Multiple imputation of missing fMRI data in whole brain analysis

Duplicated Heschl's gyrus associations with phonological decoding

The reason(s) for why a complete duplication of the left hemisphere Heschl's gyrus (HG) has been observed in people with reading disability are unclear. This study was designed to replicate and advance understanding of the HG and phonological decoding association, as well as test competing hypotheses that this HG duplication association is specifically localized to the HG or could be due to co-occurring atypical development of other brain regions that support reading and language development. Participants were selected on the basis of having a duplicated left hemisphere HG (N = 96) or a single HG (N = 96) and matched according to age, sex, and research site in this multi-site study. Duplicated and single HG morphology specific templates were created to determine the extent to which HG sizes were related to phonological decoding within each HG morphology group. The duplicated HG group had significantly lower phonological decoding (F = 4.48, p = 0.04) but not verbal IQ (F = 1.39, p = 0.41) compared to the single HG group. In addition, larger HG were significantly associated with lower phonological decoding in the duplicated HG group, with effects driven by the size of the lateral HG after controlling for age, sex, research site, and handedness (ps < 0.05). Brain regions that exhibited structural covariance with HG did not clearly explain the HG and phonological decoding associations. Together, the results suggest that presence of a duplicated HG indicates some risk for lower phonological decoding ability compared to verbal IQ, but the reason(s) for this association remain unclear.

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis

This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.

Dynamics of cognitive predictors during reading acquisition in a sample of children overrepresented for dyslexia risk

Literacy acquisition is a complex process with genetic and environmental factors influencing cognitive and neural processes associated with reading. Previous research identified factors that predict word reading fluency (WRF), including phonological awareness (PA), rapid automatized naming (RAN), and speech-in-noise perception (SPIN). Recent theoretical accounts suggest dynamic interactions between these factors and reading, but direct investigations of such dynamics are lacking. Here, we investigated the dynamic effect of phonological processing and speech perception on WRF. More specifically, we evaluated the dynamic influence of PA, RAN, and SPIN measured in kindergarten (the year prior to formal reading instruction), first grade (the first year of formal reading instruction) and second grade on WRF in second and third grade. We also assessed the effect of an indirect proxy of family risk for reading difficulties using a parental questionnaire (Adult Reading History Questionnaire, ARHQ). We applied path modeling in a longitudinal sample of 162 Dutch-speaking children of whom the majority was selected to have an increased family and/or cognitive risk for dyslexia. We showed that parental ARHQ had a significant effect on WRF, RAN and SPIN, but unexpectedly not on PA. We also found effects of RAN and PA directly on WRF that were limited to first and second grade respectively, in contrast to previous research reporting pre-reading PA effects and prolonged RAN effects throughout reading acquisition. Our study provides important new insights into early prediction of later word reading abilities and into the optimal time window to target a specific reading-related subskill during intervention.

Evidence of Neurovascular Water Exchange and Endothelial Vascular Dysfunction in Schizophrenia: An Exploratory Study

BACKGROUND AND HYPOTHESIS

Mounting evidence supports cerebrovascular contributions to schizophrenia spectrum disorder (SSD) but with unknown mechanisms. The blood-brain barrier (BBB) is at the nexus of neural-vascular exchanges, tasked with regulating cerebral homeostasis. BBB abnormalities in SSD, if any, are likely more subtle compared to typical neurological insults and imaging measures that assess large molecule BBB leakage in major neurological events may not be sensitive enough to directly examine BBB abnormalities in SSD.

STUDY DESIGN

We tested the hypothesis that neurovascular water exchange (Kw) measured by non-invasive diffusion-prepared arterial spin label MRI (n = 27 healthy controls [HC], n = 32 SSD) is impaired in SSD and associated with clinical symptoms. Peripheral vascular endothelial health was examined by brachial artery flow-mediated dilation (n = 44 HC, n = 37 SSD) to examine whether centrally measured Kw is related to endothelial functions.

STUDY RESULTS

Whole-brain average Kw was significantly reduced in SSD (P = .007). Exploratory analyses demonstrated neurovascular water exchange reductions in the right parietal lobe, including the supramarginal gyrus (P = .002) and postcentral gyrus (P = .008). Reduced right superior corona radiata (P = .001) and right angular gyrus Kw (P = .006) was associated with negative symptoms. Peripheral endothelial function was also significantly reduced in SSD (P = .0001). Kw in 94% of brain regions in HC positively associated with peripheral endothelial function, which was not observed in SSD, where the correlation was inversed in 52% of brain regions.

CONCLUSIONS

This study provides initial evidence of neurovascular water exchange abnormalities, which appeared clinically associated, especially with negative symptoms, in schizophrenia.

BLMM: Parallelised computing for big linear mixed models

Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data. Yet in the large-n setting, such a voxel-wise deletion missing data strategy leads to severe shrinkage of the final analysis mask. To counter these issues, we describe the "Big" Linear Mixed Models (BLMM) toolbox, an efficient Python package for large-scale fMRI LMM analyses. BLMM is designed for use on high performance computing clusters and utilizes a Fisher Scoring procedure made possible by derivations for the LMM Fisher information matrix and score vectors derived in our previous work, Maullin-Sapey and Nichols (2021).

Differentiating Perpetrators of Intimate Partner Violence From Other Violent Offenders Using a Statistical Learning Model: The Role of Cognition and Life History Variables

Intimate partner violence (IPV) is a widespread crime that victimizes over 4-million women per year in the United States and results in significant monetary cost and unmeasured physical and psychological consequences for victims. Specialized IPV offender treatment programs demonstrate limited effectiveness, which may be due to an insufficient understanding of the factors that differentiate between IPV perpetrators and non-IPV violent offenders. In this study, we utilized classification and regression tree (CART) analysis to identify combinations of factors that best discriminate IPV perpetrators from non-IPV violent offenders. We also compared cognitive abilities between IPV perpetrators and non-IPV violent offenders using standardized neurocognitive tests. CART analysis presented two pathways for identifying offenders as IPV perpetrators: (a) extensive nonviolent criminal history and (b) moderate-to-severe expression of interpersonal traits of psychopathy without attentional deficits. In addition, a third pathway identified non-IPV violent offenders: (c) low levels of interpersonal psychopathic traits and no history of neurodevelopmental diagnosis. IPV perpetrators demonstrated intact cognition relative to test norms, and study groups did not significantly differ on cognitive performance. These findings suggest that individuals with multiple arrests for nonviolent crime or individuals with interpersonal traits of psychopathy without attentional difficulties may be at enhanced risk for IPV perpetration.

Taking the 4D Nature of fMRI Data Into Account Promises Significant Gains in Data Completion

Functional magnetic resonance imaging (fMRI) is a powerful, noninvasive tool that has significantly contributed to the understanding of the human brain. FMRI data provide a sequence of whole-brain volumes over time and hence are inherently four dimensional (4D). Missing data in fMRI experiments arise from image acquisition limits, susceptibility and motion artifacts or during confounding noise removal. Hence, significant brain regions may be excluded from the data, which can seriously undermine the quality of subsequent analyses due to the significant number of missing voxels. We take advantage of the four dimensional (4D) nature of fMRI data through a tensor representation and introduce an effective algorithm to estimate missing samples in fMRI data. The proposed Riemannian nonlinear spectral conjugate gradient (RSCG) optimization method uses tensor train (TT) decomposition, which enables compact representations and provides efficient linear algebra operations. Exploiting the Riemannian structure boosts algorithm performance significantly, as evidenced by the comparison of RSCG-TT with state-of-the-art stochastic gradient methods, which are developed in the Euclidean space. We thus provide an effective method for estimating missing brain voxels and, more importantly, clearly show that taking the full 4D structure of fMRI data into account provides important gains when compared with three-dimensional (3D) and the most commonly used two-dimensional (2D) representations of fMRI data.

Much Ado About Missingness: A Demonstration of Full Information Maximum Likelihood Estimation to Address Missingness in Functional Magnetic Resonance Imaging Data

The current paper leveraged a large multi-study functional magnetic resonance imaging (fMRI) dataset (N = 363) and a generated missingness paradigm to demonstrate different approaches for handling missing fMRI data under a variety of conditions. The performance of full information maximum likelihood (FIML) estimation, both with and without auxiliary variables, and listwise deletion were compared under different conditions of generated missing data volumes (i.e., 20, 35, and 50%). FIML generally performed better than listwise deletion in replicating results from the full dataset, but differences were small in the absence of auxiliary variables that correlated strongly with fMRI task data. However, when an auxiliary variable created to correlate r = 0.5 with fMRI task data was included, the performance of the FIML model improved, suggesting the potential value of FIML-based approaches for missing fMRI data when a strong auxiliary variable is available. In addition to primary methodological insights, the current study also makes an important contribution to the literature on neural vulnerability factors for obesity. Specifically, results from the full data model show that greater activation in regions implicated in reward processing (caudate and putamen) in response to tastes of milkshake significantly predicted weight gain over the following year. Implications of both methodological and substantive findings are discussed.

Dissociating the Neural Correlates of Consciousness and Task Relevance in Face Perception Using Simultaneous EEG-fMRI

Current theories of visual consciousness disagree about whether it emerges during early stages of processing in sensory brain regions or later when a widespread frontoparietal network becomes involved. Moreover, disentangling conscious perception from task-related postperceptual processes (e.g., report) and integrating results across different neuroscientific methods remain ongoing challenges. The present study addressed these problems using simultaneous EEG-fMRI and a specific inattentional blindness paradigm with three physically identical phases in female and male human participants. In phase 1, participants performed a distractor task during which line drawings of faces and control stimuli were presented centrally. While some participants spontaneously noticed the faces in phase 1, others remained inattentionally blind. In phase 2, all participants were made aware of the task-irrelevant faces but continued the distractor task. In phase 3, the faces became task-relevant. Bayesian analysis of brain responses demonstrated that conscious face perception was most strongly associated with activation in fusiform gyrus (fMRI) as well as the N170 and visual awareness negativity (EEG). Smaller awareness effects were revealed in the occipital and prefrontal cortex (fMRI). Task-relevant face processing, on the other hand, led to strong, extensive activation of occipitotemporal, frontoparietal, and attentional networks (fMRI). In EEG, it enhanced early negativities and elicited a pronounced P3b component. Overall, we provide evidence that conscious visual perception is linked with early processing in stimulus-specific sensory brain areas but may additionally involve prefrontal cortex. In contrast, the strong activation of widespread brain networks and the P3b are more likely associated with task-related processes.SIGNIFICANCE STATEMENT How does our brain generate visual consciousness-the subjective experience of what it is like to see, for example, a face? To date, it is hotly debated whether it emerges early in sensory brain regions or later when a widespread frontoparietal network is activated. Here, we use simultaneous fMRI and EEG for high spatial and temporal resolution and demonstrate that conscious face perception is predominantly linked to early and occipitotemporal processes, but also prefrontal activity. Task-related processes (e.g., decision-making), on the other hand, elicit brain-wide activations including late and strong frontoparietal activity. These findings challenge numerous previous studies and highlight the importance of investigating the neural correlates of consciousness in the absence of task relevance.

Fully synthetic neuroimaging data for replication and exploration

Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and voxel-level associations with predictor variables (age, sex, verbal IQ, and ICV). The magnitude and spatial distribution of gray matter effects in the observed imaging data were replicated in the pooled results from the synthetic datasets. This approach for generating fully synthetic neuroimaging data has widespread potential for data sharing, including replication, new discovery, and education. Fully synthetic neuroimaging datasets can enable data-sharing because it accurately represents patterns of variance in the original data, while diminishing the risk of privacy disclosures that can accompany neuroimaging data sharing.

Glutamatergic hypo-function in the left superior and middle temporal gyri in early schizophrenia: a data-driven three-dimensional proton spectroscopic imaging study

Proton magnetic resonance spectroscopy (¹H-MRS) studies have examined glutamatergic abnormalities in schizophrenia, mostly in single voxels. Though the critical brain nodes remain unknown, schizophrenia involves networks with broad abnormalities. Hence, glutamine plus glutamate (Glx) and other metabolites were examined with whole-brain ¹H-MRS, in early schizophrenia. Three dimensional ¹H-MRS was acquired in young schizophrenia subjects (N = 36, 19 antipsychotic-naïve and 17 antipsychotic-treated) and healthy controls (HC, N = 29). Glx (as well as N-acetylaspartate, choline, myo-inositol and creatine) group contrasts from all individual voxels that met spectral quality, were analyzed in common brain space, followed by cluster-corrected level alpha-value (CCLAV ≤ 0.05). Schizophrenia subjects had lower Glx in the left superior (STG) and middle temporal gyri (16 voxels, CCLAV = 0.04) and increased creatine in two clusters involving left temporal, parietal and occipital regions (32, and 18 voxels, CCLAV = 0.02 and 0.04, respectively). Antipsychotic-treated and naïve patients (vs HC) had similar Glx reductions (8/16 vs 10/16 voxels respectively, but CCLAV's > 0.05). However, creatine was higher in antipsychotic-treated vs HC's in a larger left hemisphere cluster (100 voxels, CCLAV = 0.01). Also in treated patients, choline was increased in left middle frontal gyrus (18 voxels, CCLAV = 0.04). Finally in antipsychotic-naive patients, NAA was reduced in right frontal gyri (19 voxels, CCLAV = 0.05) and myo-inositol was reduced in the left cerebellum (34 voxels, CCLAV = 0.02). We conclude that data-driven spectroscopic brain examination supports that reductions in Glx in the left STG may be critical to the pathophysiology of schizophrenia. Postmortem and neuromodulation schizophrenia studies focusing on left STG, may provide critical mechanistic and therapeutic advancements, respectively.

Approaches for missing covariate data in logistic regression with MNAR sensitivity analyses

Data with missing covariate values but fully observed binary outcomes are an important subset of the missing data challenge. Common approaches are complete case analysis (CCA) and multiple imputation (MI). While CCA relies on missing completely at random (MCAR), MI usually relies on a missing at random (MAR) assumption to produce unbiased results. For MI involving logistic regression models, it is also important to consider several missing not at random (MNAR) conditions under which CCA is asymptotically unbiased and, as we show, MI is also valid in some cases. We use a data application and simulation study to compare the performance of several machine learning and parametric MI methods under a fully conditional specification framework (MI-FCS). Our simulation includes five scenarios involving MCAR, MAR, and MNAR under predictable and nonpredictable conditions, where "predictable" indicates missingness is not associated with the outcome. We build on previous results in the literature to show MI and CCA can both produce unbiased results under more conditions than some analysts may realize. When both approaches were valid, we found that MI-FCS was at least as good as CCA in terms of estimated bias and coverage, and was superior when missingness involved a categorical covariate. We also demonstrate how MNAR sensitivity analysis can build confidence that unbiased results were obtained, including under MNAR-predictable, when CCA and MI are both valid. Since the missingness mechanism cannot be identified from observed data, investigators should compare results from MI and CCA when both are plausibly valid, followed by MNAR sensitivity analysis.

The relationship between socioeconomic status and white matter microstructure in pre-reading children: A longitudinal investigation

Reading is a learned skill crucial for educational attainment. Children from families of lower socioeconomic status (SES) tend to have poorer reading performance and this gap widens across years of schooling. Reading relies on the orchestration of multiple neural systems integrated via specific white-matter pathways, but there is limited understanding about whether these pathways relate differentially to reading performance depending on SES background. Kindergarten white-matter FA and second-grade reading outcomes were investigated in an SES-diverse sample of 125 children. The three left-hemisphere white-matter tracts most associated with reading, and their right-hemisphere homologs, were examined: arcuate fasciculus (AF), superior longitudinal fasciculus (SLF), and inferior longitudinal fasciculus (ILF). There was a significant and positive association between SES and fractional anisotropy (FA) in the bilateral ILF in kindergarten. SES moderated the association between kindergarten ILF and second grade reading performance, such that it was positive in lower-SES children, but not significant in higher-SES children. These results have implications for understanding the role of the environment in the development of the neural pathways that support reading.

Reading Profiles in Multi-Site Data With Missingness

Children with reading disability exhibit varied deficits in reading and cognitive abilities that contribute to their reading comprehension problems. Some children exhibit primary deficits in phonological processing, while others can exhibit deficits in oral language and executive functions that affect comprehension. This behavioral heterogeneity is problematic when missing data prevent the characterization of different reading profiles, which often occurs in retrospective data sharing initiatives without coordinated data collection. Here we show that reading profiles can be reliably identified based on Random Forest classification of incomplete behavioral datasets, after the missForest method is used to multiply impute missing values. Results from simulation analyses showed that reading profiles could be accurately classified across degrees of missingness (e.g., ∼5% classification error for 30% missingness across the sample). The application of missForest to a real multi-site dataset with missingness (n = 924) showed that reading disability profiles significantly and consistently differed in reading and cognitive abilities for cases with and without missing data. The results of validation analyses indicated that the reading profiles (cases with and without missing data) exhibited significant differences for an independent set of behavioral variables that were not used to classify reading profiles. Together, the results show how multiple imputation can be applied to the classification of cases with missing data and can increase the integrity of results from multi-site open access datasets.

Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data

Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).

Orthographic influence on spoken word identification: Behavioral and fMRI evidence

The current study investigated behavioral and neuroimaging evidence for orthographic influences on auditory word identification. To assess such influences, the proportion of similar sounding words (i.e. phonological neighbors) that were also spelled similarly (i.e., orthographic neighbors) was computed for each auditorily presented word as the Orthographic-to-Phonological Overlap Ratio (OPOR). Speech intelligibility was manipulated by presenting monosyllabic words in multi-talker babble at two signal-to-noise ratios: + 3 and + 10 dB SNR. Identification rates were lower for high overlap words in the challenging + 3 dB SNR condition. In addition, BOLD contrast increased with OPOR at the more difficult SNR, and decreased with OPOR under more favorable SNR conditions. Both voxel-based and region of interest analyses demonstrated robust effects of OPOR in several cingulo-opercular regions. However, contrary to prior theoretical accounts, no task-related activity was observed in posterior regions associated with phonological or orthographic processing. We suggest that, when processing is difficult, orthographic-to-phonological feature overlap increases the availability of competing responses, which then requires additional support from domain general performance systems in order to produce a single response.

Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia

Impaired cognitive empathy is a core social cognitive deficit in schizophrenia associated with negative symptoms and social functioning. Cognitive empathy and negative symptoms have also been linked to medial prefrontal and temporal brain networks. While shared behavioral and neural underpinnings are suspected for cognitive empathy and negative symptoms, research is needed to test these hypotheses. In two studies, we evaluated whether resting-state functional connectivity between data-driven networks, or components (referred to as, inter-component connectivity), predicted cognitive empathy and experiential and expressive negative symptoms in schizophrenia subjects. Study 1: We examined associations between cognitive empathy and medial prefrontal and temporal inter-component connectivity at rest using a group-matched schizophrenia and control sample. We then assessed whether inter-component connectivity metrics associated with cognitive empathy were also related to negative symptoms. Study 2: We sought to replicate the connectivity-symptom associations observed in Study 1 using an independent schizophrenia sample. Study 1 results revealed that while the groups did not differ in average inter-component connectivity, a medial-fronto-temporal metric and an orbito-fronto-temporal metric were related to cognitive empathy. Moreover, the medial-fronto-temporal metric was associated with experiential negative symptoms in both schizophrenia samples. These findings support recent models that link social cognition and negative symptoms in schizophrenia. Hum Brain Mapp 38:1111-1124, 2017. © 2016 Wiley Periodicals, Inc.

Cingulo-Opercular Function During Word Recognition in Noise for Older Adults with Hearing Loss

BACKGROUND/STUDY CONTEXT

Adaptive control, reflected by elevated activity in cingulo-opercular brain regions, optimizes performance in challenging tasks by monitoring outcomes and adjusting behavior. For example, cingulo-opercular function benefits trial-level word recognition in noise for normal-hearing adults. Because auditory system deficits may limit the communicative benefit from adaptive control, we examined the extent to which cingulo-opercular engagement supports word recognition in noise for older adults with hearing loss (HL).

METHODS

Participants were selected to form groups with Less HL (n = 12; mean pure tone threshold, pure tone average [PTA] = 19.2 ± 4.8 dB HL [hearing level]) and More HL (n = 12; PTA = 38.4 ± 4.5 dB HL, 0.25-8 kHz, both ears). A word recognition task was performed with words presented in multitalker babble at +3 or +10 dB signal-to-noise ratios (SNRs) during a sparse acquisition fMRI experiment. The participants were middle-aged and older (ages: 64.1 ± 8.4 years) English speakers with no history of neurological or psychiatric diagnoses.

RESULTS

Elevated cingulo-opercular activity occurred with increased likelihood of correct word recognition on the next trial (t(23) = 3.28, p = .003), and this association did not differ between hearing loss groups. During trials with word recognition errors, the More HL group exhibited higher blood oxygen level-dependent (BOLD) contrast in occipital and parietal regions compared with the Less HL group. Across listeners, more pronounced cingulo-opercular activity during recognition errors was associated with better overall word recognition performance.

CONCLUSION

The trial-level word recognition benefit from cingulo-opercular activity was equivalent for both hearing loss groups. When speech audibility and performance levels are similar for older adults with mild to moderate hearing loss, cingulo-opercular adaptive control contributes to word recognition in noise.

Vulnerability for new episodes in recurrent major depressive disorder: protocol for the longitudinal DELTA-neuroimaging cohort study

INTRODUCTION

Major depressive disorder (MDD) is widely prevalent and severely disabling, mainly due to its recurrent nature. A better understanding of the mechanisms underlying MDD-recurrence may help to identify high-risk patients and to improve the preventive treatment they need. MDD-recurrence has been considered from various levels of perspective including symptomatology, affective neuropsychology, brain circuitry and endocrinology/metabolism. However, MDD-recurrence understanding is limited, because these perspectives have been studied mainly in isolation, cross-sectionally in depressed patients. Therefore, we aim at improving MDD-recurrence understanding by studying these four selected perspectives in combination and prospectively during remission.

METHODS AND ANALYSIS

In a cohort design, we will include 60 remitted, unipolar, unmedicated, recurrent MDD-participants (35-65 years) with ≥ 2 MDD-episodes. At baseline, we will compare the MDD-participants with 40 matched controls. Subsequently, we will follow-up the MDD-participants for 2.5 years while monitoring recurrences. We will invite participants with a recurrence to repeat baseline measurements, together with matched remitted MDD-participants. Measurements include questionnaires, sad mood-induction, lifestyle/diet, 3 T structural (T1-weighted and diffusion tensor imaging) and blood-oxygen-level-dependent functional MRI (fMRI) and MR-spectroscopy. fMRI focusses on resting state, reward/aversive-related learning and emotion regulation. With affective neuropsychological tasks we will test emotional processing. Moreover, we will assess endocrinology (salivary hypothalamic-pituitary-adrenal-axis cortisol and dehydroepiandrosterone-sulfate) and metabolism (metabolomics including polyunsaturated fatty acids), and store blood for, for example, inflammation analyses, genomics and proteomics. Finally, we will perform repeated momentary daily assessments using experience sampling methods at baseline. We will integrate measures to test: (1) differences between MDD-participants and controls; (2) associations of baseline measures with retro/prospective recurrence-rates; and (3) repeated measures changes during follow-up recurrence. This data set will allow us to study different predictors of recurrence in combination.

ETHICS AND DISSEMINATION

The local ethics committee approved this study (AMC-METC-Nr.:11/050). We will submit results for publication in peer-reviewed journals and presentation at (inter)national scientific meetings.

TRIAL REGISTRATION NUMBER

NTR3768.

Gray Matter Features of Reading Disability: A Combined Meta-Analytic and Direct Analysis Approach(1,2,3,4)

Meta-analysis of voxel-based morphometry dyslexia studies and direct analysis of 293 reading disability and control cases from six different research sites were performed to characterize defining gray matter features of reading disability. These analyses demonstrated consistently lower gray matter volume in left posterior superior temporal sulcus/middle temporal gyrus regions and left orbitofrontal gyrus/pars orbitalis regions. Gray matter volume within both of these regions significantly predicted individual variation in reading comprehension after correcting for multiple comparisons. These regional gray matter differences were observed across published studies and in the multisite dataset after controlling for potential age and gender effects, and despite increased anatomical variance in the reading disability group, but were not significant after controlling for total gray matter volume. Thus, the orbitofrontal and posterior superior temporal sulcus gray matter findings are relatively reliable effects that appear to be dependent on cases with low total gray matter volume. The results are considered in the context of genetics studies linking orbitofrontal and superior temporal sulcus regions to alleles that confer risk for reading disability.

Dual-echo fMRI can detect activations in inferior temporal lobe during intelligible speech comprehension

The neural basis of speech comprehension has been investigated intensively during the past few decades. Incoming auditory signals are analysed for speech-like patterns and meaningful information can be extracted by mapping these sounds onto stored semantic representations. Investigation into the neural basis of speech comprehension has largely focused on the temporal lobe, in particular the superior and posterior regions. The ventral anterior temporal lobe (vATL), which includes the inferior temporal gyrus (ITG) and temporal fusiform gyrus (TFG) is consistently omitted in fMRI studies. In contrast, PET studies have shown the involvement of these ventral temporal regions. One crucial factor is the signal loss experienced using conventional echo planar imaging (EPI) for fMRI, at tissue interfaces such as the vATL. One method to overcome this signal loss is to employ a dual-echo EPI technique. The aim of this study was to use intelligible and unintelligible (spectrally rotated) sentences to determine if the vATL could be detected during a passive speech comprehension task using a dual-echo acquisition. A whole brain analysis for an intelligibility contrast showed bilateral superior temporal lobe activations and a cluster of activation within the left vATL. Converging evidence implicates the same ventral temporal regions during semantic processing tasks, which include language processing. The specific role of the ventral temporal region during intelligible speech processing cannot be determined from this data alone, but the converging evidence from PET, MEG, TMS and neuropsychology strongly suggest that it contains the stored semantic representations, which are activated by the speech decoding process.

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Intelligible speech comprehension activates bilateral superior temporal lobe.

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Critically, intelligibility also activates anterior inferior temporal lobe.

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Phonological processing does not activate inferior temporal lobe.

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fMRI results converge with existing PET, MEG and neuropsychology.

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Anterior inferior temporal lobe is involved during speech comprehension.

Cortical activity predicts which older adults recognize speech in noise and when

Speech recognition in noise can be challenging for older adults and elicits elevated activity throughout a cingulo-opercular network that is hypothesized to monitor and modify behaviors to optimize performance. A word recognition in noise experiment was used to test the hypothesis that cingulo-opercular engagement provides performance benefit for older adults. Healthy older adults (N = 31; 50-81 years of age; mean pure tone thresholds <32 dB HL from 0.25 to 8 kHz, best ear; species: human) performed word recognition in multitalker babble at 2 signal-to-noise ratios (SNR = +3 or +10 dB) during a sparse sampling fMRI experiment. Elevated cingulo-opercular activity was associated with an increased likelihood of correct recognition on the following trial independently of SNR and performance on the preceding trial. The cingulo-opercular effect increased for participants with the best overall performance. These effects were lower for older adults compared with a younger, normal-hearing adult sample (N = 18). Visual cortex activity also predicted trial-level recognition for the older adults, which resulted from discrete decreases in activity before errors and occurred for the oldest adults with the poorest recognition. Participants demonstrating larger visual cortex effects also had reduced fractional anisotropy in an anterior portion of the left inferior frontal-occipital fasciculus, which projects between frontal and occipital regions where activity predicted word recognition. Together, the results indicate that older adults experience performance benefit from elevated cingulo-opercular activity, but not to the same extent as younger adults, and that declines in attentional control can limit word recognition.

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

The age-related posterior-anterior shift as revealed by voxelwise analysis of functional brain networks

The posterior-anterior shift in aging (PASA) is a commonly observed phenomenon in functional neuroimaging studies of aging, characterized by age-related reductions in occipital activity alongside increases in frontal activity. In this work we have investigated the hypothesis as to whether the PASA is also manifested in functional brain network measures such as degree, clustering coefficient, path length and local efficiency. We have performed statistical analysis upon functional networks derived from a fMRI dataset containing data from healthy young, healthy aged, and aged individuals with very mild to mild Alzheimer's disease (AD). Analysis of both task based and resting state functional network properties has indicated that the PASA can also be characterized in terms of modulation of functional network properties, and that the onset of AD appears to accentuate this modulation. We also explore the effect of spatial normalization upon the results of our analysis.

SGPP: spatial Gaussian predictive process models for neuroimaging data

The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve a better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model spatial dependence in neuroimaging data. The SGPP model uses a functional principal component model to capture medium-to-long-range (or global) spatial dependence, while SGPP uses a multivariate simultaneous autoregressive model to capture short-range (or local) spatial dependence as well as cross-correlations of different imaging modalities. We propose a three-stage estimation procedure to simultaneously estimate varying regression coefficients across voxels and the global and local spatial dependence structures. Furthermore, we develop a predictive method to use the spatial correlations as well as the cross-correlations by employing a cokriging technique, which can be useful for the imputation of missing imaging data. Simulation studies and real data analysis are used to evaluate the prediction accuracy of SGPP and show that SGPP significantly outperforms several competing methods, such as voxel-wise linear model, in prediction. Although we focus on the morphometric variation of lateral ventricle surfaces in a clinical study of neurodevelopment, it is expected that SGPP is applicable to other imaging modalities and features.

The cingulo-opercular network provides word-recognition benefit

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed word recognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during word recognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct word recognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports word recognition in difficult listening conditions.

Linear mixed-effects modeling approach to FMRI group analysis

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details.