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ImportanceAmong patients seeking care for musculoskeletal conditions, there is mixed evidence regarding whether traditional, structure-based care is associated with improvement in patients’ mental health.ObjectiveTo determine whether improvements in physical function and pain interference are associated with meaningful improvements in anxiety and depression symptoms among patients seeking musculoskeletal care.DesignRetrospective cohort study from June 22, 2015 to February 9, 2022.SettingOrthopedic department of a tertiary care US academic medical center.ParticipantsConsecutive sample of adult patients who presented to the musculoskeletal clinic 4 to 6 times during the study period and completed Patient-Reported Outcomes Measurement Information System (PROMIS) measures as standard care at each visit.ExposurePROMIS Physical Function and Pain Interference scores.Main Outcomes and MeasuresLinear mixed effects models were used to determine whether: 1) PROMIS Anxiety and 2) PROMIS Depression scores improved as a function of improved PROMIS Physical Function or Pain Interference scores, after controlling for age, gender, race, and PROMIS Depression (for the Anxiety model) and PROMIS Anxiety (for the Depression model). Clinically meaningful improvement was defined as ≥3.0 points for PROMIS Anxiety and ≥3.2 points for PROMIS Depression.ResultsAmong 11,236 patients (mean [SD] age 57 [16] years), 9,706 (86%) were White, and 7,218 (64%) were women. Improvements in physical function (β=-0.14 [95% CI -0.15– -0.13], pConclusions and RelevanceIn this cohort study, substantial improvements in physical function and pain interference were required for association with any clinically meaningful improvement in anxiety symptoms and were not associated with any meaningful improvement in depression symptoms. Among patients seeking musculoskeletal care, musculoskeletal clinicians and patients cannot assume that addressing physical health will result in improved symptoms of depression or potentially even sufficiently improved symptoms of anxiety.Key PointsQuestionAmong patients seeking musculoskeletal care, are improvements in physical function and pain interference associated with meaningful changes in symptoms of anxiety and depression?FindingsIn this large cohort study, improvement by ≥2.3 population-level standard deviations (SD) on PROMIS Physical Function or ≥1.2 SD on PROMIS Pain Interference were required for any association with meaningful improvement in anxiety symptoms. Improvements in physical function and pain interference were not associated with meaningfully improved depression symptoms.MeaningMusculoskeletal clinicians and patients cannot assume that exclusively addressing the physical aspect of a musculoskeletal condition will improve symptoms of depression or potentially even anxiety.

Addressing heterogeneity in depression is critically important to overcome replicability challenges and gain insights into neural etiology. We developed a novel hierarchical framework to systematically disentangle clinical and neurobiological sources of heterogeneity utilizing population data from the UK Biobank. Firstly, we defined patient subgroups who uniquely shared isolated clinical characteristics of depression (e.g., symptoms of anhedonia, depressed mood, and somatic disturbance; severity indices of lifetime chronicity and acute impairment; and late onset). Our results revealed distinct neurobiological features robustly associated with each clinically isolated subgroup, providing symptom-level insights into the neural etiology of depression, and supporting a one-to-one mapping between clinical and neurobiological sources of heterogeneity. Secondly, we investigated residual neurobiological heterogeneity within each subgroup using data-driven clustering. Our findings revealed stable neurobiological clusters that differed in cognitive ability within two clinical subgroups (chronicity and acute impairment), providing evidence that multiple neurobiological mechanisms may give rise to the same clinical presentation (many-to-one mapping).

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self‐report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS‐4) score which we recommend for state‐dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging‐derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test–retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health., In this study, we aimed to provide a shared foundation for UK Biobank neuroimaging research into mental health with a focus on anxiety and depression. We introduced and validated the Recent Depressive Symptoms (RDS‐4) score which we recommend for state‐dependent and longitudinal research in the UK Biobank. Our imaging results showed a significant multivariate relationship between imaging‐derived phenotypes and mental health, which was replicable.

Late-life depression (LLD) is a major source of global morbidity and mortality, influenced by multiple risk factors. Yet, a major challenge is to quantify the degree of resilience or vulnerability to LLD at the individual level, which could offer neurobiological insight and ultimately inform future interventions and treatment. Here, applying a non-parametric regression model to the UK Biobank data (N=1,988), we quantified brain-based resilience and vulnerability to LLD and tested whether risk factors could explain individual differences in the estimated magnitude of such neural resilience and vulnerability. Our results show that social isolation was positively associated with the median magnitude of neural vulnerability whereas material deprivation was negatively associated with the greatest neural resilience (top 10 percentile). These results together highlight the importance of social interaction and access to sufficient resources and services in diminishing neural vulnerability and promoting neural resilience to LLD, respectively. Our findings therefore provide insights into preventive strategies for LLD, and thus are of importance for policy makers as well as the broader society.

Contains fulltext : 288247.pdf (Publisher’s version ) (Open Access) Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was highly replicable. Conversely, effect sizes for individual IDPs were very small and contributions of individual IDPs to the multivariate result did not replicate. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.

Links between cognitive deficits and psychiatric disorders have been studied predominantly at the group level, leaving unique individual characteristics largely elusive. Here, we applied normative modeling to UK Biobank data (N=18,634) and estimated the individual-level interplay of large-scale brain networks over time (i.e., dynamic brain state) as a function of general cognitive ability. Abnormality in such brain states was linked to individual variation in mental health. Specifically, brain state measures including fractional occupancy that indicates the brain state probability over time were estimated using a Hidden Markov Model, followed by a Gaussian process regression to estimate the normative range of these brain state measures from general cognitive ability. Abnormality scores per participant were quantified to represent the degree of deviations relative to the estimated population norm. We found significant associations between the abnormality scores of several brain states and individuals’ overall mental health. Our findings suggest potential impact of mental health on dynamic brain states that subserve cognitive functions and shed light on the relevant brain mechanisms underlying cognitive deficits in mental illness.

Functional magnetic resonance imaging (fMRI) has been widely recognized as a powerful non-invasive approach to study brain function by measuring brain activity over time. Such brain activity can result from task-induced cognitive or emotional state changes (task-evoked fMRI), or from unregulated processes (task-free or resting-state fMRI). In fMRI, the blood-oxygenation-level-dependent (BOLD) signal is utilized as a proxy of neural activity, which is captured in the time-series data. Due to a number of noise sources, careful denoising or data preprocessing steps are required to ensure data quality. Appropriate model specifications are also vital to address research questions effectively. In this chapter, we briefly introduce two types of fMRI studies, and discuss typical fMRI data preprocessing steps. Statistical analyses for task-evoked and resting-state fMRI are described, with example research questions separately. The chapter closes with suggestions for efficient communication about fMRI results.

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data., NeuroImage, 243, ISSN:1053-8119, ISSN:1095-9572

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs., NeuroImage, 222, ISSN:1053-8119, ISSN:1095-9572

Contains fulltext : 229125.pdf (Publisher’s version ) (Closed access) A key principle of brain organization is the functional integration of brain regions into interconnected networks. Functional MRI scans acquired at rest offer insights into functional integration via patterns of coherent fluctuations in spontaneous activity, known as functional connectivity. These patterns have been studied intensively and have been linked to cognition and disease. However, the field is fractionated. Diverging analysis approaches have segregated the community into research silos, limiting the replication and clinical translation of findings. A primary source of this fractionation is the diversity of approaches used to reduce complex brain data into a lower-dimensional set of features for analysis and interpretation, which we refer to as brain representations. In this Primer, we provide an overview of different brain representations, lay out the challenges that have led to the fractionation of the field and that continue to form obstacles for convergence, and propose concrete guidelines to unite the field.

We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach.

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behaviour. For example, studies have used ‘functional connectivity fingerprints’ to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits., eLife digest People differ a lot from one another in terms of their personality, behaviour and lifestyle. This individuality is attributed to the different regions in the brain, and the strength of communication between them. The connectivity pattern between these areas is thought to be as unique as a fingerprint. If the connections are weak or disrupted it can play a role in conditions such as schizophrenia, depression or Alzheimer’s disease. It is thought that the strength of the connection depends on how strongly the nerve cells in these regions communicate. But are these individual differences solely caused by different strengths of connection, or could other factors contribute to them? Now, Bijsterbosch et al. found that the size, shape and exact position of the brain regions was also strongly linked to the different behaviours of individuals. The study used brain scans, behavioural tests and questionnaires from a large database about lifestyle choices and demographics, to analyse the relationship between the different brain features of healthy individuals. The results showed that the variations in the brain regions were linked to many behavioural factors including intelligence, life satisfaction, drug use and aggression problems. Moreover, Bijsterbosch et al. showed that the existing methods for estimating the strength of connection between brain regions could reveal more about the spatial layout of these regions than the actual connection strength between them. This suggests that new approaches are needed to properly evaluate the strength of the connections. Some psychiatric and neurological diseases may be associated with changes in size and position of the different regions in the brain. In future, the findings of this study could be applied to individuals affected by such conditions, to see if the location of a region could be used as a diagnostic indicator.

Using data from 45,615 people, Kaufmann et al. compare the gaps between brain age and chronological age in a number of brain disorders and study the relationship of these gaps with genetics. Their research shows that the brains of individuals with a range of different brain disorders, such as dementia and schizophrenia, are aging faster than normal.

Adjustments to movement in response to changes in our surroundings are common in everyday behavior. Previous research has suggested that the left pre-motor cortex (PMC) is specialized for the temporal control of movement and may play a role in temporal error correction. The aim of this study was to determine the role of the left PMC in sensorimotor timing and error correction using theta burst transcranial magnetic stimulation (TBS). In Experiment 1, subjects performed a sensorimotor synchronization task (SMS) with the left and the right hand before and after either continuous or intermittent TBS (cTBS or iTBS). Timing accuracy was assessed during synchronized finger tapping with a regular auditory pacing stimulus. Responses following perceivable local timing shifts in the pacing stimulus (phase shifts) were used to measure error correction. Suppression of the left PMC using cTBS decreased timing accuracy because subjects tapped further away from the pacing tones and tapping variability increased. In addition, error correction responses returned to baseline tap–tone asynchrony levels faster following negative shifts and no overcorrection occurred following positive shifts after cTBS. However, facilitation of the left PMC using iTBS did not affect timing accuracy or error correction performance. Experiment 2 revealed that error correction performance may change with practice, independent of TBS. These findings provide evidence for a role of the left PMC in both sensorimotor timing and error correction in both hands. We propose that the left PMC may be involved in voluntarily controlled phase correction responses to perceivable timing shifts.

Computational models have been be used to estimate the electric and magnetic fields induced by transcranial magnetic stimulation (TMS) and can provide valuable insights into the location and spatial distribution of TMS stimulation. However, there has been little translation of these findings into practical TMS research. This study uses the International 10-20 EEG electrode placement system to position a standard figure-of-eight TMS coil over 13 commonly adopted targets. Using a finite element method and an anatomically detailed and realistic head model, this study provides the first pictorial and numerical atlas of TMS-induced electric fields for a range of coil positions. The results highlight the importance of subject-specific gyral folding patterns and of local thickness of subarachnoid cerebrospinal fluid (CSF). Our modelling shows that high electric fields occur primarily on the peaks of those gyri which have only a thin layer of CSF above them. These findings have important implications for inter-individual generalizability of the TMS-induced electric field. We propose that, in order to determine with accuracy the site of stimulation for an individual subject, it is necessary to solve the electric field distribution using subject-specific anatomy obtained from a high-resolution imaging modality such as MRI.

Gravitational forces may lead to local changes in subarachnoid cerebrospinal fluid (CSF) layer thickness, which has important implications for neurophysiological modulation and recording techniques. This study examines the effect of gravitational pull associated with different head positions on the distribution of subarachnoid CSF using structural magnetic resonance imaging. Images of seven subjects in three different positions (supine, left lateral and prone) were statistically compared. Results suggest that subarachnoid CSF volume decreases on the side of the head closest to the ground, due to downward brain movement with gravity. These findings warrant future research into currently unexplored gravitation-induced changes in regional subarachnoid CSF thickness.

Background: The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study. Methods/design: A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012–2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60–85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study. Discussion: The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.

Sustained anxiety about potential future negative events is an important feature of anxiety disorders. In this study, we used a novel anticipation of shock paradigm to investigate individual differences in functional connectivity during prolonged threat of shock. We examined the correlates of between-participant differences in trait anxious affect and induced anxiety, where the latter reflects changes in self-reported anxiety resulting from the shock manipulation. Dissociable effects of trait anxious affect and induced anxiety were observed. Participants with high scores on a latent dimension of anxious affect showed less increase in ventromedial pFC–amygdala connectivity between periods of safety and shock anticipation. Meanwhile, lower levels of induced anxiety were linked to greater augmentation of dorsolateral pFC–anterior insula connectivity during shock anticipation. These findings suggest that ventromedial pFC–amygdala and dorsolateral pFC–insula networks might both contribute to regulation of sustained fear responses, with their recruitment varying independently across participants. The former might reflect an evolutionarily old mechanism for reducing fear or anxiety, whereas the latter might reflect a complementary mechanism by which cognitive control can be implemented to diminish fear responses generated due to anticipation of aversive stimuli or events. These two circuits might provide complementary, alternate targets for exploration in future pharmacological and cognitive intervention studies.

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal–posterior cingulate cortex–precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity.

Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1. h of whole-brain rfMRI data at 3. T, with a spatial resolution of 2. ×. 2. ×. 2. mm and a temporal resolution of 0.7. s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses. © 2013 Elsevier Inc.

Our ability to interact physically with objects in the external world critically depends on temporal coupling between perception and movement (sensorimotor timing) and swift behavioral adjustment to changes in the environment (error correction). In this study, we investigated the neural correlates of the correction of subliminal and supraliminal phase shifts during a sensorimotor synchronization task. In particular, we focused on the role of the cerebellum because this structure has been shown to play a role in both motor timing and error correction. Experiment 1 used fMRI to show that the right cerebellar dentate nucleus and primary motor and sensory cortices were activated during regular timing and during the correction of subliminal errors. The correction of supraliminal phase shifts led to additional activations in the left cerebellum and right inferior parietal and frontal areas. Furthermore, a psychophysiological interaction analysis revealed that supraliminal error correction was associated with enhanced connectivity of the left cerebellum with frontal, auditory, and sensory cortices and with the right cerebellum. Experiment 2 showed that suppression of the left but not the right cerebellum with theta burst TMS significantly affected supraliminal error correction. These findings provide evidence that the left lateral cerebellum is essential for supraliminal error correction during sensorimotor synchronization.