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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.
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.
Although the neural basis of TOT states is not yet fully understood, we do know that (1) TOTs may involve competition among candidate word representations and the involvement of the anterior cingulate cortex in conflict detection; (2) TOTs may involve recruitment of the prefrontal cortex, possibly to exert top-down control over memory-retrieval efforts such as by priming situationally relevant memory representations or otherwise initiating goal-oriented behavior that is aimed at resolving the TOT state; (3) left hemisphere temporal regions known to be involved in language are likely involved, both in the stalling of retrieval mechanisms that is taking place to prevent successful retrieval of the target word, and also possibly in where the presumed competition among candidate word representations is taking place; and (4) future research is clearly needed in order to determine the extent to which people undergoing left hemisphere sourced anomia experience increases in subjective sensations of TOT states compared to other populations and how separable these TOT states may be from access to partial target attributes.
Posttraumatic stress disorder (PTSD) is a heterogenous disorder with frequent diagnostic comorbidity. Research has deciphered this heterogeneity by identifying PTSD subtypes and their neural biomarkers. This review summarizes current approaches, symptom-based group-level and data-driven approaches, for generating PTSD subtypes, providing an overview of current PTSD subtypes and their neural correlates. Additionally, we systematically assessed studies to evaluate the influence of comorbidity on PTSD subtypes and the predictive utility of biotypes for treatment outcomes. Following the PRISMA guidelines, a systematic search was conducted to identify studies employing brain imaging techniques, including functional magnetic resonance imaging (fMRI), structural MRI, diffusion-weighted imaging (DWI), and electroencephalogram (EEG), to identify biomarkers of PTSD subtypes. Study quality was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We included 53 studies, with 44 studies using a symptom-based group-level approach, and nine studies using a data-driven approach. Findings suggest biomarkers across the default-mode network (DMN) and the salience network (SN) throughout multiple subtypes. However, only six studies considered comorbidity, and four studies tested the utility of biotypes in predicting treatment outcomes. These findings highlight the complexity of PTSD’s heterogeneity. Although symptom-based and data-driven methods have advanced our understanding of PTSD subtypes, challenges remain in addressing the impact of comorbidities and the limited validation of biotypes. Future studies with larger sample sizes, brain-based data-driven approaches, careful account for comorbidity, and rigorous validation strategies are needed to advance biologically grounded biotypes across mental disorders.
The brain’s default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. However, the genetic basis linking sociability with DMN function remains underexplored. This study aimed to elucidate the shared genetics and causal relationship between sociability and DMN-related resting-state functional MRI (rs-fMRI) traits.
Methods
We conducted a comprehensive genomic analysis using large-scale genome-wide association study (GWAS) summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N = 34,691–342,461). We performed global and local genetic correlations analyses and bi-directional Mendelian randomization (MR) to assess shared and causal effects. We prioritized genes influencing both sociability and rs-fMRI traits by combining expression quantitative trait loci MR analyses, the CELLECT framework – integrating single-nucleus RNA sequencing (snRNA-seq) data with GWAS – and network propagation within a protein–protein interaction network.
Results
Significant local genetic correlations were identified between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the cingulate and angular/temporal cortices. MR analyses suggested potential causal effects of sociability on 12 rs-fMRI traits. Seventeen genes were highly prioritized, with LINGO1, ELAVL2, and CTNND1 emerging as top candidates. Among these, DRD2 was also identified, serving as a robust internal validation of our approach.
Conclusions
By combining genomic and transcriptomic data, our gene prioritization strategy may serve as a blueprint for future studies. Our findings can guide further research into the biological mechanisms underlying sociability and its role in the development, prognosis, and treatment of neuropsychiatric disorders.
Attention is critical to our daily lives, from simple acts of reading or listening to a conversation to the more demanding situations of trying to concentrate in a noisy environment or driving on a busy roadway. This book offers a concise introduction to the science of attention, featuring real-world examples and fascinating studies of clinical disorders and brain injuries. It introduces cognitive neuroscience methods and covers the different types and core processes of attention. The links between attention, perception, and action are explained, along with exciting new insights into the brain mechanisms of attention revealed by cutting-edge research. Learning tools – including an extensive glossary, chapter reviews, and suggestions for further reading – highlight key points and provide a scaffolding for use in courses. This book is ideally suited for graduate or advanced undergraduate students as well as for anyone interested in the role attention plays in our lives.
This chapter introduces the methods used in cognitive neuroscience to study language processing in the human brain. It begins by explaining the basics of neural signaling (such as the action potential) and then delves into various brain imaging techniques. Structural imaging methods like MRI and diffusion tensor imaging are covered, which reveal the brain’s anatomy. The chapter then explores functional imaging approaches that measure brain activity, including EEG, MEG, and fMRI. Each method’s spatial and temporal resolution are discussed. The text also touches on non-invasive brain stimulation techniques like TMS and tES. Throughout, the chapter emphasizes the importance of converging evidence from multiple methods to draw robust conclusions about brain function. Methodological considerations such as the need for proper statistical comparisons are highlighted. The chapter concludes with a discussion of how neurodegenerative diseases have informed our understanding of language in the brain. Overall, this comprehensive overview equips readers with the foundational knowledge needed to critically evaluate neuroscience research on language processing.
In the human body, the brain is the organ that underpins mental processing. Mental processes use the interconnected structures of the brain to synthesize the experience of the internal and external environment. Psychiatric symptoms reflect dysfunctional mental processing. These abnormalities in mental processes could arise from any combination of functional or structural changes in the brain. Neuroimaging technology provides us with methods to study these abnormal functions and structures of the brain.
The success of deep brain stimulation (DBS) relies on applying carefully titrated therapeutic stimulation at specific targets. Once implanted, the electrical stimulation parameters at each electrode contact can be modified. Iteratively adjusting the stimulation parameters enables testing for the optimal stimulation settings. Due to the large parameter space, the currently employed empirical testing of individual parameters based on acute clinical response is not sustainable. Within the constraints of short clinical visits, optimization is particularly challenging when clinical features lack immediate feedback, as seen in DBS for dystonia and depression and with the cognitive and axial side effects of DBS for Parkinson’s disease. A personalized approach to stimulation parameter selection is desirable as the increasing complexity of modern DBS devices also expands the number of available parameters. This review describes three emerging imaging and electrophysiological methods of personalizing DBS programming. Normative connectome-base stimulation utilizes large datasets of normal or disease-matched connectivity imaging. The stimulation location for an individual patient can then be varied to engage regions associated with optimal connectivity. Electrophysiology-guided open- and closed-loop stimulation capitalizes on the electrophysiological recording capabilities of modern implanted devices to individualize stimulation parameters based on biomarkers of success or symptom onset. Finally, individual functional MRI (fMRI)-based approaches use fMRI during active stimulation to identify parameters resulting in characteristic patterns of functional engagement associated with long-term treatment response. Each method provides different but complementary information, and maximizing treatment efficacy likely requires a combined approach.
Fully updated and revised, Cognitive and Social Neuroscience of Aging, 2nd Edition provides an accessible introduction to aging and the brain. Now with full color throughout, it includes over fifty figures illustrating key research findings and anatomical diagrams. Adopting an integrative perspective across domains of psychological function, this edition features expanded coverage of multivariate methods, moral judgments, cognitive reserve, prospective memory, event boundaries, and individual differences related to aging, including sex, race, and culture. Although many declines occur with age, cognitive neuroscience research reveals plasticity and adaptation in the brain as a normal function of aging. With this perspective in mind, the book emphasizes the ways in which neuroscience methods have enriched and changed thinking about aging.
The choroid plexus produces cerebrospinal fluid, which is crucial for glymphatic system function. Evidence suggests that changes in the volume of the choroid plexus may be associated with glymphatic system function. Therefore, this study aimed to investigate alterations in choroid plexus volume in patients with migraines compared with healthy controls.
Methods:
We enrolled 59 patients with migraines (39 and 20 with episodic and chronic migraines, respectively) and 61 healthy controls. All participants underwent brain magnetic resonance imaging, including three-dimensional T1-weighted imaging. We analyzed and compared choroid plexus volumes between patients with episodic migraines, those with chronic migraines and healthy controls. Additionally, we evaluated the association between choroid plexus volume and the clinical characteristics of patients with migraine.
Results:
The choroid plexus volume in patients with chronic migraines was higher than that in healthy controls (2.018 vs. 1.698%, p = 0.002) and patients with episodic migraines (2.018 vs. 1.680%, p = 0.010). However, no differences were observed in choroid plexus volumes between patients with episodic migraine and healthy controls. Choroid plexus volume was positively correlated with age in patients with migraines (r = 0.301, p = 0.020) and in healthy controls (r = 0.382, p = 0.002).
Conclusion:
We demonstrated significant enlargement of the choroid plexus in patients with chronic migraine compared with healthy controls and those with episodic migraine. This finding suggests that chronic migraine may be associated with glymphatic system dysfunction.
Tremor, which is defined as an oscillatory and rhythmic movement of a body part, is the most common movement disorder worldwide. The most frequent tremor syndromes are tremor in Parkinson’s disease, essential tremor, and dystonic tremor syndromes, whereas Holmes tremor, orthostatic tremor, and palatal tremor are less common in clinical practice. The pathophysiology of tremor consists of enhanced oscillatory activity in brain circuits, which are ofen modulated by tremor-related afferent signals from the periphery. The cerebello-thalamo-cortical circuit and the basal ganglia play a key role in most neurologic tremor disorders, but with different roles in each disorder. Here we review the pathophysiology of tremor, focusing both on neuronal mechanisms that promote oscillations (automaticity and synchrony) and circuit-level mechanisms that drive and maintain pathologic oscillations.
The complexity of movement disorders poses challenges for clinical management and research. Functional imaging with PET or SPECT allows in-vivo assessment of the molecular underpinnings of movement disorders, and biomarkers can aid clinical decision making and understanding of pathophysiology, or determine patient eligibility and endpoints in clinical trials. Imaging targets traditionally include functional processes at the molecular level, typically neurotransmitter systems or brain metabolism, and more recently abnormal protein accumulation, a pathologic hallmark of neurodegenerative diseases. Functional neuroimaging provides complementary information to structural neuroimaging (e.g. anatomic MRI), as molecular/functional changes can present in the absence of, prior to, or alongside structural brain changes. Movement disorder specialists should be aware of the indications, advantages and limitations of molecular functional imaging. An overview is given of functional molecular imaging in movement disorders, covering methodologic background information, typical molecular changes in common movement disorders, and emerging topics with potential for greater future importance.
The clinical and pathologic hallmarks of Parkinson’s disease (PD) are motor parkinsonism due to underlying progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta accompanied by an accumulation of intracytoplasmic protein inclusions known as Lewy bodies and Lewy neurites. The diagnostic criteria/guidelines based on the UK Parkinson’s Disease Society Brain Bank clinical diagnostic criteria have guided clinicians and researchers in the diagnosis of PD for many decades. This chapter discusses whether this description represents our current understanding of PD, and why it is time to integrate new research findings and accommodate our definition and diagnostic criteria of PD, such as Parkinson-associated non-motor symptoms, genetics, biomarkers, imaging findings, or heterogeneity of phenotypes and underlying molecular mechanisms. In 2015, the International Parkinson and Movement Disorder Society published clinical diagnostic criteria for Parkinson’s disease, which were designed specifically for use in research but also as a general guide to clinical diagnosis of PD. These criteria and some of their limitations are also discussed.
Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.
Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission (‘trait depression group’), those with large longitudinal severity changes in depression symptomatology (‘state depression group’), and their respective matched control groups (total analytic n = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.
We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.