaging measures. The best performing regression models used SVR with neuroimaging data from regions within the grey matter (caudate), white matter (corticospinal tract), and fMRI (insular cortex). These results highlight the utility of machine learning analyses in addition to conventional ones. We have shown that several neuroimaging measures contain multivariate patterns of information that are useful for the development of disease-state biomarkers for HD.

Basal Ganglia Atrophy in Prodromal Huntington's Disease Is Detectable Over One Year Using Automated Segmentation

Future clinical trials of neuroprotection in prodromal Huntingtons (known as preHD) will require sensitive in vivo imaging biomarkers to track disease progression over the shortest period. Since basal ganglia atrophy is the most prominent structural characteristic of Huntingtons pathology, systematic assessment of longitudinal subcortical atrophy holds great potential for future biomarker development. We studied 36 preHD and 22 age‐matched controls using a novel method to quantify regional change from T1‐weighted structural images acquired 1 year apart. We assessed cross‐sectional volume differences and longitudinal volumetric change in 7 subcortical structures—the accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. At baseline, accumbens, caudate, pallidum, and putamen volumes were reduced in preHD versus controls (all P < .01). Longitudinally, atrophy was greater in preHD than controls in the caudate, pallidum, and putamen (all P < .01). Each structure showed a large between‐group effect size, especially the pallidum where Cohens d was 1.21. Using pallidal atrophy as a biomarker, we estimate that a hypothetical 1‐year neuroprotection study would require only 35 preHD per arm to detect a 50% slowing in atrophy and only 138 preHD per arm to detect a 25% slowing in atrophy. The effect sizes calculated for preHD basal ganglia atrophy over 1 year are some of the largest reported to date. Consequently, this translates to strikingly small sample size estimates that will greatly facilitate any future neuroprotection study. This underscores the utility of this automatic image segmentation and longitudinal nonlinear registration method for upcoming studies of preHD and other neurodegenerative disorders.

The ARSQ 2.0 reveals age and personality effects on mind-wandering experiences

The human brain frequently generates thoughts and feelings detached from environmental demands. Investigating the rich repertoire of these mind-wandering experiences is challenging, as it depends on introspection and mapping its content requires an unknown number of dimensions. We recently developed a retrospective self-report questionnaire—the Amsterdam Resting-State Questionnaire (ARSQ)—which quantifies mind wandering along seven dimensions: “Discontinuity of Mind,” “Theory of Mind,” “Self,” “Planning,” “Sleepiness,” “Comfort,” and “Somatic Awareness.” Here, we show using confirmatory factor analysis that the ARSQ can be simplified by standardizing the number of items per factor and extending it to a 10-dimensional model, adding “Health Concern,” “Visual Thought,” and “Verbal Thought.” We will refer to this extended ARSQ as the “ARSQ 2.0.” Testing for effects of age and gender revealed no main effect for gender, yet a moderate and significant negative effect for age on the dimensions of “Self,” “Planning,” and “Visual Thought.” Interestingly, we observed stable and significant test-retest correlations across measurement intervals of 3–32 months except for “Sleepiness” and “Health Concern.” To investigate whether this stability could be related to personality traits, we correlated ARSQ scores to proxy measures of Cloningers Temperament and Character Inventory, revealing multiple significant associations for the trait “Self-Directedness.” Other traits correlated to specific ARSQ dimensions, e.g., a negative association between “Harm Avoidance” and “Comfort.” Together, our results suggest that the ARSQ 2.0 is a promising instrument for quantitative studies on mind wandering and its relation to other psychological or physiological phenomena.

Automated structural imaging analysis detects premanifest Huntington's disease neurodegeneration within 1 year.

Intense efforts are underway to evaluate neuroimaging measures as biomarkers for neurodegeneration in premanifest Huntingtons disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1‐year longitudinal design, we analyzed T1‐weighted structural scans in 35 preHD individuals and 22 age‐matched controls. We used the SIENA (Structural Image Evaluation, using Normalization, of Atrophy) software tool to yield overall percentage brain volume change (PBVC) and voxel‐level changes in atrophy. We calculated sample sizes for a hypothetical disease‐modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, −0.149%; preHD, −0.388%; P = .031, Cohens d = .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close‐to‐onset preHD, −0.510%; P = .019, Cohens d = .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well‐established preHD basal ganglia atrophy. We estimated that a neuroprotection study using SIENA would only need 74 close‐to‐onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole‐brain analysis of structural MRI can reliably detect preHD disease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD.

Wake High-Density Electroencephalographic Spatiospectral Signatures of Insomnia.

STUDY OBJECTIVESnAlthough daytime complaints are a defining characteristic of insomnia, most EEG studies evaluated sleep only. We used high-density electroencephalography to investigate wake resting state oscillations characteristic of insomnia disorder (ID) at a fine-grained spatiospectral resolution.nnnMETHODSnA case-control assessment during eyes open (EO) and eyes closed (EC) was performed in a laboratory for human physiology. Participants (n = 94, 74 female, 21-70 y) were recruited through www.sleepregistry.nl: 51 with ID, according to DSM-5 and 43 matched controls. Exclusion criteria were any somatic, neurological or psychiatric condition. Group differences in the spectral power topographies across multiple frequencies (1.5 to 40 Hz) were evaluated using permutation-based inference with Threshold-Free Cluster-Enhancement, to correct for multiple comparisons.nnnRESULTSnAs compared to controls, participants with ID showed less power in a narrow upper alpha band (11-12.7 Hz, peak: 11.7 Hz) over bilateral frontal and left temporal regions during EO, and more power in a broad beta frequency range (16.3-40 Hz, peak: 19 Hz) globally during EC. Source estimates suggested global rather than cortically localized group differences.nnnCONCLUSIONSnThe widespread high power in a broad beta band reported previously during sleep in insomnia is present as well during eyes closed wakefulness, suggestive of a round-the-clock hyperarousal. Low power in the upper alpha band during eyes open is consistent with low cortical inhibition and attentional filtering. The fine-grained HD-EEG findings suggest that, while more feasible than PSG, wake EEG of short duration with a few well-chosen electrodes and frequency bands, can provide valuable features of insomnia.

Sex differences in gray and white matter structure in age-matched unrelated males and females and opposite-sex siblings

Apart from the general finding of larger global brain volumes in men, neuroimaging studies that compared brain structure between men and women have yielded some inconsistencies with regard to regional differences. One confound when comparing men and women may be differences in their genetic and or family background. A design that addresses such confounds compares brain structures between brothers and sisters, who share their genetic and family background. In the present study, we aimed to contribute to the existing literature on structural brain sex differences by comparing regional gray and white matter volume, using voxel based morphometry (VBM); and white matter microstructure, using tract-based spatial statistics (TBSS), between 40 unrelated males and females, and contrasting the results with those obtained in a group of 47 opposite-sex siblings, including 42 dizygotic opposite-sex (DOS) twin pairs.” Our results showed that men had larger global brain volumes as well as higher mean fractional anisotropy across the brain and showed regionally enlarged gray matter volume and higher fractional anisotropy in, or surrounding, subcortical structures (hypothalamus, thalamus, putamen and globus pallidus and rostral midbrain). Increased gray matter volume in women was restricted to areas of the cortex, including inferior temporal, insular, cingulate, precentral and frontal/prefrontal regions. These sex differences were generally consistent between the unrelated male-female pairs and the opposite-sex sibling pairs. Therefore, we conclude that these sex differences are not the result of confounding differences in genetic or family background and that the etiology of these sex differences merits further investigation.

Increased hippocampal-prefrontal functional connectivity in insomnia

HighlightsHigh hippocampal functional connectivity with left middle frontal gyrus in insomnia.Insomnia severity increases with strength of hippocampal‐MFG connectivity.Sleep efficiency decreases with strength of hippocampal‐MFG connectivity.No differences in hippocampal volume between people with insomnia and controls. Abstract Insomnia Disorder (ID) is the second‐most common mental disorder and has a far‐reaching impact on daytime functioning. A meta‐analysis indicates that, of all cognitive domains, declarative memory involving the hippocampus is most affected in insomnia. Hippocampal functioning has consistently been shown to be sensitive to experimental sleep deprivation. Insomnia however differs from sleep deprivation in many aspects, and findings on hippocampal structure and function have been equivocal. The present study used both structural and resting‐state functional Magnetic Resonance Imaging in a larger sample than previously reported to evaluate hippocampal volume and functional connectivity in ID. Included were 65 ID patients (mean age = 48.3 y ± 14.0, 17 males) and 65 good sleepers (mean age = 44.1 y ± 15.2, 23 males). Insomnia severity was assessed with the Insomnia Severity Index (ISI), subjective sleep with the Consensus Sleep Diary (CSD) and objective sleep by two nights of polysomnography (PSG). Seed‐based analysis showed a significantly stronger connectivity of the bilateral hippocampus with the left middle frontal gyrus in ID than in controls (p = .035, cluster based correction for multiple comparisons). Further analyses across all participants moreover showed that individual differences in the strength of this connectivity were associated with insomnia severity (ISI, r = 0.371, p = 9.3e−5) and with subjective sleep quality (CSD sleep efficiency, r = −0.307, p = .009) (all p FDR‐corrected). Hippocampal volume did not differ between ID and controls. The findings indicate more severe insomnia and worse sleep quality in people with a stronger functional connectivity between the bilateral hippocampus and the left middle frontal gyrus, part of a circuit that characteristically activates with maladaptive rumination and deactivates with sleep.