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The dysconnection hypothesis of schizophrenia posits that widespread synaptic inefficiencies lead to altered macroscale brain connectivity, contributing to symptom severity and cognitive deficits in individuals with schizophrenia spectrum disorders (SSD). Emerging evidence suggests that physical exercise may help to ameliorate these connectivity abnormalities and associated clinical impairments.
Aims
This study investigated whether reductions in functional dysconnectivity following exercise therapy were associated with clinical improvements in individuals with SSD. In addition, it explored the genetic underpinnings of these changes using imaging transcriptomics.
Method
Using data from the ESPRIT C3 trial, we analysed 23 SSD patients (seven female) undergoing aerobic exercise or flexibility, strengthening and balance training over 6 months. Functional dysconnectivity, assessed at baseline and post-intervention relative to a healthy reference sample (n = 200), was evaluated at the whole-brain, network and regional levels. Linear mixed effect models and voxel-wise Pearson’s correlations were used to assess exercise-induced changes and clinical relevance.
Results
Functional dysconnectivity significantly decreased (d = −2.73, P < 0.001), and this decrease was primarily linked to enhanced oligodendrocyte-related gene expression. Reductions in the default-mode network were correlated with improved global functioning, whereas changes in insular regions were associated with symptom severity and functioning. Dysconnectivity reductions in somatomotor and frontoparietal networks were correlated with total symptom improvements, and changes in language-related regions (e.g. Broca’s area) were linked to cognitive benefits.
Conclusions
Our findings support the role of oligodendrocyte pathology in SSD and suggest that targeting dysconnectivity in the default-mode, salience and language networks may enhance global functioning, symptom severity and cognitive impairments.
Structural abnormalities in cortical and subcortical brain regions are consistently observed in schizophrenia; however, substantial inter-individual variability complicates identifying clear neurobiological biomarkers. The Person-Based Similarity Index (PBSI) quantifies individual structural variability; however, its applicability across schizophrenia stages remains unclear. This study aimed to compare cortical and subcortical structural variability in recent-onset and chronic schizophrenia and explore associations with clinical measures.
Methods:
Neuroimaging data from 41 patients with recent-onset schizophrenia, 32 with chronic schizophrenia, and 59 healthy controls were analyzed. The PBSI scores were calculated for cortical thickness, surface area, cortical gray matter volume, and subcortical volumes. Group differences in PBSI scores were assessed using linear regression and analysis of variance. Correlations between the PBSI scores and clinical measures were also examined.
Results:
Both patients with recent-onset and chronic schizophrenia exhibited significantly lower PBSI scores than healthy controls, indicating greater morphometric heterogeneity. However, significant differences between the recent-onset and chronic patient groups were limited to subcortical and cortical thickness PBSI scores. Correlations between PBSI scores and clinical symptoms are sparse and primarily restricted to surface area variability and symptom severity in patients with recent-onset schizophrenia.
Conclusion:
Patients with schizophrenia show marked structural brain heterogeneity compared with healthy controls, which is detectable even in the early stages of the illness. Although there were few differences in PBSI scores between the recent-onset and chronic schizophrenia groups and limited correlations between PBSI scores and clinical measures, the PBSI may still provide valuable insights into individual differences contributing to clinical heterogeneity in schizophrenia.
Cutting-edge computational tools like artificial intelligence, data scraping, and online experiments are leading to new discoveries about the human mind. However, these new methods can be intimidating. This textbook demonstrates how Big Data is transforming the field of psychology, in an approachable and engaging way that is geared toward undergraduate students without any computational training. Each chapter covers a hot topic, such as social networks, smart devices, mobile apps, and computational linguistics. Students are introduced to the types of Big Data one can collect, the methods for analyzing such data, and the psychological theories we can address. Each chapter also includes discussion of real-world applications and ethical issues. Supplementary resources include an instructor manual with assignment questions and sample answers, figures and tables, and varied resources for students such as interactive class exercises, experiment demos, articles, and tools.
As we think and act, the brain is constantly producing Big Data in the firing of its neurons and in the connections that are strengthened and weakened. This chapter discusses how we can study the brain and the Big Data that it creates. First, we discuss how clever behavioral tasks, looking at development and other species, and natural variation across people are our first tools for understanding the brain. Next, we delve into describing several popular brain imaging methods – direct recording, electroencephalography, magnetoencephalography, magnetic resonance imaging, and a few others. We discuss how to interpret the Big Data shown by brain maps, and some Big Data methods like multiple comparisons correction to consider when viewing this data. Finally, we end the chapter discussing the ethical question of whether such neuroimaging allows mindreading.
Heightened reactivity in the amygdala measured by functional magnetic resonance imaging during emotional processing is considered a potential biomarker for clinical depression. Still, it is unknown whether this is also true for depressive symptoms in the general population, and – when in remission after recurrent depressive episodes – it is associated with future episodes.
Methods
Using the UK Biobank population study (n = 11,334), we investigated the association of amygdala reactivity during negative facial stimuli, focusing on lifetime depression (trait), depressive symptoms (state), and the modulating effect of antidepressant (AD) treatment thereof. We employed normative modeling (NM) to better incorporate population heterogeneity of the amygdala activity.
Results
In line with a previous study, depressive symptoms (state) over the last 2 weeks were not associated with the amygdala reactivity signal. Rather, our results indicate a significant positive association (p = 0.03, ω2 = 0.001) between amygdala response and the recurrence of depressive episodes (trait). Longitudinal analysis revealed that the group that had experienced a single depressive episode before showed a significantly increased amygdala response after additional episodes (p = 0.03, ω2 = 0.017). ADs were not associated with amygdala response directly, but decreased associations within episode recurrence severity.
Conclusions
The amygdala response to negative stimuli was associated with an individual’s risk of recurrence of depressive episodes, and AD treatment reduced these associations. This study highlights the relevance of amygdala reactivity as a trait, but not a state biomarker for (recurrent) depression. Moreover, it demonstrates the benefit of applying NM in the context of population data.
There is a considerable overlap in clinical features and genetics between schizophrenia (SZ) and bipolar disorder (BD). Previous neuroimaging research has demonstrated common and distinct brain damage patterns between relatives (RELs) of SZ and BD patients, suggesting shared and differential genetic influences on the brain. Despite an increasing recognition that disorders localize better to distributed brain networks than individual brain regions, studies investigating network localization of genetic risk for SZ and BD are still lacking.
Methods
To address this gap, we initially identified brain functional and structural damage locations in SZ- and BD-RELs from 103 published studies with 2364 SZ-RELs, 864 BD-RELs, and 4114 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional MRI datasets, we mapped these affected brain locations to four disorder-susceptibility networks.
Results
SZ-susceptibility functional damage network primarily involved the executive control and salience networks, while its BD-counterpart principally implicated the default mode and basal ganglia networks. SZ-susceptibility structural damage network predominantly involved the auditory and default mode networks, yet its BD-counterpart mainly implicated the language and executive control networks. Although these networks showed cross-disorder inconsistencies when focusing on either imaging modality alone, the combined SZ- and BD-susceptibility brain damage networks had a substantially increased spatial similarity.
Conclusions
These findings may support the concept that SZ and BD represent distinct diagnostic categories from a neurobiological perspective, helping to clarify the common network substrates via which the shared genetic mechanisms underlying both disorders give rise to their overlapping clinical phenotypes.
Pavlovian conditioning paradigms have been a stalwart of animal research on fear learning for over a century. Recent advances in cognitive neuroscience research have led to new insights into the neural mechanisms of how humans learn to associate cues with threats, how these representations become bound to contextual features of the environment, and how they generalize to stimuli that are perceptually or conceptually related. By integrating information gleaned from patients with brain lesions, scalp electrophysiology, neuroimaging, and intracranial recordings, researchers are assembling a dynamic view of the distributed brain activity that generates conditioned fear responses. Innovative virtual reality technology, computational modeling, and multivariate analysis tools have further refined a scientific understanding of the component processes involved, which can inform future clinical interventions for treating fear- and anxiety-related disorders.
Although perceived threats in a child’s social environment, including in the family, school, and neighborhood, are known to increase risk for adolescent psychopathology, the underlying biological mechanisms remain unclear. To investigate, we examined whether perceived social threats were associated with the functional connectivity of large-scale cortical networks in early adolescence, and whether such connectivity differences mediated the development of subsequent mental health problems in youth.
Methods
Structural equation models were used to analyze data from 8,690 youth (50% female, 45% non-White, age 9–10 years) drawn from the large-scale, nationwide Adolescent Brain Cognitive Development study that has 21 clinical and research sites across the United States. Data were collected from 2016 to 2018.
Results
Consistent with Social Safety Theory, perceived social threats were prospectively associated with mental health problems both 6 months (standardized $ \beta =0.27,p<.001 $) and 30 months ($ \beta =0.14,p<.001 $) later. Perceived social threats predicted altered connectivity patterns within and between the default mode (DMN), dorsal attention (DAN), frontoparietal (FPN), and cingulo-opercular (CON) networks. In turn, hypoconnectivity within the DMN and FPN – and higher (i.e., less negative) connectivity between DMN-DAN, DMN-CON, and FPN-CON – mediated the association between perceived social threats and subsequent mental health problems.
Conclusions
Perceiving social threats in various environments may alter neural connectivity and increase the risk of psychopathology in youth. Therefore, parenting, educational, and community-based interventions that bolster social safety may be helpful.
Developmental trauma increases psychosis risk in adulthood and is associated with poor prognosis and treatment response. It has been proposed that developmental trauma may give rise to a distinct psychosis phenotype. Our aim was to explore this by systematically reviewing neuroimaging studies of brain structure and function in adults with psychosis diagnoses, according to whether or not they had survived developmental trauma. We registered our search protocol in PROSPERO (CRD42018105021).
Method
We systematically searched literature databases for relevant studies published before May 2024. We identified 31 imaging studies (n = 1,761 psychosis patients, n = 1,775 healthy controls or healthy siblings).
Results
Developmental trauma was associated with global and regional differences in gray matter; corticolimbic structural dysconnectivity; a potentiated threat detection system; dysfunction in regions associated with mentalization; and elevated striatal dopamine synthesis capacity.
Conclusion
These findings warrant further research to elucidate vulnerability and resilience mechanisms for psychosis in developmental trauma survivors.
Imaging genetics is an interdisciplinary field that integrates neuroimaging and genetic data to improve behavioral prediction and investigate the genetic bases of brain structure and function. It aims to identify associations between genetic markers and brain imaging phenotypes, with a behavioral or clinical trait as the outcome of interest. Since its emergence nearly 30 years ago, the field has advanced substantially, fueled by rapid developments in molecular-genetic and neuroimaging techniques. These advances have opened new avenues for exploring individual differences in cognitive and socio-emotional development and their links to neurodevelopmental disorders. This systematic review examined studies published between 2020 and 2024, focusing on developmental psychopathology. We screened 769 articles from PubMed/MEDLINE and PsycINFO and selected 42 publications that met specific inclusion criteria for review. The studies were categorized into three groups based on the developmental ages in which conditions typically develop: birth/early childhood, late childhood or early adolescence, and late adolescence. Although the field has seen considerable progress, multiple challenges in data acquisition, analysis, and interpretation remain. Larger sample sizes and novel analytical techniques are crucial for the continued advancement of imaging genetics, with animal studies offering potential complementary insights.
Identifying key areas of brain dysfunction in mental illness is critical for developing precision diagnosis and treatment. This study aimed to develop region-specific brain aging trajectory prediction models using multimodal magnetic resonance imaging (MRI) to identify similarities and differences in abnormal aging between bipolar disorder (BD) and major depressive disorder (MDD) and pinpoint key brain regions of structural and functional change specific to each disorder.
Methods
Neuroimaging data from 340 healthy controls, 110 BD participants, and 68 MDD participants were included from the Taiwan Aging and Mental Illness cohort. We constructed 228 models using T1-weighted MRI, resting-state functional MRI, and diffusion tensor imaging data. Gaussian process regression was used to train models for estimating brain aging trajectories using structural and functional maps across various brain regions.
Results
Our models demonstrated robust performance, revealing accelerated aging in 66 gray matter regions in BD and 67 in MDD, with 13 regions common to both disorders. The BD group showed accelerated aging in 17 regions on functional maps, whereas no such regions were found in MDD. Fractional anisotropy analysis identified 43 aging white matter tracts in BD and 39 in MDD, with 16 tracts common to both disorders. Importantly, there were also unique brain regions with accelerated aging specific to each disorder.
Conclusions
These findings highlight the potential of brain aging trajectories as biomarkers for BD and MDD, offering insights into distinct and overlapping neuroanatomical changes. Incorporating region-specific changes in brain structure and function over time could enhance the understanding and treatment of mental illness.
Neuroimaging research must reflect the diversity of the populations it aims to serve. This scoping review examines the demographic characteristics (age, sex, race and ethnicity, and geographic representation) of participants in brain MRI and positron-emission tomography studies conducted in Quebec, Canada, between 1992 and 2023. A total of 1,549 studies, representing 62,555 participants, were identified through searches of Medline, Embase and Google Scholar, following JBI methodology. The vast majority of studies (92.7%) were conducted in Montreal, with limited representation from other urban centers and almost none from rural areas. Reporting of demographic variables was inconsistent: 22.1% of studies failed to report participant age adequately, and 20.3% did not fully report sex. Race and ethnicity were the most poorly documented, with fewer than 4% of studies reporting this information. Among the 2,396 participants with recorded race and ethnicity, 94.2% were categorized as White, highlighting a significant mismatch with Quebec’s population diversity. Healthy participant samples were largely concentrated in the 20–35 age range, while clinical populations generally aligned with the expected age of disease onset. These findings reveal major gaps in demographic representation and reporting in Quebec-based neuroimaging research. Improving diversity and transparency is essential to ensure that neuroimaging findings are generalizable, equitable and clinically meaningful. We recommend the adoption of standardized demographic reporting formats, such as the Brain Imaging Data Structure, and broader recruitment efforts to capture underrepresented groups, including rural residents and racial and ethnic minorities.
Preclinical evidence suggests that diazepam enhances hippocampal γ-aminobutyric acid (GABA) signalling and normalises a psychosis-relevant cortico-limbic-striatal circuit. Hippocampal network dysconnectivity, particularly from the CA1 subfield, is evident in people at clinical high-risk for psychosis (CHR-P), representing a potential treatment target. This study aimed to forward-translate this preclinical evidence.
Methods
In this randomised, double-blind, placebo-controlled study, 18 CHR-P individuals underwent resting-state functional magnetic resonance imaging twice, once following a 5 mg dose of diazepam and once following a placebo. They were compared to 20 healthy controls (HC) who did not receive diazepam/placebo. Functional connectivity (FC) between the hippocampal CA1 subfield and the nucleus accumbens (NAc), amygdala, and ventromedial prefrontal cortex (vmPFC) was calculated. Mixed-effects models investigated the effect of group (CHR-P placebo/diazepam vs. HC) and condition (CHR-P diazepam vs. placebo) on CA1-to-region FC.
Results
In the placebo condition, CHR-P individuals showed significantly lower CA1-vmPFC (Z = 3.17, PFWE = 0.002) and CA1-NAc (Z = 2.94, PFWE = 0.005) FC compared to HC. In the diazepam condition, CA1-vmPFC FC was significantly increased (Z = 4.13, PFWE = 0.008) compared to placebo in CHR-P individuals, and both CA1-vmPFC and CA1-NAc FC were normalised to HC levels. In contrast, compared to HC, CA1-amygdala FC was significantly lower contralaterally and higher ipsilaterally in CHR-P individuals in both the placebo and diazepam conditions (lower: placebo Z = 3.46, PFWE = 0.002, diazepam Z = 3.33, PFWE = 0.003; higher: placebo Z = 4.48, PFWE < 0.001, diazepam Z = 4.22, PFWE < 0.001).
Conclusions
This study demonstrates that diazepam can partially restore hippocampal CA1 dysconnectivity in CHR-P individuals, suggesting that modulation of GABAergic function might be useful in the treatment of this clinical group.
Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.
Methods
T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).
Results
PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).
Conclusions
PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.
Stress leads to neurobiological changes, and failure to regulate these can contribute to chronic psychiatric issues. Despite considerable research, the relationship between neural alterations in acute stress and coping with chronic stress is unclear. This longitudinal study examined whole-brain network dynamics following induced acute stress and their role in predicting chronic stress vulnerability.
Methods
Sixty military pre-deployment soldiers underwent a lab-induced stress task where subjective stress and resting-state functional magnetic resonance imaging were acquired repeatedly (before stress, after stress, and at recovery, 90 min later). Baseline depression and post-traumatic stress symptoms were assessed, and again a year later during military deployment. We used the Leading Eigenvector Dynamic Analysis framework to characterize changes in whole-brain dynamics over time. Time spent in each state was compared across acute stress conditions and correlated with psychological outcomes.
Results
Findings reveal significant changes at the network level from acute stress to recovery, where the frontoparietal and subcortical states decreased in dominance in favor of the default mode network, sensorimotor, and visual states. A significant normalization of the frontoparietal state activity was related to successful psychological recovery. Immediately after induced stress, a significant increase in the lifetimes of the frontoparietal state was associated with higher depression symptoms (r = 0.49, p < .02) and this association was also observed a year later following combat exposure (r = 0.49, p < .009).
Conclusions
This study revealed how acute stress-related neural alterations predict chronic stress vulnerability. Successful recovery from acute stress involves reducing cognitive–emotional states and enhancing self-awareness and sensory–perceptual states. Elevated frontoparietal activity is suggested as a neural marker of vulnerability to chronic stress.
Predicting long-term outcome trajectories in psychosis remains a crucial and challenging goal in clinical practice. The identification of reliable neuroimaging markers has often been hindered by the clinical and biological heterogeneity of psychotic disorders and the limitations of traditional case-control methodologies, which often mask individual variability. Recently, normative brain charts derived from extensive magnetic resonance imaging (MRI) data-sets covering the human lifespan have emerged as a promising biologically driven solution, offering a more individualised approach.
Aims
To examine how deviations from normative cortical and subcortical grey matter volume (GMV) at first-episode psychosis (FEP) onset relate to symptom and functional trajectories.
Method
We leveraged the largest available brain normative model (N > 100 000) to explore normative deviations in a sample of over 240 patients with schizophrenia spectrum disorders who underwent MRI scans at the onset of FEP and received clinical follow-up at 1, 3 and 10 years.
Results
Our findings reveal that deviations in regional normative GMV at FEP onset are significantly linked to overall long-term clinical trajectories, modulating the effect of time on both symptom and functional outcome. Specifically, negative deviations in the left superior temporal gyrus and Broca’s area at FEP onset were notably associated with a more severe progression of positive and negative symptoms, as well as with functioning trajectories over time.
Conclusions
These results underscore the potential of brain developmental normative approaches for the early prediction of disorder progression, and provide valuable insights for the development of preventive and personalised therapeutic strategies.