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Statistical inference for same data meta-analysis in neuroimaging multiverse analyzes
Researchers using task-functional magnetic resonance imaging (fMRI) data have access to a wide range of analysis tools to model brain activity. If not accounted for properly, this plethora of analytical approaches can lead to an inflated rate of false positives and contribute to the irreproducibility of neuroimaging findings. Multiverse analyses are a way to systematically explore pipeline variations on a given dataset. We focus on the setting where multiple statistic maps are produced as an output of a set of analyses originating from a single dataset. However, having multiple outputs for the same research question—corresponding to different analytical approaches—makes it especially challenging to draw conclusions and interpret the findings. Meta-analysis is a natural approach to extract consensus inferences from these maps, yet the traditional assumption of independence among input datasets does not hold here. In this work, we consider a suite of methods to conduct meta-analysis in the multiverse setting, which we call same data meta-analysis (SDMA), accounting for inter-pipeline dependence among the results. First, we assessed the validity of these methods in simulations. Then, we tested them on the multiverse outputs of two real-world multiverse analyses: “NARPS”, a multiverse study originating from the same dataset analyzed by 70 different teams, and “HCP Young Adult”, a more homogeneous multiverse analysis using 24 different pipelines analyzed by the same team. Our findings demonstrate the validity of our proposed SDMA models under inter-pipeline dependence, and provide an array of options, with different levels of relevance, for the analysis of multiverse outputs.
Statistical Analysis of fMRI Data
fMRI is a powerful tool used in the study of brain function. It can noninvasively detect signal changes in areas of the brain where neuronal activity is varying. This chapter is a comprehensive description of the various steps in the statistical analysis of fMRI data. This will cover topics such as the general linear model (including orthogonality, hemodynamic variability, noise modeling, and the use of contrasts), multi-subject statistics, and statistical thresholding (including random field theory and permutation methods, as well as a discussion of some recent controversies about correction for multiple comparisons of statistical models).
Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults
The functional connectome changes with aging. We systematically evaluated aging-related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state functional magnetic resonance imaging (fMRI) data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both with permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p < 0.001, 95% CI [7.6% 36.0%]) and 11.5% (p < 0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p < 0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
Cohort profile: characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19 - providing the evidence base for health care services (CONVALESCENCE) in the UK.
PURPOSE: The pathogenesis of the long-lasting symptoms which can follow an infection with the SARS-CoV-2 virus ('long covid') is not fully understood. The 'COroNaVirus post-Acute Long-term EffectS: Constructing an evidENCE base' (CONVALESCENCE) study was established as part of the Longitudinal Health and Wellbeing COVID-19 UK National Core Study. We performed a deep phenotyping case-control study nested within two cohorts (the Avon Longitudinal Study of Parents and Children and TwinsUK) as part of CONVALESCENCE. PARTICIPANTS: From September 2021 to May 2023, 349 participants attended the CONVALESCENCE deep phenotyping clinic at University College London. Four categories of participants were recruited: cases of long covid (long covid(+)/SARS-CoV-2(+)), alongside three control groups: those with neither long covid symptoms nor evidence of prior COVID-19 (long covid(-)/SARS-CoV-2(-); control group 1), those who self-reported COVID-19 and had evidence of SARS-CoV-2 infection, but did not report long covid (long covid(-)/SARS-CoV-2(+); control group 2) and those who self-reported persistent symptoms attributable to COVID-19 but no evidence of SARS-CoV-2 infection (long covid(+)/SARS-CoV-2(-); control group 3). Remote wearable measurements were performed up until February 2024. FINDINGS TO DATE: This cohort profile describes the baseline characteristics of the CONVALESCENCE cohort. Of the 349 participants, 141 (53±15 years old; 21 (15%) men) were cases, 89 (55±16 years old; 11 (12%) men) were in control group 1, 75 (49±15 years old; 25 (33%) men) were in control group 2 and 44 (55±16 years old; 9 (21%) men) were in control group 3. FUTURE PLANS: The study aims to use a multiorgan score calculated as the cumulative total for each of nine domains (ie, lung, vascular, heart, kidney, brain, autonomic function, muscle strength, exercise capacity and physical performance). The availability of data preceding acute COVID-19 infection in cohorts may help identify the consequences of infection independent of pre-existing subclinical disease and also provide evidence of determinants that influence the development of long covid.
The developing Human Connectome Project fetal functional MRI release: Methods and data structures
Recent advances in fetal fMRI present a new opportunity for neuroscience to study functional human brain connectivity at the time of its emergence. Progress in the field, however, has been hampered by the lack of openly available datasets that can be exploited by researchers across disciplines to develop methods that would address the unique challenges associated with imaging and analysing functional brain in utero, such as unconstrained head motion, dynamically evolving geometric distortions, or inherently low signal-to-noise ratio. Here we describe the developing Human Connectome Project’s release of the largest open access fetal fMRI dataset to date, containing 275 scans from 255 foetuses and spanning the period of 20.86 to 38.29 post-menstrual weeks. We present a systematic approach to its pre-processing, implementing multi-band soft SENSE reconstruction, dynamic distortion corrections via phase unwrapping method, slice-to-volume reconstruction and a tailored temporal filtering model, with attention to the prominent sources of structured noise in the in utero fMRI. The dataset is accompanied with an advanced registration infrastructure, enabling group-level data fusion, and contains outputs from the main intermediate processing steps. This allows for various levels of data exploration by the imaging and neuroscientific community, starting from the development of robust pipelines for anatomical and temporal corrections to methods for elucidating the development of functional connectivity in utero. By providing a high-quality template for further method development and benchmarking, the release of the dataset will help to advance fetal fMRI to its deserved and timely place at the forefront of the efforts to build a life-long connectome of the human brain.
Individualised prediction of longitudinal change in multimodal brain imaging.
It remains largely unknown whether individualised longitudinal changes of brain imaging features can be predicted based only on the baseline brain images. This would be of great value, for example, for longitudinal data imputation, longitudinal brain-behaviour associations, and early prediction of brain-related diseases. We explore this possibility using longitudinal data of multiple modalities from UK Biobank brain imaging, with around 3,500 subjects. As baseline and follow-up images are generally similar in the case of short follow-up time intervals (e.g., 2 years), a simple copy of the baseline image may have a very good prediction performance. Therefore, for the first time, we propose a new mathematical framework for guiding the longitudinal prediction of brain images, providing answers to fundamental questions: (1) what is a suitable definition of longitudinal change; (2) how to detect the existence of changes; (3) what is the "null" prediction performance; and (4) can we distinguish longitudinal change prediction from simple data denoising. Building on these, we designed a deep U-Net based model for predicting longitudinal changes in multimodal brain images. Our results show that the proposed model can predict to a modest degree individualised longitudinal changes in almost all modalities, and outperforms other potential models. Furthermore, compared with the true longitudinal changes computed from real data, the predicted longitudinal changes have a similar or even improved accuracy in predicting subjects' non-imaging phenotypes, and have a high between-subject discriminability. Our study contributes a new theoretical framework for longitudinal brain imaging studies, and our results show the potential for longitudinal data imputation, along with highlighting several caveats when performing longitudinal data analysis.
Differential Associations of Dopamine and Serotonin With Reward and Punishment Processes in Humans: A Systematic Review and Meta-Analysis.
IMPORTANCE: Mechanistic biomarkers for guiding treatment selection require selective sensitivity to specific pharmacological interventions. Reinforcement learning processes show potential, but there have been conflicting and sometimes inconsistent reports on how dopamine and serotonin-2 key targets in treating common mental illnesses-affect reinforcement learning in humans. OBJECTIVE: To perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct associations with reinforcement learning components in humans. DATA SOURCES: Ovid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946, and January 19, 2023 (repeated April 9, 2024, and October 15, 2024), investigating dopaminergic or serotonergic effects on reward and punishment processes in humans according to PRISMA guidelines. STUDY SELECTION: Studies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward or punishment processing task in healthy humans were included. DATA EXTRACTION AND SYNTHESIS: Standardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine or serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward or punishment processes and 4 main subcategories. Study quality (Cochrane Collaboration tool), moderators, heterogeneity, and publication bias were also assessed. MAIN OUTCOMES AND MEASURES: Performance on reward or punishment processing tasks. RESULTS: In total, 102 studies conducted among healthy volunteers were included (2291 participants receiving dopamine vs 2284 receiving placebo and 1491 receiving serotonin vs 1523 receiving placebo). Dopamine was associated with an increase in overall reward (SMD, 0.18; 95% CI, 0.09 to 0.28) but not punishment function (SMD, -0.06; 95% CI, -0.26 to 0.13). Serotonin was not meaningfully associated with overall punishment (SMD, 0.22; 95% CI, -0.04 to 0.49) or reward (SMD, 0.02; 95% CI, -0.33 to 0.36). Dopaminergic and serotonergic manipulations had distinct associations with subcomponents. Dopamine was associated with reward learning or sensitivity (SMD, 0.26; 95% CI, 0.11 to 0.40), reward discounting (SMD, -0.08; 95% CI, -0.14 to -0.01), and reward vigor (SMD, 0.32; 95% CI, 0.11 to 0.54). By contrast, serotonin was associated with punishment learning or sensitivity (SMD, 0.32; 95% CI, 0.05 to 0.59), reward discounting (SMD, -0.35; 95% CI, -0.67 to -0.02), and aversive pavlovian processes (within-participant studies only; SMD, 0.36; 95% CI, 0.20 to 0.53). CONCLUSIONS AND RELEVANCE: In this study, pharmacological manipulations of both dopamine and serotonin had measurable associations with reinforcement learning in humans. The selective associations with different components suggest that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment.
Gamma activation spread reflects disease activity in amyotrophic lateral sclerosis.
OBJECTIVE: A non-invasive measure of cerebral motor system dysfunction would be valuable as a biomarker in amyotrophic lateral sclerosis (ALS). Task-based magnetoencephalography (tMEG) measures the magnetic fields generated by cortical neuronal oscillatory activity during task performance. Gamma activations are periods of high-power and high-frequency cortical oscillations integral to motor control. METHODS: tMEG was undertaken during 60 bilateral isometric hand grip exercises in ALS (n = 42) and compared with healthy controls (HC, n = 33). Gamma activation spread (GAS) was estimated by calculating the number of activated regions during each 100 ms time-bin and compared statistically between groups. Gamma activation patterns were visualised by plotting each participant's brain activity separately as a 2-dimensional video. RESULTS: There was no difference in grip strength between groups. GAS was greatly increased in the ALS group compared to HC (p
Pramipexole augmentation for the acute phase of treatment-resistant, unipolar depression: a placebo-controlled, double-blind, randomised trial in the UK.
BACKGROUND: About 30% of patients with depression treated with antidepressant medication do not respond sufficiently to the first agents used. Pramipexole might usefully augment antidepressant medication in such cases of treatment-resistant depression, but data on its effects and tolerability are scarce. We aimed to assess the efficacy and tolerability of pramipexole augmentation of ongoing antidepressant treatment, over 48 weeks, in patients with treatment-resistant depression. METHODS: We did a multicentre, double-blind, placebo-controlled randomised trial in which adults with resistant major depressive disorder were randomly assigned (1:1; using an online randomisation system) to 48 weeks of pramipexole (titrated to 2·5 mg) or placebo added to their ongoing antidepressant medication. The study was conducted in nine National Health Service Trusts in England. Participants, investigators, and researchers involved in recruitment and assessment were masked to group allocation, and the central pharmacy team dispensing the medication was not masked. The primary outcome was change from baseline to week 12 in the total score of the 16-item Quick Inventory of Depressive Symptomology self-report version (QIDS-SR16). The primary analysis was performed on the intention-to-treat population that included all eligible, randomly assigned participants. People with lived experience were involved in the design, oversight, and interpretation of the study. The trial was registered with ISCTRN (ISRCTN84666271) and EudraCT (2019-001023-13) and is complete. FINDINGS: Between Feb 16 and May 29, 2024, 217 participants attended a screening visit, of whom 66 were excluded due to ineligibility. 151 participants were randomly assigned (75 to the pramipexole group and 75 to the placebo group, after one participant was found to be ineligible after randomisation). 84 (56%) participants were female and 66 (44%) were male and the mean age of participants was 44·9 years (SD 14·0). Ethnicity data were not available. The mean QIDS-SR16 total score at baseline was 16·4 (SD 3·4) in the pramipexole group and 16·2 (3·5) in the placebo group. The mean dose of pramipexole received at week 12 was 2·3 mg (SD 0·45). Adjusted mean decrease from baseline to week 12 of the QIDS-SR16 total score was 6·4 (SD 4·9) for the pramipexole group and 2·4 (4·0) for the placebo group; the mean difference between groups was -3·91 (95% CI -5·37 to -2·45; p<0·0001). Termination of trial treatment due to adverse events was more frequent in the pramipexole group (15 participants [20%]) than in the placebo group (four participants [5%]), with reported adverse events consistent with known side-effects of pramipexole, in particular nausea, headache, and sleep disturbance or somnolence. INTERPRETATION: In this trial involving participants with treatment-resistant depression, pramipexole augmentation of antidepressant treatment, at a target dose of 2·5 mg, demonstrated a reduction in symptoms relative to placebo at 12 weeks but was associated with some adverse effects. These results suggest that pramipexole is a clinically effective option for reducing symptoms in patients with treatment-resistant depression. Future trials directly comparing pramipexole with existing treatments for this disorder are needed. FUNDING: National Institute of Health and Care Research, Efficacy and Mechanism Evaluation Programme.
Memory reactivation during rest forms shortcuts in a cognitive map.
Efficient and flexible cognition relies upon cognitive maps-representations of concepts and the relations between them. Cognitive maps integrate relations that were learned separately into a cohesive whole. Memory reactivation during rest and sleep may contribute to cognitive map formation in two ways: by simply strengthening memories for directly experienced relations, or by reorganising concepts and creating new relations that capture the underlying structure. We designed a multi-stage learning task to test whether reactivation during rest is involved in restructuring memories as opposed to simply consolidating what was experienced. We causally manipulated memory reactivation during rest using awake, contextual targeted memory reactivation. We found that promoting memory reactivation during rest qualitatively reorganises the cognitive map by forming 'shortcuts' between events which have not been experienced together. These shortcuts in memory extend beyond direct experience to facilitate our ability to make novel inferences. Using a series of control tests we show that inference performance cannot be explained by quantitative strengthening of the experienced component links. Interestingly, we show that representing a shortcut may come with limitations, as shortcuts cannot be readily updated in response to rapid changes in the environment. Together, these findings reveal how memories are reorganised during awake rest to construct a cognitive map of our environment, while highlighting the constraints set by a trade-off between efficient and flexible behaviour.
Achieving robust labeling above the circle of Willis with vessel-encoded arterial spin labeling.
PURPOSE: To improve the robustness of noninvasive vessel-selective perfusion imaging and angiography using vessel-encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain. METHODS: Our proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR-efficient encodings than previous approaches. Pseudo-continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients. RESULTS: In simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients. CONCLUSIONS: Combining IOES with optimized PCASL parameters, the vessel-decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation.
SNR-efficient whole-brain pseudo-continuous arterial spin labeling perfusion imaging at 7 T.
PURPOSE: To optimize pseudo-continuous arterial spin labeling (PCASL) parameters to maximize SNR efficiency for RF power constrained whole brain perfusion imaging at 7 T. METHODS: We used Bloch simulations of pulsatile laminar flow to optimize the PCASL parameters for maximum SNR efficiency, balancing labeling efficiency and total RF power. The optimization included adjusting the inter-RF pulse spacing (TRPCASL), mean B1 + (B1 + ave), slice-selective gradient amplitude (Gmax), and mean gradient amplitude (Gave). In vivo data were acquired from six volunteers at 7 T to validate the optimized parameters. Dynamic B0-shimming and flip angle adjustments were used to avoid needing to make the PCASL parameters robust to B0/B1 + variations. RESULTS: The optimized PCASL parameters achieved a significant (3.3×) reduction in RF power while maintaining high labeling efficiency. This allowed for longer label durations and minimized deadtime, resulting in a 118% improvement in SNR efficiency in vivo compared to a previously proposed protocol. Additionally, the static tissue response was improved, reducing the required distance between labeling plane and imaging volume. CONCLUSION: These optimized PCASL parameters provide a robust and efficient approach for whole brain perfusion imaging at 7 T, with significant improvements in SNR efficiency and reduced specific absorption rate burden.
Apathy in rapid eye movement sleep behaviour disorder is associated with serotonin depletion in the dorsal raphe nucleus.
Apathy is a common and under-recognized disorder that often emerges in the prodromal phase of Parkinsonian diseases. The mechanism by which this occurs is not known, but recent evidence from patients with established Parkinson's disease suggests that serotonergic dysfunction may play a role. The integrity of the raphe serotonergic system can be assessed alongside dopaminergic basal ganglia imaging using the radioligand 123I-ioflupane, which binds both serotonin and dopamine transporters. To investigate the relative roles of these neurotransmitters in prodromal parkinsonism, we imaged patients with idiopathic rapid eye movement sleep behaviour disorder, the majority of whom will develop a parkinsonian disorder in future. Forty-three patients underwent brain imaging with 123I-ioflupane single photon emission computed tomography and structural MRI. Apathy was quantified using the Lille Apathy Rating Scale. Other clinical parkinsonian features were assessed using standard measures. A negative correlation was observed between apathy severity and serotonergic 123I-ioflupane signal in the dorsal raphe nucleus (r = -0.55, P < 0.001). There was no significant correlation between apathy severity and basal ganglia dopaminergic signal, nor between dorsal raphe signal and other neuropsychiatric scores. This specific association between apathy and raphe 123I-ioflupane signal suggests that the serotonergic system might represent a target for the treatment of apathy.
Reward insensitivity is associated with dopaminergic deficit in rapid eye movement sleep behaviour disorder.
Idiopathic rapid eye movement sleep behaviour disorder (iRBD) has now been established as an important marker of the prodromal stage of Parkinson's disease and related synucleinopathies. However, although dopamine transporter single photon emission computed tomography (SPECT) has been used to demonstrate the presence of nigro-striatal deficit in iRBD, quantifiable correlates of this are currently lacking. Sensitivity to rewarding stimuli is reduced in some people with Parkinson's disease, potentially contributing to aspects of the neuropsychiatric phenotype in these individuals. Furthermore, a role for dopaminergic degeneration is suggested by the fact that reward insensitivity can be improved by dopaminergic medications. Patients with iRBD present a unique opportunity to study the relationship between reward sensitivity and early dopaminergic deficit in the unmedicated state. Here, we investigate whether a non-invasive, objective measure of reward sensitivity might be a marker of dopaminergic status in prodromal Parkinson's disease by comparing with SPECT/CT measurement of dopaminergic loss in the basal ganglia. Striatal dopaminergic deficits in iRBD are associated with progression to Parkinsonian disorders. Therefore, identification of a clinically measurable correlate of this degenerative process might provide a basis for the development of novel risk stratification tools. Using a recently developed incentivized eye-tracking task, we quantified reward sensitivity in a cohort of 41 patients with iRBD and compared this with data from 40 patients with Parkinson's disease and 41 healthy controls. Patients with iRBD also underwent neuroimaging with dopamine transporter SPECT/CT. Overall, reward sensitivity, indexed by pupillary response to monetary incentives, was reduced in iRBD cases compared with controls and was not significantly different to that in patients with Parkinson's disease. However, in iRBD patients with normal dopamine transporter SPECT/CT imaging, reward sensitivity was not significantly different from healthy controls. Across all iRBD cases, a positive association was observed between reward sensitivity and dopaminergic SPECT/CT signal in the putamen. These findings demonstrate a direct relationship between dopaminergic deficit and reward sensitivity in patients with iRBD and suggest that measurement of pupillary responses could be of value in models of risk stratification and disease progression in these individuals.