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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Left-dominant temporal-frontal hypercoupling in schizophrenia patients with hallucinations during speech perception

BACKGROUND

Task-based functional neuroimaging studies of schizophrenia have not yet replicated the increased coordinated hyperactivity in speech-related brain regions that is reported with symptom-capture and resting-state studies of hallucinations. This may be due to suboptimal selection of cognitive tasks.

METHODS

In the current study, we used a task that allowed experimental manipulation of control over verbal material and compared brain activity between 23 schizophrenia patients (10 hallucinators, 13 nonhallucinators), 22 psychiatric (bipolar), and 27 healthy controls. Two conditions were presented, one involving inner verbal thought (in which control over verbal material was required) and another involving speech perception (SP; in which control verbal material was not required).

RESULTS

A functional connectivity analysis resulted in a left-dominant temporal-frontal network that included speech-related auditory and motor regions and showed hypercoupling in past-week hallucinating schizophrenia patients (relative to nonhallucinating patients) during SP only.

CONCLUSIONS

These findings replicate our previous work showing generalized speech-related functional network hypercoupling in schizophrenia during inner verbal thought and SP, but extend them by suggesting that hypercoupling is related to past-week hallucination severity scores during SP only, when control over verbal material is not required. This result opens the possibility that practicing control over inner verbal thought processes may decrease the likelihood or severity of hallucinations.

Decreased efficiency of task-positive and task-negative networks during working memory in schizophrenia

Working memory (WM) is one of the most impaired cognitive processes in schizophrenia. Functional magnetic resonance imaging (fMRI) studies in this area have typically found a reduction in information processing efficiency but have focused on the dorsolateral prefrontal cortex. In the current study using the Sternberg Item Recognition Test, we consider networks of regions supporting WM and measure the activation of functionally connected neural networks over different WM load conditions. We used constrained principal component analysis with a finite impulse response basis set to compare the estimated hemodynamic response associated with different WM load condition for 15 healthy control subjects and 15 schizophrenia patients. Three components emerged, reflecting activated (task-positive) and deactivated (task-negative or default-mode) neural networks. Two of the components (with both task-positive and task-negative aspects) were load dependent, were involved in encoding and delay phases (one exclusively encoding and the other both encoding and delay), and both showed evidence for decreased efficiency in patients. The results suggest that WM capacity is reached sooner for schizophrenia patients as the overt levels of WM load increase, to the point that further increases in overt memory load do not increase fMRI activation, and lead to performance impairments. These results are consistent with an account holding that patients show reduced efficiency in task-positive and task-negative networks during WM and also partially support the shifted inverted-U-shaped curve theory of the relationship between WM load and fMRI activation in schizophrenia.

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.

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.

Aberrant connectivity during self-other source monitoring in schizophrenia

Numerous investigations into schizophrenia have reported impairment in self-other source monitoring, and studies on healthy subjects have suggested that the medial prefrontal cortex (mPFC) is a critical region underlying self-monitoring abilities. In the current study, we examined the mPFC-related modulation of other brain regions in schizophrenia during self-other monitoring using a psychophysiological interaction approach. Twenty-three patients with schizophrenia and 33 healthy controls performed a self-other source monitoring task while undergoing functional magnetic resonance imaging (fMRI) scanning. Independent component analysis was used to identify the mPFC region of interest, and the averaged mPFC time course was extracted and entered into a general linear regression model for use with the psychophysiological interaction analysis, with Self vs. Other monitoring being the psychological condition of interest. Results suggested that connectivity between the mPFC and the left superior temporal gyrus (LSTG) was greater in the Other than the Self condition for the healthy subjects, but this was reversed for the schizophrenia patients, such that mPFC-LSTG connectivity was greater during Self than the Other condition. The modified functional connectivity associated with the performance of recollection of self-source information suggests that schizophrenia patients invoke circuits normally involved in retrieving other-generated information when processing self-generated information, thereby providing a possible biological basis for the self-other confusion characteristic of schizophrenia.

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.

Anterior cingulate cortex signals the requirement to break inertia when switching tasks: a study of the bivalency effect

When switching tasks, if stimuli are presented that cue two of the tasks in the task set (i.e., bivalent stimuli), performance slowing is observed on all tasks, including those not cued by the bivalent stimulus. This slowing has been coined the bivalency effect, and may reflect adaptive tuning of the response style under conditions that appear to require adjustments in control over the course of action. Recent work on the function of the dorsal anterior cingulate (dACC) cortex has suggested that this neural region may be recruited under such conditions. In the current task switching study, we used tightly matched experimental and control conditions to isolate the bivalency effect. As predicted, dACC activation was associated with the bivalency effect, supporting an account stating that the role of the dACC is to signal a break in task inertia in order to adaptively tune the response style due to conditions that may require adjustments in control over the course of action. This result may extend the conflict monitoring account of dACC activation to situations where conflict occurred on past trials (i.e., conflict is not elicited by the current stimulus), and/or may support a more generalized account of dACC function involving monitoring internal states for conditions that may require adjustments in control over the course of action.