Constrained principal component analysis (CPCA) with a finite impulse response (FIR) basis set was used to reveal functionally connected networks and their temporal progression over a multistage verbal working memory trial in which memory load was varied. Four components were extracted, and all showed statistically significant sensitivity to the memory load manipulation. Additionally, two of the four components sustained this peak activity, both for approximately 3 s (Components 1 and 4). The functional networks that showed sustained activity were characterized by increased activations in the dorsal anterior cingulate cortex, right dorsolateral prefrontal cortex, and left supramarginal gyrus, and decreased activations in the primary auditory cortex and "default network" regions. The functional networks that did not show sustained activity were instead dominated by increased activation in occipital cortex, dorsal anterior cingulate cortex, sensori-motor cortical regions, and superior parietal cortex. The response shapes suggest that although all four components appear to be invoked at encoding, the two sustained-peak components are likely to be additionally involved in the delay period. Our investigation provides a unique view of the contributions made by a network of brain regions over the course of a multiple-stage working memory trial.
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.
Background: Despite three decades of research, there is no consensus on a model for clinical staging of depression. One major gap is the lack of neurobiological evidence to support the construct validity of a clinical staging approach in depression. In this study, 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 the construct validity of a staging model and examine whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions.Methods: 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 decision-tree classification algorithms and feature selection procedures that were followed by between-group and linear regression characterizations.Results: Classification algorithms contrasting each clinical stage with 1:1 age-matched HC participants showed that bilateral hippocampal volume decrease and cortical thinning within cortico-limbic brain areas were the most informative features for the RD vs HC classification. FD vs HC comparisons 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. A concurrent increase in thalamic volumes with cortical thinning within the parahippocampal area in FD, but not RD individuals was significantly associated with longer illness duration.Conclusion: Our analyses 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.Funding Information: CAN-BIND is an Integrated Discovery Program carried out in partnership with, and financial support from, the Ontario Brain Institute, an independent non-profit corporation, funded partially by the Ontario government. The opinions, results and conclusions are those of the authors and no endorsement by the Ontario Brain Institute is intended or should be inferred. Additional funding is provided by the Canadian Institutes of Health Research (CIHR), Lundbeck, and Servier. Funding and/or in kind support is also provided by the investigators’ universities and academic institutions.Declaration of Interests: Dr. Sidney Kennedy has received funding for Consulting or Speaking engagements from Abbvie, Boehringer-Ingelheim, Janssen, Lundbeck, Lundbeck Institute, Merck, Otsuka Pfizer, Sunovion and Servier. Dr. Kennedy has received Research Support from Abbott, Brain Canada, CIHR (Canadian Institutes of Health Research), Janssen, Lundbeck, Ontario Brain Institute, Otsuka, Pfizer, SPOR (Canada's Strategy for Patient-Oriented Research); and has stock/stock options in Field Trip Health. No other disclosures or conflict of interests stated by authors of this work.Ethics Approval Statement: At each study site, written informed consent was obtained from subjects to participate in each respective study (clinicaltrials.gov identifiers: NCT01655706, NCT02739932, NCT02798094).
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.
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