The relationship of white matter microstructure with psychomotor disturbance and relapse in remitted psychotic depression.

BACKGROUND: Psychomotor disturbance is common in psychotic depression and is associated with relapse. In this analysis, we examined whether white matter microstructure is associated with relapse probability in psychotic depression and, if so, whether white matter microstructure accounts for the association between psychomotor disturbance and relapse. METHODS: We used tractography to characterize diffusion-weighted MRI data in 80 participants enrolled in a randomized clinical trial that compared efficacy and tolerability of sertraline plus olanzapine with sertraline plus placebo in the continuation treatment of remitted psychotic depression. Cox proportional hazard models tested the relationships between psychomotor disturbance (processing speed and CORE score) at baseline, white matter microstructure (fractional anisotropy [FA] and mean diffusivity [MD]) in 15 selected tracts at baseline, and relapse probability. RESULTS: CORE was significantly associated with relapse. Higher mean MD was significantly associated with relapse in the each of the following tracts: corpus callosum, left striato-frontal, left thalamo-frontal, and right thalamo-frontal. CORE and MD were each associated with relapse in the final models. LIMITATIONS: As a secondary analysis with a small sample size, this study was not powered for its aims, and is vulnerable to types I and II statistical errors. Further, the sample size was not sufficient to test the interaction of the independent variables and randomized treatment group with relapse probability. CONCLUSIONS: While both psychomotor disturbance and MD were associated with psychotic depression relapse, MD did not account for the relationship between psychomotor disturbance and relapse. The mechanism by which of psychomotor disturbance increases the risk of relapse requires further investigation. CLINICAL TRIAL REGISTRATION: Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II); NCT01427608. URL: https://clinicaltrials.gov/ct2/show/NCT01427608.

Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls.

BACKGROUND AND HYPOTHESIS: Neurocognitive and social cognitive abilities are important contributors to functional outcomes in schizophrenia spectrum disorders (SSDs). An unanswered question of considerable interest is whether neurocognitive and social cognitive deficits arise from overlapping or distinct white matter impairment(s). STUDY DESIGN: We sought to fill this gap, by harnessing a large sample of individuals from the multi-center Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) dataset, unique in its collection of advanced diffusion imaging and an extensive battery of cognitive assessments. We applied canonical correlation analysis to estimates of white matter microstructure, and cognitive performance, across people with and without an SSD. STUDY RESULTS: Our results established that white matter circuitry is dimensionally and strongly related to both neurocognition and social cognition, and that microstructure of the uncinate fasciculus and the rostral body of the corpus callosum may assume a "privileged role" subserving both. Further, we found that participant-wise estimates of white matter microstructure, weighted by cognitive performance, were largely consistent with participants' categorical diagnosis, and predictive of (cross-sectional) functional outcomes. CONCLUSIONS: The demonstrated strength of the relationship between white matter circuitry and neurocognition and social cognition underscores the potential for using relationships among these variables to identify biomarkers of functioning, with potential prognostic and therapeutic implications.

Neuroimaging features of depression-frailty phenotype in older adults: a pilot study.

OBJECTIVE: Frailty and late-life depression (LLD) often coexist and share several structural brain changes. We aimed to study the joint effect LLD and frailty have on brain structure. DESIGN: Cross-sectional study. SETTING: Academic Health Center. PARTICIPANTS: Thirty-one participants (14 LLD+Frail and 17 Never-depressed+Robust). MEASUREMENT: LLD was diagnosed by a geriatric psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition for single episode or recurrent major depressive disorder without psychotic features. Frailty was assessed using the FRAIL scale (0-5), classifying subjects as robust (0), prefrail (1-2), and frail (3-5). Participants underwent T1-weighted magnetic resonance imaging in which covariance analysis of subcortical volumes and vertex-wise analysis of cortical thickness values were performed to access changes in grey matter. Participants also underwent diffusion tensor imaging in which tract-based spatial statistics was used with voxel-wise statistical analysis on fractional anisotropy and mean diffusion values to assess changes in white matter (WM). RESULTS: We found a significant difference in mean diffusion values (48,225 voxels; peak voxel: pFWER=0.005, MINI coord. (X,Y,Z) = -26,-11,27) between the LLD-Frail group and comparison group. The corresponding effect size (f=0.808) was large. CONCLUSION: We showed the LLD+Frailty group is associated with significant microstructural changes within WM tracts compared to Never-depressed+Robust individuals. Our findings indicate the possibility of a heightened neuroinflammatory burden as a potential mechanism underlying the co-occurrence of both conditions and the possibility of a depression-frailty phenotype in older adults.

Associations between locus coeruleus integrity and diagnosis, age, and cognitive performance in older adults with and without late-life depression: An exploratory study.

Late-life depression (LLD) is a risk factor for age-dependent cognitive deterioration. Norepinephrine-related degeneration in the locus coeruleus (LC) may explain this link. To examine the LC norepinephrine system in vivo, we acquired neuromelanin-sensitive MRI (NM-MRI) in a sample of 48 participants, including 25 with LLD (18 women, age 68.08 ± 5.41) and 23 never-depressed comparison participants (ND, 12 women, age 70 ± 8.02), matched on age and cognitive status. We employed a semi-automated procedure to segment the LC into three bilateral sections along its rostro-caudal extent, and calculated relative contrast as a proxy of integrity. Then, we examined associations between integrity and LLD diagnosis, age, and cognition, as measured via the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Delis-Kaplan Executive Function System (D-KEFS). We did not identify an effect of LLD diagnosis nor age on LC integrity, but exploratory canonical correlation analysis across the combined participant sample revealed a strong (Rc = 0.853) and significant multivariate relationship between integrity and cognition (Wilks' λ = 0.03, F(84, 162.44) = 1.66, p = <.01). The first and only significant variate explained 72.83% model variance, and linked better attention and delayed memory performance, faster processing speed, and lower verbal fluency performance with higher integrity in the right rostral but lower integrity in the left caudal LC. Our results complement prior evidence of LC involvement in cognition in healthy older adults, and extend this association to individuals with LLD.

Novel polygenic risk score as a translational tool linking depression-related changes in the corticolimbic transcriptome with neural face processing and anhedonic symptoms.

Convergent data from imaging and postmortem brain transcriptome studies implicate corticolimbic circuit (CLC) dysregulation in the pathophysiology of depression. To more directly bridge these lines of work, we generated a novel transcriptome-based polygenic risk score (T-PRS), capturing subtle shifts toward depression-like gene expression patterns in key CLC regions, and mapped this T-PRS onto brain function and related depressive symptoms in a nonclinical sample of 478 young adults (225 men; age 19.79 +/- 1.24) from the Duke Neurogenetics Study. First, T-PRS was generated based on common functional SNPs shifting CLC gene expression toward a depression-like state. Next, we used multivariate partial least squares regression to map T-PRS onto whole-brain activity patterns during perceptual processing of social stimuli (i.e., human faces). For validation, we conducted a comparative analysis with a PRS summarizing depression risk variants identified by the Psychiatric Genomics Consortium (PGC-PRS). Sex was modeled as moderating factor. We showed that T-PRS was associated with widespread reductions in neural response to neutral faces in women and to emotional faces and shapes in men (multivariate p < 0.01). This female-specific reductions in neural response to neutral faces was also associated with PGC-PRS (multivariate p < 0.03). Reduced reactivity to neutral faces was further associated with increased self-reported anhedonia. We conclude that women with functional alleles mimicking the postmortem transcriptomic CLC signature of depression have blunted neural activity to social stimuli, which may be expressed as higher anhedonia.

Ciftify: A framework for surface-based analysis of legacy MR acquisitions.

The preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI "grayordinate" file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this 'legacy' data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to "legacy" data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with fMRIPrep following the BIDS-app framework. Finally, we present the package and comment on future neuroinformatics advances that may accelerate the movement to a CIFTI-based grayordinate framework.

Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls.

Schizophrenia and bipolar disorder (BD) may be disorders of accelerated aging. Direct comparison of healthy aging populations with schizophrenia and BD patients across the adult lifespan may help inform this theory. In total, 225 individuals (91 healthy controls, 81 schizophrenia, 53 euthymic BD) underwent 3T T1-weighted magnetic resonance imaging, diffusion tensor imaging, and cognitive testing. We analyzed associations among age, diagnosis, and cognition with cortical thickness and fractional anisotropy (FA) using general linear models. We then assessed "brain age" using a random forest algorithm, which was also assessed in an independent sample (n = 147). Participants with schizophrenia had lower cortical thickness and FA compared with the other two groups, most prominently in fronto-temporal circuitry. These brain changes were more evident in younger participants than in older ones, yet were associated with cognitive performance independent of diagnosis. Predicted age was 8 years greater than chronological age in individuals with schizophrenia in the first sample and 6 years greater in the second sample. Predicted and chronological age were not different in BD. Differences in brain circuitry are present from illness onset most prominently in schizophrenia and to a lesser extent in BD. These results support a non-progressive "early hit" hypothesis/etiology of illness in the major psychoses. Brain age differences support the hypothesized early aging mechanism in schizophrenia but not in BD.

Separable and Replicable Neural Strategies During Social Brain Function in People With and Without Severe Mental Illness.

OBJECTIVE: Case-control study design and disease heterogeneity may impede biomarker discovery in brain disorders, including serious mental illnesses. To identify biologically and/or behaviorally driven as opposed to diagnostically driven subgroups of individuals, the authors used hierarchical clustering to identify individuals with similar patterns of brain activity during a facial imitate/observe functional MRI task. METHODS: Participants in the Social Processes Initiative in Neurobiology of the Schizophrenia(s) study (N=179; 109 with a schizophrenia spectrum disorder and 70 healthy control participants) underwent MRI scanning at three sites. Hierarchical clustering was used to identify new data-driven groups of participants; differences on social and neurocognitive tests completed outside the scanner were compared among the new groups. RESULTS: Three clusters with distinct patterns of neural activity were found. Cluster membership was not related to diagnosis or scan site. The largest cluster consisted of "typical activators," with activity in the canonical "simulation" circuit. The other clusters represented a "hyperactivating" group and a "deactivating" group. Between-participants Euclidean distances were smaller within clusters than within site or diagnostics groups. The deactivating group had the highest social cognitive and neurocognitive test scores. The hierarchical clustering analysis was repeated on a replication sample (N=108; 32 schizophrenia spectrum disorder, 37 euthymic bipolar disorder, and 39 healthy control participants), which exhibited the same three cluster patterns. CONCLUSIONS: The study findings demonstrate replicable differing patterns of neural activity among individuals during a socio-emotional task, independent of DSM diagnosis or scan site. The findings may provide objective neuroimaging endpoints (biomarkers) for subgroups of individuals in target engagement research aimed at enhancing cognitive performance independent of diagnostic category.

A longitudinal human phantom reliability study of multi-center T1-weighted, DTI, and resting state fMRI data.

Multi-center MRI studies can enhance power, generalizability, and discovery for clinical neuroimaging research in brain disorders. Here, we sought to establish the utility of a clustering algorithm as an alternative to more traditional intra-class correlation coefficient approaches in a longitudinal multi-center human phantom study. We completed annual reliability scans on 'travelling human phantoms'. Acquisitions across sites were harmonized prospectively. Twenty-seven MRI sessions were available across four participants, scanned on five scanners, across three years. For each scan, three metrics were extracted: cortical thickness (CT), white matter fractional anisotropy (FA), and resting state functional connectivity (FC). For each metric, hierarchical clustering (Ward's method) was performed. The cluster solutions were compared to participant and scanner using the adjusted Rand index (ARI). For all metrics, data clustered by participant rather than by scanner (ARI > 0.8 comparing clusters to participants, ARI < 0.2 comparing clusters to scanners). These results demonstrate that hierarchical clustering can reliably identify structural and functional scans from different participants imaged on different scanners across time. With increasing interest in data-driven approaches in psychiatric and neurologic brain imaging studies, our findings provide a framework for multi-center analytic approaches aiming to identify subgroups of participants based on brain structure or function.

Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects.

BACKGROUND: Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome). METHODS: We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance. RESULTS: Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75). CONCLUSIONS: A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.

Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder.

BACKGROUND: Recent advances in techniques using functional magnetic resonance imaging data demonstrate individually specific variation in brain architecture in healthy individuals. To our knowledge, the effects of individually specific variation in complex brain disorders have not been previously reported. METHODS: We developed a novel approach (Personalized Intrinsic Network Topography, PINT) for localizing individually specific resting-state networks using conventional resting-state functional magnetic resonance imaging scans. Using cross-sectional data from participants with autism spectrum disorder (ASD; n = 393) and typically developing (TD) control participants (n = 496) across 15 sites, we tested: 1) effect of diagnosis and age on the variability of intrinsic network locations and 2) whether prior findings of functional connectivity differences in persons with ASD compared with TD persons remain after PINT application. RESULTS: We found greater variability in the spatial locations of resting-state networks within individuals with ASD compared with those in TD individuals. For TD persons, variability decreased from childhood into adulthood and increased in late life, following a U-shaped pattern that was not present in those with ASD. Comparison of intrinsic connectivity between groups revealed that the application of PINT decreased the number of hypoconnected regions in ASD. CONCLUSIONS: Our results provide a new framework for measuring altered brain functioning in neurodevelopmental disorders that may have implications for tracking developmental course, phenotypic heterogeneity, and ultimately treatment response. We underscore the importance of accounting for individual variation in the study of complex brain disorders.

Impoverished descriptions of familiar routes in three cases of hippocampal/medial temporal lobe amnesia.

Recent research has challenged classic theories of hippocampal function in spatial memory with findings that the hippocampus may be necessary for detailed representations of environments learned long ago, but not for remembering the gist or schematic aspects that are sufficient for navigating within those environments (Rosenbaum et al., 2000; Rosenbaum, Winocur, Binns, & Moscovitch, 2012). We aimed to probe further distinctions between detailed and schematic representations of familiar environments in three cases of hippocampal/medial temporal lobe (MTL) amnesia by testing them on a route description task and mental navigation tasks that assess the identity and location of landmarks, and distances and directions between them. The amnesic cases could describe basic directions along known, imagined routes, estimate distance and direction between well-known landmarks, and produce sketch maps with accurate layouts, suggestive of intact schematic representations. However, findings that their route descriptions lack richness of detail, along with impoverished sketch maps and poor landmark recognition, substantiates previous findings that detailed representations are hippocampus-dependent.