Atypical brain aging and its association with working memory performance in major depressive disorder.

BACKGROUND: Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories. METHODS: 304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response. RESULTS: Brain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS: Consistent with prior work on machine learning models that predict "brain age", brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.

An empirical analysis of structural neuroimaging profiles in a staging model of depression.

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression.

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis.

BACKGROUND: Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI. METHODS: Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data. RESULTS: Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections. CONCLUSIONS: Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

Cerebello-limbic functional connectivity patterns in youth at clinical high risk for psychosis.

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.

Social functioning and brain imaging in individuals at clinical high-risk for psychosis: A systematic review.

Impairments in social functioning are a core impairment in psychosis and are associated with poor outcomes. These deficits are found in those at clinical high-risk (CHR) for psychosis, and can persist even in the absence of transition. However, the neurobiological underpinnings of social functioning remain unclear, therefore we conducted a systematic review of brain metrics that have been associated with social functioning in youth at CHR for psychosis. Five databases (MEDLINE, CINAHL, EBM reviews, Embase, and PsycINFO) were searched from inception to May 5, 2020. Studies were selected if they examined brain imaging, and social functioning in youth at CHR for psychosis. Of the 9629 citations found through online database searching, 12 studies with 696 CHR participants met inclusion criteria. Too few studies were focused on the same brain region using the same methodology to perform a meta-analysis, however, loci within the prefrontal cortex were most often associated with social functioning. Few studies have linked social functioning to brain imaging metrics, suggesting that future work should focus on this relationship.

White matter microstructure in youth at risk for serious mental illness: A comparative analysis.

Identifying biomarkers of serious mental illness, such as altered white matter microstructure, can aid in early diagnosis and treatment. White matter microstructure was assessed using constrained spherical deconvolution of diffusion imaging data in a sample of 219 youth (age 12-25 years, 64.84% female) across 8 sites. Participants were classified as healthy controls (HC; n = 47), familial risk for serious mental illness (n = 31), mild-symptoms (n = 37), attenuated syndromes (n = 66), or discrete disorder (n = 38) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations (ATR), inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF). Linear mixed effects models showed a significant effect of age on MD of the left ATR, left SLF, and left UF, and a significant effect of group on FA for all tracts examined. For most tracts, the discrete disorder group had significantly lower FA than other groups, and the attenuated syndromes group had higher FA compared to HC, with few differences between the remaining groups. White matter differences in MDD are most evident in individuals following illness onset, as few significant differences were observed in the risk phase.

Functional imaging in youth at risk for transdiagnostic serious mental illness: Initial results from the PROCAN study.

BACKGROUND: In their early stages, serious mental illnesses (SMIs) are often indistinguishable from one another, suggesting that studying alterations in brain activity in a transdiagnostic fashion could help to understand the neurophysiological origins of different SMI. The purpose of this study was to examine brain activity in youth at varying stages of risk for SMI using functional magnetic resonance imaging tasks (fMRI) that engage brain systems believed to be affected. METHODS: Two hundred and forty three participants at different stages of risk for SMI were recruited to the Canadian Psychiatric Risk and Outcome (PROCAN) study, however only 179 were scanned. Stages included asymptomatic participants at no elevated risk, asymptomatic participants at elevated risk due to family history, participants with undifferentiated general symptoms of mental illness, and those experiencing attenuated versions of diagnosable psychiatric illnesses. The fMRI tasks included: (1) a monetary incentive delay task; (2) an emotional Go-NoGo and (3) an n-back working memory task. RESULTS: Strong main effects with each of the tasks were found in brain regions previously described in the literature. However, there were no significant differences in brain activity between any of the stages of risk for SMI for any of the task contrasts, after accounting for site, sex and age. Furthermore, results indicated no significant differences even when participants were dichotomized as asymptomatic or symptomatic. CONCLUSIONS: These results suggest that univariate BOLD responses during typical fMRI tasks are not sensitive markers of SMI risk and that further study, particularly longitudinal designs, will be necessary to understand brain changes underlying the early stages of SMI.

Progression from being at-risk to psychosis: next steps.

Over the past 20 years there has been a great deal of research into those considered to be at risk for developing psychosis. Much has been learned and studies have been encouraging. The aim of this paper is to offer an update of the current status of research on risk for psychosis, and what the next steps might be in examining the progression from CHR to psychosis. Advances have been made in accurate prediction, yet there are some methodological issues in ascertainment, diagnosis, the use of data-driven selection methods and lack of external validation. Although there have been several high-quality treatment trials the heterogeneity of this clinical high-risk population has to be addressed so that their treatment needs can be properly met. Recommendations for the future include more collaborative research programmes, and ensuring they are accessible and harmonized with respect to criteria and outcomes so that the field can continue to move forward with the development of large collaborative consortiums as well as increased funding for multisite projects.

Hallucination-Specific structure-function associations in schizophrenia.

Combining structural (sMRI) and functional magnetic resonance imaging (fMRI) data in schizophrenia patients with and without auditory hallucinations (9 SZ_AVH, 12 SZ_nAVH), 18 patients with bipolar disorder, and 22 healthy controls, we examined whether cortical thinning was associated with abnormal activity in functional brain networks associated with auditory hallucinations. Language-task fMRI data were combined with mean cortical thickness values from 148 brain regions in a constrained principal component analysis (CPCA) to identify brain structure-function associations predictable from group differences. Two components emerged from the multimodal analysis. The "AVH component" highlighted an association of frontotemporal and cingulate thinning with altered brain activity characteristic of hallucinations among patients with AVH. In contrast, the "Bipolar component" distinguished bipolar patients from healthy controls and linked increased activity in the language network with cortical thinning in the left occipital-temporal lobe. Our findings add to a body of evidence of the biological underpinnings of hallucinations and illustrate a method for multimodal data analysis of structure-function associations in psychiatric illness.

Brain changes associated with negative symptoms in clinical high risk for psychosis: A systematic review.

The negative symptoms of schizophrenia are linked to poorer functional outcomes and decreases in quality of life, and are often the first to develop in individuals who are at clinical high risk (CHR) for psychosis. However, the accompanying neurobiological changes are poorly understood. Therefore, we conducted a systematic review of the studies that have examined the brain metrics associated with negative symptoms in those at CHR. Electronic databases were searched from inception to August 2019. Studies were selected if they mentioned negative symptoms in youth at CHR for psychosis, and brain imaging. Of 261 citations, 43 studies with 2144 CHR participants met inclusion criteria. Too few studies were focused on the same brain regions using similar neuroimaging methods to perform a meta-analysis, however, the results of this systematic review suggest a relationship between negative symptom increases and decreases in grey matter. The paucity of studies linking changes in brain structure and function with negative symptoms in those at CHR suggests that future work should focus on examining these relationships.

White Matter Connectivity in Youth at Risk for Serious Mental Illness: A Longitudinal Analysis.

Longitudinal changes in white matter connectivity were assessed in a sample of youth at-risk for serious mental illness (n=183; age 12-25). Diffusion tensor imaging (DTI) was acquired at baseline and 12 months from youth recruited across two sites and classified as healthy controls (n=36), familial risk (n=30), mild-symptoms (n=41), attenuated syndromes (n=70), or transition (n=9) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, and uncinate fasciculus. MANCOVA analysis controlling for site, sex, and age showed no significant group differences in FA and MD at baseline or at 12 months. Linear mixed effects analysis showed a significant effect for time for most white matter tracts, but no effect for group, or group by time interaction. Transdiagnostic risk groups have similar profiles of WM connectivity and similar rates of change over time.

Stress impacts sensory variability through cortical sensory activity motifs.

Medically unexplained symptoms in depression are common. These individual-specific complaints are often considered an 'idiom of distress', yet animal studies suggest that cortical sensory representations are flexible and influenced by spontaneous cortical activity. We hypothesized that stress would reveal activity dynamics in somatosensory cortex resulting in greater sensory-evoked response variability. Using millisecond resolution in vivo voltage sensitive dye (VSD) imaging in mouse neocortex, we characterized spontaneous regional depolarizations within limb and barrel regions of somatosensory cortex, or spontaneous sensory motifs, and their influence on sensory variability. Stress revealed an idiosyncratic increase in spontaneous sensory motifs that is normalized by selective serotonin reuptake inhibitor treatment. Spontaneous motif frequency is associated with increased variability in sensory-evoked responses, and we optogenetically demonstrate that regional depolarization in somatosensory cortex increases sensory-evoked variability for seconds. This reveals a putative circuit level target for changes in sensory processing and for unexplained physical complaints in stress-related psychopathology.

Aberrant limbic brain structures in young individuals at risk for mental illness.

AIM: Alterations in limbic structures may be present before the onset of serious mental illness, but whether subfield-specific limbic brain changes parallel stages in clinical risk is unknown. To address this gap, we compared the hippocampus, amygdala, and thalamus subfield-specific volumes in adolescents at various stages of risk for mental illness. METHODS: MRI scans were obtained from 182 participants (aged 12-25 years) from the Canadian Psychiatric Risk and Outcome study. The sample comprised of four groups: asymptomatic youth at risk due to family history of mental illness (Stage 0, n = 32); youth with early symptoms of distress (Stage 1a, n = 41); youth with subthreshold psychotic symptoms (Stage 1b, n = 72); and healthy comparison participants with no family history of serious mental illness (n = 37). Analyses included between-group comparisons of brain measurements and correlational analyses that aimed to identify significant associations between neuroimaging and clinical measurements. A machine-learning technique examined the discriminative properties of the clinical staging model. RESULTS: Subfield-specific limbic volume deficits were detected at every stage of risk for mental illness. A machine-learning classifier identified volume deficits within the body of the hippocampus, left amygdala nuclei, and medial-lateral nuclei of the thalamus that were most informative in differentiating between risk stages. CONCLUSION: Aberrant subfield-specific changes within the limbic system may serve as biological evidence to support transdiagnostic clinical staging in mental illness. Differential patterns of volume deficits characterize those at risk for mental illness and may be indicative of a risk-stage progression.

Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report.

Finding a clinically useful neuroimaging biomarker that can predict treatment response in patients with major depressive disorder (MDD) is challenging, in part because of poor reproducibility and generalizability of findings across studies. Previous work has suggested that posterior hippocampal volumes in depressed patients may be associated with antidepressant treatment outcomes. The primary purpose of this investigation was to examine further whether posterior hippocampal volumes predict remission following antidepressant treatment. Magnetic resonance imaging (MRI) scans from 196 patients with MDD and 110 healthy participants were obtained as part of the first study in the Canadian Biomarker Integration Network in Depression program (CAN-BIND 1) in which patients were treated for 16 weeks with open-label medication. Hippocampal volumes were measured using both a manual segmentation protocol and FreeSurfer 6.0. Baseline hippocampal tail (Ht) volumes were significantly smaller in patients with depression compared to healthy participants. Larger baseline Ht volumes were positively associated with remission status at weeks 8 and 16. Participants who achieved early sustained remission had significantly greater Ht volumes compared to those who did not achieve remission by week 16. Ht volume is a prognostic biomarker for antidepressant treatment outcomes in patients with MDD.

Functional brain networks involved in lexical decision.

Functional magnetic resonance imaging (fMRI)5 studies on lexical decision (LD)6 attempting to isolate the brain network underlying access to lexical representations can be confounded by attentional and response processes. However, manipulating the "wordlikeness" of the LD stimuli can facilitate functional interpretation of each emerging brain network, providing principles for separation of attentional demand from linguistic processing. This is because activation of difficult-to-access lexical representations (for obscure real words), and avoidance of interfering word properties (for wordlike non-words), are both generally attentionally demanding. Therefore, congruent patterns of activation would be predicted for general-attention-responsive networks, but opposing patterns for language-responsive networks. 59 healthy adults performed a LD task, and multidimensional functional connectivity analysis was used to extract three functional brain networks. A linguistic processing network (LPN) was separated from attention/response networks anatomically (LPN included Broca's and Wernicke's areas), but also temporally by showing reduced activation for the most attentionally demanding condition (i.e., wordlike non-words). This demonstrated that during LD in fMRI a network involved in linguistic processing can be disentangled from attention- and response-specific networks, using a combination of experimental design and multidimensional analysis methods.

Predictors of Transition to Psychosis in Individuals at Clinical High Risk.

PURPOSE OF REVIEW: Current research is examining predictors of the transition to psychosis in youth who are at clinical high risk based on attenuated psychotic symptoms (APS). Determining predictors of the development of psychosis is important for an improved understanding of mechanisms as well as the development of preventative strategies. The purpose is to review the most recent literature identifying predictors of the transition to psychosis in those who are already assessed as being at risk. RECENT FINDINGS: Multidomain models, in particular, integrated models of symptoms, social functioning, and cognition variables, achieve better predictive performance than individual factors. There are many methodological issues; however, several solutions have now been described in the literature. For youth who already have APS, predicting who may go on to later develop psychosis is possible. Several studies are underway in large consortiums that may overcome some of the methodological concerns and develop improved means of prediction.

Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.

Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues - Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.

Functional brain networks involved in reality monitoring.

Source monitoring refers to the recollection of variables that specify the context and conditions in which a memory episode was encoded. This process involves using the qualitative and quantitative features of a memory trace to distinguish its source. One specific class of source monitoring is reality monitoring, which involves distinguishing internally generated from externally generated information, that is, memories of imagined events from real events. The purpose of the present study was to identify functional brain networks that underlie reality monitoring, using an alternative type of source monitoring as a control condition. On the basis of previous studies on self-referential thinking, it was expected that a medial prefrontal cortex (mPFC) based network would be more active during reality monitoring than the control condition, due to the requirement to focus on a comparison of internal (self) and external (other) source information. Two functional brain networks emerged from this analysis, one reflecting increasing task-related activity, and one reflecting decreasing task-related activity. The second network was mPFC based, and was characterized by task-related deactivations in areas resembling the default-mode network; namely, the mPFC, middle temporal gyri, lateral parietal regions, and the precuneus, and these deactivations were diminished during reality monitoring relative to source monitoring, resulting in higher activity during reality monitoring. This result supports previous research suggesting that self-referential thinking involves the mPFC, but extends this to a network-level interpretation of reality monitoring.

Reduced functional connectivity during controlled semantic integration in schizophrenia: A multivariate approach.

Impairment in controlled semantic association is a central feature of schizophrenia, and the goal of the current functional magnetic resonance imaging study was to identify the neural correlates of this impairment. Thirty people with schizophrenia and 30 healthy age- and gender-matched control subjects performed a task requiring participants to match word pairs that varied in semantic distance (distant vs. close). A whole-brain multivariate connectivity analysis revealed three functional brain networks of primary interest engaged by the task: two configurations of a multiple demands network, in which brain activity did not differ between groups, and a semantic integration network, in which coordinated activity was reduced in schizophrenia patients relative to healthy controls, for distantly relative to closely related word pairs. The hypoactivity during controlled semantic integration in schizophrenia reported here, combined with hyperactivity in automatic semantic association reported in the literature, suggests an imbalance between controlled integration and automatic association. This provides a biological basis for Bleuler's concept of schizophrenia as a "split mind" arising from an impaired ability to form coherent associations between semantic concepts.