Socioeconomic factors, sleep timing and duration, and amygdala resting-state functional connectivity in children.

Introduction: Reduced sleep health has been consistently linked with increased negative emotion in children. While sleep characteristics have been associated with neural function in adults and adolescents, much less is known about these associations in children while considering socioeconomic context. In this study, we examined the associations among socioeconomic factors, sleep duration and timing, and resting-state functional connectivity (rsFC) of the amygdala in children. Methods: Participants were typically-developing 5- to 9-year-olds from socioeconomically diverse families (61% female; N = 94). Parents reported on children's weekday and weekend bedtimes and wake-up times, which were used to compute sleep duration and midpoint. Analyses focused on amygdala-anterior cingulate cortex (ACC) connectivity followed by amygdala-whole brain connectivity. Results: Lower family income-to-needs ratio and parental education were significantly associated with later weekday and weekend sleep timing and shorter weekday sleep duration. Shorter weekday sleep duration was associated with decreased amygdala-ACC and amygdala-insula connectivity. Later weekend sleep midpoint was associated with decreased amygdala-paracingulate cortex and amygdala-postcentral gyrus connectivity. Socioeconomic factors were indirectly associated with connectivity in these circuits via sleep duration and timing. Discussion: These results suggest that socioeconomic disadvantage may interfere with both sleep duration and timing, in turn possibly altering amygdala connectivity in emotion processing and regulation circuits in children. Effective strategies supporting family economic conditions may have benefits for sleep health and brain development in children.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Background: Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). Methods: One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. Results: We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. Conclusion: This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Characteristics of Adolescents with and without a Family History of Substance Use Disorder from a Minority Cohort.

Family history (FH+) of substance use disorder (SUD) is an established risk factor for offspring SUD. The extent to which offspring psychological traits or the family environment, each of which may be relevant to familial transmission of SUD risk, vary by FH+ in socioeconomically disadvantaged populations is less clear. We compared the family/social environmental and psychological characteristics of 73 FH+ and 69 FH- youth ages 12-16, from a study of parental criminal justice system involvement in a primarily low-income, minority urban population. A latent profile analysis (LPA) empirically identified groups of subjects with similar psychological characteristics, which were then compared by FH+. FH+ youths were found to have greater mean household size, greater parental psychological aggression, and a higher mean number of adverse childhood experiences, even without considering parental SUD. FH+ individuals had lower report card grades according to parental report and were more likely to have a history of externalizing disorders than FH- individuals. However, FH+ was not significantly associated with many psychological characteristics or with the class membership from the LPA. In conclusion, among a population of low-income, minority urban youth, FH+ was associated with differences in the family environment and only subtle differences in individual psychological characteristics.

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Explaining Deep Learning-Based Representations of Resting State Functional Connectivity Data: Focusing on Interpreting Nonlinear Patterns in Autism Spectrum Disorder.

Background: Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used to study brain function in psychiatric disorders, yielding insight into brain organization. However, the high dimensionality of the rs-fMRI data presents challenges, and requires dimensionality reduction before applying machine learning techniques. Neural networks, specifically variational autoencoders (VAEs), have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity patterns, addressing the complex nonlinear structure of rs-fMRI. However, interpreting those latent representations remains a challenge. This paper aims to address this gap by creating explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). Methods: One-thousand one hundred and fifty participants (601 HC and 549 patients with ASD) were included in the analysis. We extracted functional connectivity correlation matrices from the preprocessed rs-fMRI data using Power atlas with 264 ROIs. Then VAEs were trained in an unsupervised fashion. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. Results: We quantified the latent contribution scores for the ASD and control groups at the network level. We found that both ASD and control groups share the top network connectivity that contribute to all estimated latent components. For example, latent 0 was driven by resting state functional connectivity patterns (rsFC) within ventral attention network in both the ASD and control. However, significant differences in the latent contribution scores between the ASD and control groups were discovered within the ventral attention network in latent 0 and the sensory/somatomotor network in latent 2. Conclusion: This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC features as estimated latent representation changes, enabling an explainable deep learning model to better understand the underlying neural mechanism of ASD.

Connectivity based Real-Time fMRI Neurofeedback Training in Youth with a History of Major Depressive Disorder.

BACKGROUND: Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has proven to be a powerful technique to help subjects to gauge and enhance emotional control. Traditionally, rtfMRI-nf has focused on emotional regulation through self-regulation of amygdala. Recently, rtfMRI studies have observed that regulation of a target brain region is accompanied by connectivity changes beyond the target region. Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task. METHOD: Ten remitted MDD subjects and twelve healthy controls (HC) performed an emotion regulation task in 4 runs of rtfMRI-nf training followed by one transfer run without neurofeedback conducted in a single session. The functional connectivity between amygdala and prefrontal cortex was presented as a feedback bar concurrent with the emotion regulation task. Participants' emotional state was measured by the Positive and Negative Affect Schedule (PANAS) prior to and following the rtfMRI-nf. Psychological assessments were used to determine subjects' history of depression. RESULTS: Participants with a history of MDD showed a trend of decreasing functional connectivity across the four rtfMRI-nf runs, and there was a marginally significant interaction between the MDD history and number of training runs. The HC group showed a significant increase of frontal cortex activation between the second and third neurofeedback runs. Comparing PANAS scores before and after connectivity-based rtfMRI-nf, we observed a significant decrease in negative PANAS score in the whole group overall, and a significant decrease in positive PANAS score in the MDD group alone.

Socioeconomic disparities in sleep duration are associated with cortical thickness in children.

INTRODUCTION: Disrupted sleep has been consistently linked with lower academic achievement and worse mental health in children. Less is understood about sleep as a potential factor underlying socioeconomic differences in brain morphometry in children. The goals of this study were to investigate the associations among socioeconomic factors, sleep duration, and brain morphometry in children, and to examine the roles of the sleep environment and family routines in these associations. METHODS: Participants were 5- to 9-year-old children from socioeconomically diverse families (N = 94; 61% female). Parents reported on children's weekday and weekend sleep durations, sleep environment, and family routines. High-resolution, T1-weighted structural magnetic resonance imaging (MRI) data were acquired. Analyses focused on cortical thickness, cortical surface area, and amygdala and hippocampal volume. RESULTS: Results indicated that lower family income-to-needs ratio and parental education were significantly associated with shorter weekday sleep duration in children. Shorter weekday sleep duration was significantly associated with reduced thickness in the left middle temporal, right postcentral, and right superior frontal cortices and smaller basolateral but not centromedial amygdala volume. Family routines significantly mediated the associations of family income-to-needs ratio and parental education with weekday sleep duration in children. CONCLUSION: These results contribute to our understanding of sleep factors as proximal mechanisms through which socioeconomic context may alter neural development during childhood.

A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.

Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.

Environmental noise, brain structure, and language development in children.

While excessive noise exposure in childhood has been associated with reduced language ability, few studies have examined potential underlying neurobiological mechanisms that may account for noise-related differences in language skills. In this study, we tested the hypotheses that higher everyday noise exposure would be associated with 1) poorer language skills and 2) differences in language-related cortical structure. A socioeconomically diverse sample of children aged 5-9 (N = 94) completed standardized language assessments. High-resolution T1-weighted magnetic resonance imaging (MRI) scans were acquired, and surface area and cortical thickness of the left inferior frontal gyrus (IFG) and left superior temporal gyrus (STG) were extracted. Language Environmental Analysis (LENA) was used to measure levels of exposure to excessive environmental noise over the course of a typical day (n = 43 with complete LENA, MRI, and behavioral data). Results indicated that children exposed to excessive levels of noise exhibited reduced cortical thickness in the left IFG. These findings add to a growing literature that explores the extent to which home environmental factors, such as environmental noise, are associated with neurobiological development related to language development in children.

Shapes of subcortical structures in adolescents with and without familial history of substance use disorder.

It has been suggested that intergenerational transmission of risk for substance use disorder (SUD) manifests in the brain anatomy of substance naïve adolescents. While volume and shapes of subcortical structures (SSS) have been shown to be heritable, these structures, especially the pallidum, putamen, nucleus accumbens, and hippocampus, have also been associated with substance use disorders. However, it is not clear if those anatomical differences precede substance use or are the result of that use. Therefore, we examined if volume and SSS of adolescents with a family history (FH+) of SUD differed from adolescents without such a history (FH-). Because risk for SUD is associated with anxiety and impulsivity, we also examined correlations between these psychological characteristics and volume/SSS. Using structural MRI and FSL software, we segmented subcortical structures and obtained indices of SSS and volumes of 64 FH+ and 58 FH- adolescents. We examined group differences in volume and SSS, and the correlations between volume/SSS and trait anxiety and impulsivity. FH+ adolescents had a significant inward deformation in the shape of the right anterior hippocampus compared to FH- adolescents, while the volume of this structure did not differ between groups. Neither shape nor volume of the other subcortical structures differed between groups. In the FH+ adolescents, the left hippocampus shape was positively correlated with both trait anxiety and impulsivity, while in FH- adolescents a negative correlation pattern of SSS was seen in the hippocampus. SSS appears to capture local anatomical features that traditional volumetric analysis does not. The inward shape deformation in the right anterior hippocampus in FH+ adolescents may be related to the known increased risk for behavioral dysregulation leading to SUD in FH+ offspring. Hippocampus shape also exhibits opposite patterns of correlation with anxiety and impulsivity scores across the FH+ and FH- adolescents. These novel findings may reveal neural correlates, not captured by traditional volumetric analysis, of familial transmission of increased vulnerability to SUD.

Hearing Rehabilitative Treatment for Older Adults With Comorbid Hearing Loss and Depression: Effects on Depressive Symptoms and Executive Function.

OBJECTIVES: Recent research has revealed important neural and psychiatric consequences of hearing loss (HL) in older adults. This pilot study examined the neural effects of HL and the impact of hearing aids on neuropsychiatric outcomes in major depressive disorder (MDD). DESIGN: Twelve-week, double-blind, randomized controlled trial. PARTICIPANTS/INTERVENTION: N = 25 (≥60 years) with MDD and moderate-profound HL were randomized to receive hearing aids (100% gain targets) or sham hearing aids (flat 30 dB HL) in addition to psychiatric treatment-as-usual. MEASUREMENTS: Depressive symptoms (Hamilton Rating Scale for Depression [HRSD]), executive functioning (NIH Toolbox Flanker), integrity of auditory brain areas (structural MRI, diffusion tensor imaging). RESULTS: At baseline, worse speech discrimination was associated with auditory cortical thinning (Left anterior transverse temporal gyrus: r = 0.755, p = 0.012) and lower integrity of the superior longitudinal fasciculus (FA: Left r = 0.772, p = 0.025, Right r = 0.782, p = 0.022). After 12-weeks, hearing aids were effective at improving hearing functioning (Hearing Handicap for the Elderly: active -12.47 versus sham -4.19, t = -2.64, df = 18, p = 0.016) and immediate memory (active +14.9 versus sham +5.7, t = 2.28, df = 16, p = 0.037). Moderate improvement was observed for hearing aids on executive functioning but did not reach statistical significance (Flanker: active +4.8 versus sham -2.4, t = 1.95, df = 15, p = 0.071). No significant effect on depression was found (HRSD: active -5.50 versus sham -7.32, t = 0.75, df = 19, p = 0.46). CONCLUSIONS: HL can affect brain regions important for auditory and cognitive processing, and hearing remediation may have beneficial effects on executive functioning in MDD. Future studies may evaluate whether impairment in cognitive control consequent to HL may be an important risk mechanism for MDD.

Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups.

BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD: Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.

Socioeconomic factors, stress, hair cortisol, and white matter microstructure in children.

Socioeconomic disadvantage has been linked to increased stress exposure in children and adults. Exposure to stress in childhood has been associated with deleterious effects on cognitive development and well-being throughout the lifespan. Further, exposure to stress has been associated with differences in brain development in children, both in cortical and subcortical gray matter. However, less is known about the associations among socioeconomic disadvantage, stress, and children's white matter development. In this study, we investigated whether socioeconomic disparities would be associated with differences in white matter microstructure in the cingulum bundle, as has been previously reported. We additionally investigated whether any such differences could be explained by differences in stress exposure and/or physiological stress levels. White matter tracts were measured via diffusion tensor imaging in 58 children aged 5-9 years. Results indicated that greater exposure to stressful life events was associated with higher child hair cortisol concentrations. Further, physiological stress, as indexed by hair cortisol concentrations, were associated with higher fractional anisotropy in the cingulum bundle. These results have implications for better understanding how perceived and physiological stress may alter neural development during childhood.

Contributions of Cerebellar White Matter Microstructure to Social Difficulty in Nonverbal Learning Disability.

Emerging evidence suggests that the cerebellum may contribute to variety of cognitive capacities, including social cognition. Nonverbal learning disability (NVLD) is characterized by visual-spatial and social impairment. Recent functional neuroimaging studies have shown that children with NVLD have altered cerebellar resting-state functional connectivity, which is associated with various symptom domains. However, little is known about cerebellar white matter microstructure in NVLD and whether it contributes to social deficits. Twenty-seven children (12 with NVLD, 15 typically developing (TD)) contributed useable diffusion tensor imaging data. Tract-based spatial statistics (TBSS) were used to quantify fractional anisotropy (FA) in the cerebellar peduncles. Parents completed the Child Behavior Checklist, providing a measure of social difficulty. Children with NVLD had greater fractional anisotropy in the left and right inferior cerebellar peduncle. Furthermore, right inferior cerebellar peduncle FA was associated with social impairment as measured by the Child Behavior Checklist Social Problems subscale. Finally, the association between NVLD diagnosis and greater social impairment was mediated by right inferior cerebellar peduncle FA. These findings provide additional evidence that the cerebellum contributes both to social cognition and to the pathophysiology of NVLD.

Delay discounting and neurocognitive correlates among inner city adolescents with and without family history of substance use disorder.

Adolescents with a family history (FH+) of substance use disorder (SUD) are at a greater risk for SUD, suggested to be partly due to the transmission of behavioral impulsivity. We used a delay discounting task to compare impulsivity in decision-making and its associated brain functioning among FH+ and FH - minority adolescents. Participants chose between Smaller Sooner (SS) and Larger Later (LL) rewards. The SS was available immediately (Now trials) or in the future (Not-Now trials), allowing for greater differentiation between impulsive decisions. The FH+ group showed greater impatience by responding SS more frequently than the FH - group, only on the Now trials, and even when the relative reward differences (RRD) increased. Surprisingly, there were no differences in brain activity between the groups. Combined, the groups showed greater reward activity during the Now vs. Not-Now trials in medial prefrontal/anterior cingulate, posterior cingulate, precuneus, and inferior frontal gyrus (i.e., an immediacy effect). As the RRD increased activation in the reward network decreased, including the striatum, possibly reflecting easy decision-making. These results indicate that risk for SUD, seen behaviorally among FH+ adolescents, may not yet be associated with discernable brain changes, suggesting that early intervention has the potential to reduce this risk.

Association of White Matter Integrity With Executive Function and Antidepressant Treatment Outcome in Patients With Late-Life Depression.

OBJECTIVE: While patients with late-life depression (LLD) often exhibit microstructural white matter alterations that can be identified with diffusion tensor imaging (DTI), there is a dearth of information concerning the links between DTI findings and specific cognitive performance, as well as between DTI measures and antidepressant treatment outcomes. DESIGN: Neuroimaging and cognitive tests were conducted at baseline in 71 older adults participating in a larger, 8-week duration antidepressant randomized controlled trial. Correlations between DTI measures of white matter integrity evaluated with tract-based spatial statistics, baseline neurocognitive performance, and prospective antidepressant treatment outcome were evaluated. RESULTS: Fractional anisotropy (FA), an index of white matter integrity, was significantly positively associated with better cognitive function as measured by the Initiation/Perseveration subscale of the Dementia Rating Scale in the bilateral superior longitudinal fasciculus (SLF), bilateral SLF-temporal, and right corticospinal tract (CST). An exploratory analysis limited to these tracts revealed that increased FA in the right CST, right SLF, and right SLF-temporal tracts was correlated with a greater decrease in depressive symptoms. Increased FA in the right CST predicted a greater chance of remission, while increased FA in the right CST and the right SLF predicted a greater chance of treatment response. CONCLUSION: In late-life depression LLD subjects, white matter integrity was positively associated with executive function in white matter tracts which act as key connecting structures underlying the cognitive control network. These tracts may play a role as a positive prognostic factor in antidepressant treatment outcome.

A quality control pipeline for probabilistic reconstruction of white-matter pathways.

BACKGROUND: One of the most well-validated tools for DTI data analysis is TRACULA, part of the FreeSurfer software. TRACULA automatically segments 18 major white matter (WM) tracts. Occasionally, tracts may be only partially reconstructed, thus requiring intervention to avoid biasing analyses. A majority of studies have not reported any quality control procedures and those that have tend to discard partially reconstructed tracts from group analyses if they cannot be salvaged during TRACULA reinitialization. NEW METHOD: We propose a semi-automated method to improve the detection and recovery of incomplete WM tracts. We detail several steps to maximize the quality of preprocessed DTI data. The steps include: (1) a visual inspection of eddy current corrected diffusion weighted images and (2) an automated evaluation of color- encoded FA images; (3) assessment of the volume of each tract saved in the TRACULA output file; (4) re-processing of tracts with a volume smaller than a specified threshold; (5) minimal manual editing of the control points for tracts that remained partially reconstructed; and (6) final re-initiation of TRACULA. RESULTS: Our method can speed and improve quality control relative to tract-by-tract visual inspection and can recover data that otherwise would need to be excluded from analyses due to incomplete reconstruction. COMPARISON WITH EXISTING METHODS: To our knowledge, there are no publications proposing alternative methods for quality control and recovering of partially reconstructed tracts in the TRACULA environment. CONCLUSIONS: Our method helps TRACULA users automatically access the quality of reconstructed WM tracts and semi-automatically recover those in-complete WM tracts.