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Persistence of training-induced visual improvements after occipital stroke
Damage to the primary visual cortex causes homonymous visual impairments that appear to benefit from visual discrimination training. However, whether improvements persist without continued training remains to be determined and was the focus of the present study. After a baseline assessment visit, 20 participants trained twice daily in their blind-field for a minimum of six months (median=155 sessions), using a motion discrimination and integration task. At the end of training, a return study visit was used to assess recovery. Three months later, 14 of the participants returned for a third study visit to assess persistence of recovery. At each study visit, motion discrimination and integration thresholds, Humphrey visual fields, and structural MRI scans were collected. Immediately after training, all but four participants showed improvements in the trained discrimination task, and shrinkage of the perimetrically-defined visual defect. While these gains were sustained in seven out of eleven participants who improved with training, four participants lost their improvement in motion discrimination thresholds at the follow-up visit. Persistence of recovery was not related to age, time since lesion, number of training sessions performed, proportion of V1 damaged, deficit size, or optic tract degeneration measured from structural MRI scans. The present findings underscore the potential of extended visual training to induce long-term improvements in stroke-induced vision loss. However, they also highlight the need for further investigations to better understand the mechanisms driving recovery, its persistence post-training, and especially heterogeneity among participants.
Investigating the impact of electroconvulsive therapy on brain networks and sleep: an observational study protocol.
INTRODUCTION: Electroconvulsive therapy (ECT) is a highly effective treatment for refractory depression, but it may also cause cognitive side effects. Despite decades of use, the mechanisms by which ECT exerts both its antidepressant and cognitive effects are still poorly understood, with the latter substantially limiting referral and adherence to therapy. ECT induces changes in correlated neural activity-functional connectivity-across various brain networks, which may underlie both its clinical efficacy and associated cognitive side effects. Electroencephalography (EEG) could address these knowledge gaps by identifying biomarkers that predict therapeutic outcomes or cognitive side effects. Such developments could ultimately improve patient selection and adherence. Such markers likely span large-scale functional brain networks or temporal dynamics of brain activity during sleep. We hypothesise that enhancement in slow wave sleep mediates the relationship between antidepressant effects and changes in functional connectivity throughout the course of ECT. METHODS AND ANALYSIS: Disruptions of Brain Networks and Sleep by Electroconvulsive Therapy (DNS-ECT) is an ongoing observational study investigating the impact of ECT on large-scale brain functional networks and their relationships to sleep slow waves, an EEG marker linked to synaptic plasticity. The novelty of this study stems from our focus on the assessment of EEG markers during sleep, wakefulness and ECT-induced seizures over the course of therapy. Graph-based network analyses of high-density EEG signals allow characterisation of functional networks locally in specific subnetworks and globally over large-scale functional networks. Longitudinal assessments of EEG alongside clinical and cognitive outcomes provide a unique opportunity to improve our understanding of the circuit mechanisms underlying the development of cognitive impairments and antidepressant effects incurred during ECT. ETHICS AND DISSEMINATION: Recruitment for this 5-year study started in March 2023. Dissemination plans include presentations at scientific conferences and peer-reviewed publications. This study has been registered with ClinicalTrials.gov registry under identifier. TRIAL REGISTRATION NUMBER: NCT05905705.
Relationships between depression, anxiety, and motivation in the real-world: Effects of physical activity and screentime.
BACKGROUND: Mood and anxiety disorders are highly prevalent and comorbid worldwide, with variability in symptom severity that fluctuates over time. Digital phenotyping, a growing field that aims to characterize clinical, cognitive and behavioral features via personal digital devices, enables continuous quantification of symptom severity in the real world, and in real-time. METHODS: In this study, N=114 individuals with a mood or anxiety disorder (MA) or healthy controls (HC) were enrolled and completed 30-days of ecological momentary assessments (EMA) of symptom severity. Novel real-world measures of anxiety, distress and depression were developed based on the established Mood and Anxiety Symptom Questionnaire (MASQ). The full MASQ was also completed in the laboratory (in-lab). Additional EMA measures related to extrinsic and intrinsic motivation, and passive activity data were also collected over the same 30-days. Mixed-effects models adjusting for time and individual tested the association between real-world symptom severity EMA and the corresponding full MASQ sub-scores. A graph theory neural network model (DEPNA) was applied to all data to estimate symptom interactions. RESULTS: There was overall good adherence over 30-days (MA=69.5%, HC=71.2% completion), with no group difference (t(58)=0.874, p=0.386). Real-world measures of anxiety/distress/depression were associated with their corresponding MASQ measure within the MA group (t's > 2.33, p's < 0.024). Physical activity (steps) was negatively associated with real-world distress and depression (IRRs > 0.93, p's ≤ 0.05). Both intrinsic and extrinsic motivation were negatively associated with real-world distress/depression (IRR's > 0.82, p's < 0.001). DEPNA revealed that both extrinsic and intrinsic motivation significantly influenced other symptom severity measures to a greater extent in the MA group compared to the HC group (extrinsic/intrinsic motivation: t(46) = 2.62, p < 0.02, q FDR < 0.05, Cohen's d = 0.76; t(46) = 2.69, p < 0.01, q FDR < 0.05, Cohen's d = 0.78 respectively), and that intrinsic motivation significantly influenced steps (t(46) = 3.24, p < 0.003, q FDR < 0.05, Cohen's d = 0.94). CONCLUSIONS: Novel real-world measures of anxiety, distress and depression significantly related to their corresponding established in-lab measures of these symptom domains in individuals with mood and anxiety disorders. Novel, exploratory measures of extrinsic and intrinsic motivation also significantly related to real-world mood and anxiety symptoms and had the greatest influencing degree on patients' overall symptom profile. This suggests that measures of cognitive constructs related to drive and activity may be useful in characterizing phenotypes in the real-world.
Preprocessing for fMRI (and a little bit for diffusion MRI)
This chapter is about the processing of fMRI data that is performed after the acquisition of the data, and before using a statistical model to try to infer what parts of the brain were involved in the task. It is aimed primarily at people who are about to undertake their first fMRI project, or who have already completed one or two and who want a greater understanding of the analysis steps they have been taught to perform. It will focus on explaining the different steps that constitute what is traditionally referred to as “preprocessing.” But it will also touch upon some of the MR-physics relevant to the acquisition, as well as on the statistical modeling that is used for the inference, as we think this makes it easier to understand why we do some of the preprocessing. The aim of preprocessing is twofold: 1. To improve location accuracy, i.e. to ensure that we are able to accurately assign an observed activation to the right part of the brain anatomy. 2. To increase statistical power, i.e. to try to detect and remove as much variance unrelated to the experimental task as possible, thereby making it more likely that any activation is statistically significant. The division is not necessarily as clear cut as that. For example, correcting for subject movement over time will primarily be aimed at increasing statistical power, but it is easily realized that large, uncorrected, movement would also impair localization. This chapter will give an overview of the following preprocessing steps. Distortion correction : fMRI images are distorted. It will be explained why this is, and how it can be corrected. Movement correction: Subject movement is the greatest source of unwanted variance in fMRI. The different ways in which movement can affect the data will be discussed, along with methods for how it can be corrected. Slice timing correction: The different slices of an fMRI volume will be acquired at different times, while the statistical modeling often assumes a single time point. This will be explained, along with ways to correct it. Physiological noise correction: It will be discussed how breathing and cardiac pulsation introduce unwanted variance to the data, and how it can be corrected. Removal of unwanted variance: Independent Component Analysis (ICA) and “Scrubbing” are methods for removal of unwanted variance that may remain even after applying the corrections above. Co-registration of functional and structural data: fMRI images often have poor resolution and tissue contrast, which makes anatomical orientation difficult. It is therefore useful to align them to a high resolution structural (e.g., T1-weighted) image.
Under pressure: UK preclinical neuroscience at a crossroads.
Graphical Abstract.
Development of a diagnostic checklist to identify functional cognitive disorder versus other neurocognitive disorders.
BACKGROUND: Functional cognitive disorder (FCD) poses a diagnostic challenge due to its resemblance to other neurocognitive disorders and limited biomarker accuracy. We aimed to develop a new diagnostic checklist to identify FCD versus other neurocognitive disorders. METHODS: The clinical checklist was developed through mixed methods: (1) a literature review, (2) a three-round Delphi study with 45 clinicians from 12 countries and (3) a pilot discriminative accuracy study in consecutive patients attending seven memory services across the UK. Items gathering consensus were incorporated into a pilot checklist. Item redundancy was evaluated with phi coefficients. A briefer checklist was produced by removing items with >10% missing data. Internal validity was tested using Cronbach's alpha. Optimal cut-off scores were determined using receiver operating characteristic curve analysis. RESULTS: A full 11-item checklist and a 7-item briefer checklist were produced. Overall, 239 patients (143 FCD, 96 non-FCD diagnoses) were included. The checklist scores were significantly different across subgroups (FCD and other neurocognitive disorders) (F(2, 236)=313.3, p<0.001). The area under the curve was excellent for both the full checklist (0.97, 95% CI 0.95 to 0.99) and its brief version (0.96, 95% CI 0.93 to 0.98). Optimal cut-off scores corresponded to a specificity of 97% and positive predictive value of 91% for identifying FCD. Both versions showed good internal validity (>0.80). CONCLUSIONS: This pilot study shows that a brief clinical checklist may serve as a quick complementary tool to differentiate patients with neurodegeneration from those with FCD. Prospective blind large-scale validation in diverse populations is warranted.Cite Now.
Organization of Community Mental Health Care in Italy
In Italy, the approval of the 180 Law in 1978, also known as Basaglia Law, marked the transition from an asylum-based to a community-based mental health care system. According to the law, patients with mental disorders are treated in the community, are integrated into society, and their rights and preferences on treatments are recognized. Following the Italian model, similar acts were approved worldwide. By 2000, all psychiatric hospitals in Italy had been closed and all patients discharged. Within the Italian National Health System, the Department of Mental Health is responsible for the provision of community-based mental health care. It includes several facilities, such as psychiatric wards located in general hospitals, residential units, mental health centres, and day-hospital and day-care facilities. The main effects of the 180 Law include the shift from a custiodialistic to a therapeutic paradigm of mental health care and the respect for autonomy and dignity of patients with mental health problems. However, after more than 45 years from its approval, several unmet needs still persist, including low staffing levels, reduced use of community facilities as long-stay services, and economic disparities between regions.
Statistical analysis plan for the Petal trial: the effects of parental touch on relieving acute procedural pain in neonates
Background Infants undergo multiple clinically-required painful procedures during their time in hospital, and there is an increasing desire from both parents and clinical staff to have parents directly involved in their newborn’s pain relief. To avoid biases due to selective analysis and reporting, a clinical trial’s statistical analysis plan (SAP) should be finalised and registered prior to dataset lock and unblinding. Here, we outline the SAP for the Petal trial, which was registered on the ISRCTN registry prior to dataset lock and unblinding. Methods The Petal trial is a multicentre, individually randomised, parallel-group interventional superiority trial. The study involves in-patient neonates born at or after 35+0 weeks gestation with a postnatal age of ≤7 days, in two hospital research sites (John Radcliffe Hospital, Oxford, UK; Royal Devon and Exeter Hospital, Exeter, UK). The primary objective is to investigate the potential efficacy of a non-pharmacological parent-led stroking intervention on reducing the magnitude of neonates’ noxious stimulus-evoked brain activity. The primary outcome is the neonate’s brain activity recorded using electroencephalography (EEG) in response to a heel lance blood sampling procedure. Secondary outcomes include neonatal clinical pain scores and tachycardia, and parental anxiety. The study hypothesis is neonates’ pain responses and parents’ anxiety scores are lower in the intervention group. Randomisation will be via a minimisation algorithm to maintain balance in five prognostic factors. Conclusions Paediatric pain trials have been highlighted by regulatory bodies as an important and challenging topic, with interest increasing in brain imaging outcomes. The Petal trial, to which this SAP relates, is part of a larger effort of establishing a brain-based EEG outcome measure of infant pain for use in clinical trials. This SAP is thus likely to be of interest to those in academia, pharmaceutical companies, and regulatory bodies. Trial registration ClinicalTrials.gov: NCT04901611, 25/05/2021; ISRCTN: ISRCTN14135962, 23/08/2021).
Autistic behavior is a common outcome of biallelic disruption of PDZD8 in humans and mice.
BACKGROUND: Intellectual developmental disorder with autism and dysmorphic facies (IDDADF) is a rare syndromic intellectual disability (ID) caused by homozygous disruption of PDZD8 (PDZ domain-containing protein 8), an integral endoplasmic reticulum (ER) protein. All four previously identified IDDADF cases exhibit autistic behavior, with autism spectrum disorder (ASD) diagnosed in three cases. To determine whether autistic behavior is a common outcome of PDZD8 disruption, we studied a third family with biallelic mutation of PDZD8 (family C) and further characterized PDZD8-deficient (Pdzd8tm1b) mice that exhibit stereotyped motor behavior relevant to ASD. METHODS: Homozygosity mapping, whole-exome sequencing, and cosegregation analysis were used to identify the PDZD8 variant responsible for IDDADF, including diagnoses of ASD, in consanguineous family C. To assess the in vivo effect of PDZD8 disruption on social responses and related phenotypes, behavioral, structural magnetic resonance imaging, and microscopy analyses were conducted on the Pdzd8tm1b mouse line. Metabolic activity was profiled using sealed metabolic cages. RESULTS: The discovery of a third family with IDDADF caused by biallelic disruption of PDZD8 permitted identification of a core clinical phenotype consisting of developmental delay, ID, autism, and facial dysmorphism. In addition to impairments in social recognition and social odor discrimination, Pdzd8tm1b mice exhibit increases in locomotor activity (dark phase only) and metabolic rate (both lights-on and dark phases), and decreased plasma triglyceride in males. In the brain, Pdzd8tm1b mice exhibit increased levels of accessory olfactory bulb volume, primary olfactory cortex volume, dendritic spine density, and ER stress- and mitochondrial fusion-related transcripts, as well as decreased levels of cerebellar nuclei volume and adult neurogenesis. LIMITATIONS: The total number of known cases of PDZD8-related IDDADF remains low. Some mouse experiments in the study did not use balanced numbers of males and females. The assessment of ER stress and mitochondrial fusion markers did not extend beyond mRNA levels. CONCLUSIONS: Our finding that the Pdzd8tm1b mouse model and all six known cases of IDDADF exhibit autistic behavior, with ASD diagnosed in five cases, identifies this trait as a common outcome of biallelic disruption of PDZD8 in humans and mice. Other abnormalities exhibited by Pdzd8tm1b mice suggest that the range of comorbidities associated with PDZD8 deficiency may be wider than presently recognized.
Statistical analysis plan for the Petal trial: the effects of parental touch on relieving acute procedural pain in neonates.
BACKGROUND: Infants undergo multiple clinically-required painful procedures during their time in hospital, and there is an increasing desire from both parents and clinical staff to have parents directly involved in their newborn's pain relief. To avoid biases due to selective analysis and reporting, a clinical trial's statistical analysis plan (SAP) should be finalised and registered prior to dataset lock and unblinding. Here, we outline the SAP for the Petal trial, which was registered on the ISRCTN registry prior to dataset lock and unblinding. METHODS: The Petal trial is a multicentre, individually randomised, parallel-group interventional superiority trial. The study involves in-patient neonates born at or after 35+0 weeks gestation with a postnatal age of ≤7 days, in two hospital research sites (John Radcliffe Hospital, Oxford, UK; Royal Devon and Exeter Hospital, Exeter, UK). The primary objective is to investigate the potential efficacy of a non-pharmacological parent-led stroking intervention on reducing the magnitude of neonates' noxious stimulus-evoked brain activity. The primary outcome is the neonate's brain activity recorded using electroencephalography (EEG) in response to a heel lance blood sampling procedure. Secondary outcomes include neonatal clinical pain scores and tachycardia, and parental anxiety. The study hypothesis is neonates' pain responses and parents' anxiety scores are lower in the intervention group. Randomisation will be via a minimisation algorithm to maintain balance in five prognostic factors. CONCLUSIONS: Paediatric pain trials have been highlighted by regulatory bodies as an important and challenging topic, with interest increasing in brain imaging outcomes. The Petal trial, to which this SAP relates, is part of a larger effort of establishing a brain-based EEG outcome measure of infant pain for use in clinical trials. This SAP is thus likely to be of interest to those in academia, pharmaceutical companies, and regulatory bodies. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04901611, 25/05/2021; ISRCTN: ISRCTN14135962, 23/08/2021).
Post-stroke changes in brain structure and function can both influence acute upper limb function and subsequent recovery.
Improving outcomes after stroke depends on understanding both the causes of initial function/impairment and the mechanisms of recovery. Recovery in patients with initially low function/high impairment is variable, suggesting the factors relating to initial function/impairment are different to the factors important for subsequent recovery. Here we aimed to determine the contribution of altered brain structure and function to initial severity and subsequent recovery of the upper limb post-stroke. The Nine-Hole Peg Test was recorded in week 1 and one-month post-stroke and used to divide 36 stroke patients (18 females, age: M = 66.56 years) into those with high/low initial function and high/low subsequent recovery. We determined differences in week 1 brain structure (Magnetic Resonance Imaging) and function (Magnetoencephalography, tactile stimulation) between high/low patients for both initial function and subsequent recovery. Lastly, we examined the relative contribution of changes in brain structure and function to recovery in patients with low levels of initial function. Low initial function and low subsequent recovery are related to lower sensorimotor β power and greater lesion-induced disconnection of contralateral [ipsilesional] white-matter motor projection connections. Moreover, differences in intra-hemispheric connectivity (structural and functional) are unique to initial motor function, while differences in inter-hemispheric connectivity (structural and functional) are unique to subsequent motor recovery. Function-related and recovery-related differences in brain function and structure after stroke are related, yet not identical. Separating out the factors that contribute to each process is key to identifying potential therapeutic targets for improving outcomes.
Assessing the impact of COmorbidities and Sociodemographic factors on Multiorgan Injury following COVID-19: rationale and protocol design of COSMIC, a UK multicentre observational study of COVID-negative controls.
INTRODUCTION: SARS-CoV-2 disease (COVID-19) has had an enormous health and economic impact globally. Although primarily a respiratory illness, multi-organ involvement is common in COVID-19, with evidence of vascular-mediated damage in the heart, liver, kidneys and brain in a substantial proportion of patients following moderate-to-severe infection. The pathophysiology and long-term clinical implications of multi-organ injury remain to be fully elucidated. Age, gender, ethnicity, frailty and deprivation are key determinants of infection severity, and both morbidity and mortality appear higher in patients with underlying comorbidities such as ischaemic heart disease, hypertension and diabetes. Our aim is to gain mechanistic insights into the pathophysiology of multiorgan dysfunction in people with COVID-19 and maximise the impact of national COVID-19 studies with a comparison group of COVID-negative controls. METHODS AND ANALYSIS: COmorbidities and Sociodemographic factors on Multiorgan Injury following COVID-19 (COSMIC) is a prospective, multicentre UK study which will recruit 200 subjects without clinical evidence of prior COVID-19 and perform extensive phenotyping with multiorgan imaging, biobank serum storage, functional assessment and patient reported outcome measures, providing a robust control population to facilitate current work and serve as an invaluable bioresource for future observational studies. ETHICS AND DISSEMINATION: Approved by the National Research Ethics Service Committee East Midlands (REC reference 19/EM/0295). Results will be disseminated via peer-reviewed journals and scientific meetings. TRIAL REGISTRATION NUMBER: COSMIC is registered as an extension of C-MORE (Capturing Multi-ORgan Effects of COVID-19) on ClinicalTrials.gov (NCT04510025).
Acute neural effects of fluoxetine on emotional regulation in depressed adolescents.
BACKGROUND: Adolescent major depressive disorder (MDD) is associated with disrupted processing of emotional stimuli and difficulties in cognitive reappraisal. Little is known however about how current pharmacotherapies act to modulate the neural mechanisms underlying these key processes. The current study therefore investigated the neural effects of fluoxetine on emotional reactivity and cognitive reappraisal in adolescent depression. METHODS: Thirty-one adolescents with MDD were randomised to acute fluoxetine (10 mg) or placebo. Seventeen healthy adolescents were also recruited but did not receive any treatment for ethical reasons. During functional magnetic resonance imaging (fMRI), participants viewed aversive images and were asked to either experience naturally the emotional state elicited ('Maintain') or to reinterpret the content of the pictures to reduce negative affect ('Reappraise'). Significant activations were identified using whole-brain analysis. RESULTS: No significant group differences were seen when comparing Reappraise and Maintain conditions. However, when compared to healthy controls, depressed adolescents on placebo showed reduced visual activation to aversive pictures irrespective of the condition. The depressed adolescent group on fluoxetine showed the opposite pattern, i.e. increased visuo-cerebellar activity in response to aversive pictures, when compared to depressed adolescents on placebo. CONCLUSIONS: These data suggest that depression in adolescence may be associated with reduced visual processing of aversive imagery and that fluoxetine may act to reduce avoidance of such cues. This could reflect a key mechanism whereby depressed adolescents engage with negative cues previously avoided. Future research combining fMRI with eye-tracking is nonetheless needed to further clarify these effects.
A systematic review of in vivo brain insulin resistance biomarkers in humans
Type 2 diabetes mellitus (T2DM) is associated with an elevated risk of dementia, prompting interest into the concept of brain-specific insulin resistance. However, the brain's reliance on insulin-independent glucose transporters complicates attempts to measure in vivo brain insulin resistance using the definition of system-wide insulin resistance, which is based on glucose-insulin interactions. In this review, we explore three available biomarkers for evaluating in vivo brain-specific insulin resistance in humans: (1) correlating systemic insulin resistance with brain function, (2) examining functional brain changes after the administration of intranasal insulin, and (3) quantifying insulin signalling proteins in neuronally enriched blood-derived extracellular vesicles. Integrating evidence from these three approaches tentatively suggests for the first time that a comprehensive assessment of the brain's default mode network (DMN), combining these methodologies within a single study, may offer a useful biomarker to quantify in vivo brain-specific insulin resistance in humans. Correlating DMN responses to concentrations of pY-IRS-1 in blood-derived extracellular vesicles would corroborate evidence for a brain-specific biomarker and provide a scalable approach to detecting brain-specific insulin resistance in humans. This advancement would enable in vivo evaluations of insulin resistance in the central nervous system, akin to the precise measurements of systemic insulin resistance seen in T2DM. An established and clearly defined biomarker of in vivo brain insulin resistance in humans would permit further investigation into the links between diabetes and dementia, ultimately bolstering support for secondary dementia prevention by identifying those at higher risk for cognitive decline.
Emotional Processing Following Digital Cognitive Behavioral Therapy for Insomnia in People With Depressive Symptoms: A Randomized Clinical Trial.
IMPORTANCE: Cognitive behavioral therapy for insomnia (CBT-I) has been shown to reduce depressive symptoms, but the underlying mechanisms are not well understood and warrant further examination. OBJECTIVE: To investigate whether CBT-I modifies negative bias in the perception of emotional facial expressions and whether such changes mediate improvement in depressive symptoms. DESIGN, SETTING, AND PARTICIPANTS: A randomized clinical trial of digital CBT-I vs sleep hygiene education was conducted. Adults living in the UK who met diagnostic criteria for insomnia disorder and Patient Health Questionnaire-9 criteria (score ≥10) for depression were recruited online from the community and randomly assigned to either a 6-session digital CBT-I program or a sleep hygiene webpage. Participant recruitment took place between April 26, 2021, and January 24, 2022, and outcomes were assessed at 5 and 10 weeks post randomization. Data analysis was performed from December 1, 2022, to March 1, 2023. MAIN OUTCOMES AND MEASURES: Coprimary outcomes were recognition accuracy (percentage) of happy and sad facial expressions at 10 weeks assessed with the facial expression recognition task. Secondary outcomes were self-reported measures of insomnia, depressive symptoms, affect, emotional regulation difficulties, worry, perseverative thinking, midpoint of sleep, social jet lag, and the categorization of and recognition memory for emotional words. Intention-to-treat analysis was used. RESULTS: A total of 205 participants were randomly assigned to CBT-I (n = 101) or sleep hygiene education (n = 104). The sample had a mean (SD) age of 49.3 (10.1) years and was predominately female (165 [80.8%]). Retention was 85.7% (n = 175). At 10 weeks, the estimated adjusted mean difference for recognition accuracy was 3.01 (97.5% CI, -1.67 to 7.69; P = .15; Cohen d = 0.24) for happy facial expressions and -0.54 (97.5% CI, -3.92 to 2.84; P = .72; Cohen d = -0.05) for sad facial expressions. At 10 weeks, CBT-I compared with control decreased insomnia severity (adjusted difference, -4.27; 95% CI, -5.67 to -2.87), depressive symptoms (adjusted difference, -3.91; 95% CI, -5.20 to -2.62), negative affect (adjusted difference, -2.75; 95% CI, -4.58 to -0.92), emotional regulation difficulties (adjusted difference, -5.96; 95% CI, -10.61 to -1.31), worry (adjusted difference, -8.07; 95% CI, -11.81 to -4.33), and perseverative thinking (adjusted difference, -4.21; 95% CI, -7.03 to -1.39) and increased positive affect (adjusted difference, 4.99; 95% CI, 3.13-6.85). Improvement in negative affect, emotional regulation difficulties, and worry at week 5 mediated the effect of CBT-I on depression severity at 10 weeks (% mediated: 21.9% Emotion regulation difficulties; 24.4% Worry; and 29.7% Negative affect). No serious adverse events were reported to the trial team. CONCLUSIONS AND RELEVANCE: This randomized clinical trial did not find evidence that CBT-I engenders change in the perception of facial expressions at post treatment, despite improvements in insomnia and depressive symptoms. Early change in negative affect, emotional regulation difficulties, and worry mediated lagged depression outcomes and deserve further empirical scrutiny. TRIAL REGISTRATION: isrctn.org Identifier: ISRCTN17117237.
Prediction models for treatment response in migraine: a systematic review and meta-analysis
Background: Migraine is a complex neurological disorder with significant clinical variability, posing challenges for effective management. Multiple treatments are available for migraine, but individual responses vary widely, making accurate prediction crucial for personalized care. This study aims to examine the use of statistical and machine learning models to predict treatment response in migraine patients. Methods: A systematic review and meta-analysis were conducted to assess the performance and quality of predictive models for migraine treatment response. Relevant studies were identified from databases such as PubMed, Cochrane Register of Controlled Trials, Embase, and Web of Science, up to 30th of November 2024. The risk of bias was evaluated using the PROBAST tool, and adherence to reporting standards was assessed with the TRIPOD + AI checklist. Results: After screening 1,927 documents, ten studies met the inclusion criteria, and six were included in a quantitative synthesis. Key data extracted included sample characteristics, intervention types, response outcomes, modeling methods, and predictive performance metrics. A pooled analysis of the area under the curve (AUC) yielded a value of 0.86 (95% CI: 0.67–0.95), indicating good predictive performance. However, the included studies generally had a high risk of bias, particularly in the analysis domain, as assessed by the PROBAST tool. Conclusion: This review highlights the potential of statistical and machine learning models in predicting treatment response in migraine patients. However, the high risk of bias and significant heterogeneity emphasize the need for caution in interpretation. Future research should focus on developing models using high-quality, comprehensive, and multicenter datasets, rigorous external validation, and adherence to standardized guidelines like TRIPOD + AI. Incorporating multimodal magnetic resonance imaging (MRI) data, exploring migraine symptom-treatment interactions, and establishing uniform methodologies for outcome measures, sample size calculations, and missing data handling will enhance model reliability and clinical applicability, ultimately improving patient outcomes and reducing healthcare burdens. Trial registration: PROSPERO, CRD42024621366.