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.

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.

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.

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 underlying detection and integration of disconfirmatory evidence.

Processing evidence that disconfirms a prior interpretation is a fundamental aspect of belief revision, and has clear social and clinical relevance. This complex cognitive process requires (at minimum) an alerting stage and an integration stage, and in the current functional magnetic resonance imaging (fMRI) study, we used multivariate analysis methodology on two datasets in an attempt to separate these sequentially-activated cognitive stages and link them to distinct functional brain networks. Thirty-nine healthy participants completed one of two versions of an evidence integration experiment involving rating two consecutive animal images, both of which consisted of two intact images of animal faces morphed together at different ratios (e.g., 70/30 bird/dolphin followed by 10/90 bird/dolphin). The two versions of the experiment differed primarily in terms of stimulus presentation and timing, which facilitated functional interpretation of brain networks based on differences in the hemodynamic response shapes between versions. The data were analyzed using constrained principal component analysis for fMRI (fMRI-CPCA), which allows distinct, simultaneously active task-based networks to be separated, and these were interpreted using both temporal (task-based hemodynamic response shapes) and spatial (dominant brain regions) information. Three networks showed increased activity during integration of disconfirmatory relative to confirmatory evidence: (1) a network involved in alerting to the requirement to revise an interpretation, identified as the salience network (dorsal anterior cingulate cortex and bilateral insula); (2) a sensorimotor response-related network (pre- and post-central gyri, supplementary motor area, and thalamus); and (3) an integration network involving rostral prefrontal, orbitofrontal and posterior parietal cortex. These three networks were staggered in their peak activity (alerting, responding, then integrating), but at certain time points (e.g., 17s after trial onset) the hemodynamic responses associated with all three networks were simultaneously active. These findings highlight distinct cognitive processes and corresponding functional brain networks underlying stages of disconfirmatory evidence integration, and demonstrate the power of multivariate and multi-experiment methodology in cognitive neuroscience.

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.

Epoch-specific functional networks involved in working memory.

Working memory (WM) is not a unitary construct. There are distinct processes involved in encoding information, maintaining it on-line, and using it to guide responses. The anatomical configurations of these processes are more accurately analyzed as functionally connected networks than collections of individual regions. In the current study we analyzed event-related functional magnetic resonance imaging (fMRI) data from a Sternberg Item Recognition Paradigm WM task using a multivariate analysis method that allowed the linking of functional networks to temporally-separated WM epochs. The length of the delay epochs was varied to optimize isolation of the hemodynamic response (HDR) for each task epoch. All extracted functional networks displayed statistically significant sensitivity to delay length. Novel information extracted from these networks that was not apparent in the univariate analysis of these data included involvement of the hippocampus in encoding/probe, and decreases in BOLD signal in the superior temporal gyrus (STG), along with default-mode regions, during encoding/delay. The bilateral hippocampal activity during encoding/delay fits with theoretical models of WM in which memoranda held across the short term are activated long-term memory representations. The BOLD signal decreases in the STG were unexpected, and may reflect repetition suppression effects invoked by internal repetition of letter stimuli. Thus, analysis methods focusing on how network dynamics relate to experimental conditions allowed extraction of novel information not apparent in univariate analyses, and are particularly recommended for WM experiments for which task epochs cannot be randomized.

The bivalency effect in task switching: event-related potentials.

During task switching, if we occasionally encounter stimuli that cue more than one task (i.e., bivalent stimuli), response slowing is observed on all univalent trials within that block, even when no features overlap with the bivalent stimuli. This observation is known as the bivalency effect. Previous fMRI work (Woodward et al., 2008) clearly suggests a role for the dorsal anterior cingulate cortex (dACC) in the bivalency effect, but the time course remains uncertain. Here, we present the first high-temporal resolution account for the bivalency effect using stimulus-locked event-related potentials. Participants alternated among three simple tasks in six experimental blocks, with bivalent stimuli appearing occasionally in bivalent blocks (blocks 2, 4, and 6). The increased reaction times for univalent stimuli in bivalent blocks demonstrate that these stimuli are being processed differently from univalent stimuli in purely univalent blocks. Frontal electrode sites captured significant amplitude differences associated with the bivalency effect within time windows 100-120 ms, 375-450 ms, and 500-550 ms, which may reflect additional extraction of visual features present in bivalent stimuli (100-120 ms) and suppression of processing carried over from irrelevant cues (375-450 ms and 500-550 ms). Our results support the fMRI findings and provide additional evidence for involvement of the dACC. Furthermore, the bivalency effect dissipated with extended practice both behaviorally and electrophysiologically. These findings are discussed in relation to the differential processing involved in a controlled response style.

Hyperintensity of functional networks involving voice-selective cortical regions during silent thought in schizophrenia.

An important aspect of schizophrenia symptomatology is inner-outer confusion, or blurring of ego boundaries, which is linked to symptoms such as hallucinations and Schneiderian delusions. Dysfunction in the cognitive processes involved in the generation of private thoughts may contribute to blurring of the ego boundaries through increased activation in functional networks including speech- and voice-selective cortical regions. In the present study, the neural underpinnings of silent verbal thought generation and speech perception were investigated using functional magnetic resonance imaging (fMRI). Functional connectivity analysis was performed using constrained principal component analysis for fMRI (fMRI-CPCA). Group differences were observable on two functional networks: one reflecting hyperactivity in speech- and voice-selective cortical regions (e.g., bilateral superior temporal gyri (STG)) during both speech perception and silent verbal thought generation, and another involving hyperactivity in a multiple demands (i.e., task-positive) network that included Wernicke's area, during silent verbal thought generation. This set of preliminary results suggests that hyperintensity of functional networks involving voice-selective cortical regions may contribute to the blurring of ego boundaries characteristic of schizophrenia.

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.

Impaired efficiency of functional networks underlying episodic memory-for-context in schizophrenia.

Memory for context and episodic memory have been identified as primary contributors to cognitive impairments in schizophrenia. This study examined neural networks involved in episodic memory-for-context in schizophrenia using a multimodal strategy including a graph theoretical approach, combined with an assessment of the contribution of structural impairments to disruption in the efficiency of functional brain networks. Twenty-three patients with schizophrenia and 33 healthy controls performed an episodic memory-for-context task while undergoing functional magnetic resonance imaging scanning. Graph theory was used to characterize the small-world properties of functional connections between activated regions, and a morphometric analysis was used to investigate schizophrenia-related structural deficits. Similar functional activations were identified in the two groups; however, although small-world properties were present in the topological organization of the functional networks in both groups, significant reductions in local, but not global, efficiency were observed in the schizophrenia group. Several key network "hub" regions related to recollection, such as the bilateral dorsal anterior cingulate gyrus, showed reduced gray matter volume in schizophrenia patients. These findings suggest that loss of gray matter volume may contribute to local inefficiencies in the architecture of the network underlying memory-for-context in schizophrenia.

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.

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.

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.

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.

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.

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.

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.

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 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.