Is musical engagement enough to keep the brain young?

Music-making and engagement in music-related activities have shown procognitive benefits for healthy and pathological populations, suggesting reductions in brain aging. A previous brain aging study, using Brain Age Gap Estimation (BrainAGE), showed that professional and amateur-musicians had younger appearing brains than non-musicians. Our study sought to replicate those findings and analyze if musical training or active musical engagement was necessary to produce an age-decelerating effect in a cohort of healthy individuals. We scanned 125 healthy controls and investigated if musician status, and if musical behaviors, namely active engagement (AE) and musical training (MT) [as measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)], had effects on brain aging. Our findings suggest that musician status is not related to BrainAGE score, although involvement in current physical activity is. Although neither MT nor AE subscales of the Gold-MSI are predictive for BrainAGE scores, dispositional resilience, namely the ability to deal with challenge, is related to both musical behaviors and sensitivity to musical pleasure. While the study failed to replicate the findings in a previous brain aging study, musical training and active musical engagement are related to the resilience factor of challenge. This finding may reveal how such musical behaviors can potentially strengthen the brain’s resilience to age, which may tap into a type of neurocognitive reserve.publishedVersio

Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being

The menstrual cycle (MC) is a sex hormone-related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large-scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early-follicular, peri-ovulatory, and mid-luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self-esteem, and well-being. We showed that during the peri-ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well-being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large-scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies

Computational modelling of imaging markers to support the diagnosis and monitoring of multiple sclerosis

Multiple sclerosis is a leading cause of neurological disability in young adults which affects more than 2.5 million people worldwide. An important substrate of disability accrual is the loss of neurons and connections between them (neurodegeneration) which can be captured by serial brain imaging, especially in the cerebral grey matter. In this thesis in four separate subprojects, I aimed to assess the strength of imaging-derived grey matter volume as a biomarker in the diagnosis, predicting the evolution of multiple sclerosis, and developing a staging system to stratify patients. In total, I retrospectively studied 1701 subjects, of whom 1548 had longitudinal brain imaging data. I used advanced computational models to investigate cross-sectional and longitudinal datasets. In the cross-sectional study, I demonstrated that grey matter volumes could distinguish multiple sclerosis from another demyelinating disorder (neuromyelitis optica) with an accuracy of 74%. In longitudinal studies, I showed that over time the deep grey matter nuclei had the fastest rate of volume loss (up to 1.66% annual loss) across the brain regions in multiple sclerosis. The volume of the deep grey matter was the strongest predictor of disability progression. I found that multiple sclerosis affects different brain areas with a specific temporal order (or sequence) that starts with the deep grey matter nuclei, posterior cingulate cortex, precuneus, and cerebellum. Finally, with multivariate mechanistic and causal modelling, I showed that brain volume loss causes disability and cognitive worsening which can be delayed with a potential neuroprotective treatment (simvastatin). This work provides conclusive evidence that grey matter volume loss affects some brain regions more severely, can predict future disability progression, can be used as an outcome measure in phase II clinical trials, and causes clinical and cognitive worsening. This thesis also provides a subject staging system based on which patients can be scored during multiple sclerosis

Exploring Outcome Measures of disease progression in Secondary Progressive Multiple Sclerosis

Multiple sclerosis (MS) is a disabling and progressive neurological disease affecting more than 120,000 people in the UK and 2.5 million people worldwide. Most people diagnosed with relapsing-remitting MS (RRMS) experience a late phase of the disease characterised by gradual progression of impairment of neurological function, known as secondary progressive MS (SPMS). The underlying cause of disability accrual in SPMS is attributed to progressive neuro-axonal loss (neurodegeneration). Unlike RRMS, treatments for SPMS are lacking and research from clinical trials has provided only modest positive results. This thesis concerns my work on a multi-centre, multi-arm, placebo-controlled phase 2B clinical trial (MS-SMART, ClinicalTrials.gov NCT01910259) investigating neuroprotection in SPMS. Through this trial, I investigated: (1) cross-sectional relationships between clinical and radiological outcomes that characterise a well-defined UK SPMS population not on disease-modifying therapy; (2) whether amiloride, fluoxetine and riluzole – the three pre-identified putative neuroprotective drugs used in the MS-SMART trial – reduced the rate of MRI-derived brain atrophy compared to placebo; (3) whether spinal cord MRI measures - reflecting cord damage and atrophy - were predictors of long-term disease course in people with SPMS and could be reliably used as outcome measures in future phase 2 trials; and (4) whether optical coherence tomography (OCT) measures were predictors of long-term disease course in people with SPMS and had a role in the measurement of neuroaxonal loss in future phase 2 trials. I collected several measures of disability, which included the expanded disability status scale (EDSS), the symbol digit modalities test (SDMT), the multiple sclerosis functional composite (MSFC) and its sub-components (9-holep peg test, timed 25-foot walk, paced auditory serial addition test), and multiple sclerosis impact scale (MSIS-29v2). To characterise the trial cohort at baseline and identify the variables that could better explain the sample variance, I used some sophisticated statistical analyses (principal component analysis, LASSO regression analysis) which helped to deal with the multiplicity of the variables. I also looked at some of the MS symptoms onset and comorbidities and their relationships with disease severity using multivariate analyses. To investigate neuroprotection in the whole trial cohort, I measured MRI-derived percentage brain volume change, which is thought to reflect neuroaxonal integrity and disability. The percentage brain volume change was analysed with the SIENA method. As MS-related disability is also referable to spinal cord damage, which is seen in up to 90% of patients with MS, I investigated spinal cord MRI abnormalities in the UCL cohort. I measured the cervical cord lesion number and volume, and calculated cord cross-sectional area using the active surface model. Additionally, I measured the cross-sectional area using two different pipelines to improve reduceing variability and get more reliable results. Although MRI techniques are reliable and sensitive surrogate biomarkers of axonal pathology in MS, they are expensive, time consuming and not easily accessible. OCT is an emerging imaging technique that enables the measurement of the neural retina, whose layer thinning reflect axonal loss. In order to do so, I used a Heidelberg Spectralis OCT machine and measured the peripapillary retinal nerve fibre layer and the ganglion cell plus the inner plexiform layer thicknesses. Both participants from UCL and Edinburgh trial sites were included. From December 2014 to June 2016, I actively recruited 176 subjects at UCL. In total, 445 subjects were enrolled in the MS-SMART trial across the UK. The baseline analysis of the whole cohort showed that none of the clinical measures could explain the sample variance in isolation and that the SDMT – a measure of processing speed – emerged as the strongest explanatory component, although it could not explain more than 30% of the sample variance. The SDMT and the body mass index were the strongest predictor of whole brain volume. I also found that a history of optic neuritis at MS onset did not predict a better SPMS prognosis, while a history of hypertension was related to higher disease severity. The primary trial analysis findings were negative, meaning that there was no difference in terms of percentage brain volume change between any of the three active arms and the placebo. This suggested that amiloride, fluoxetine and riluzole had no neuroprotective effects. The annualised percentage brain volume change was -0.87%. In the fluoxetine arm, there was a significant pseudoatrophy at 24 weeks suggesting perhaps possible neuromodulation with some anti-inflammatory effects. Spinal cord atrophy occurred at a rate of -0.66% per year on average. Both spinal cord area and brain volume measures at baseline were significantly associated with EDSS at baseline and at 96 weeks. However, only spinal cord area at baseline, and not brain volume, seemed to predict confirmed disability progression at 96 weeks (OR= 0.94; 95% CI= 0.89 to 0.98, p= 0.01). The results from the two different spinal cord MRI analysis pipelines showed that head position into the scanner and lack of registration between baseline and follow-up would lead to substantial variability. However, when the sample size was larger, the two pipelines seemed to offer similar results. OCT measures showed significant mean annual thinning independent of age. Additionally, baseline OCT measures could significantly predict clinical changes as measured by the two most common clinical metrics (EDSS and MSFC) and with the MRI-derived percentage brain volume change at 96 weeks. There was no correlation between annualised atrophy rates of OCT measures and MRI percentage brain volume change or EDSS change. In summary, the cross-sectional analysis of the baseline characteristics of the trial cohort showed that the SDMT is an important clinical variable which correlates significantly with other clinical and MRI parameters. My study suggests that SDMT should be collected in all cross-sectional studies in SPMS. I found that multi-arm trials are feasible in a UK population of people with SPMS. None of the three drugs – amiloride, fluoxetine, and riluzole - had neuroprotective effects, suggesting that future trials in SPMS should focus on different agents with different mechanisms of action. The role of fluoxetine as an anti-inflammatory was questionable and might deserve more investigations in the future. I also found that the annual rates of brain volume and spinal cord atrophy were similar, although brain volume atrophy was slightly higher than spinal cord atrophy. However, both these measures were significantly associated with EDSS. Brain volume had the advantage of being associated with the timed 25-foot walk measure; whereas, spinal cord area had the advantage to predict confirmed disability progression at 96 weeks. OCT measures significantly decreased over time independently of patients’ age. Baseline OCT measures could predict EDSS changes and EDSS at 96 weeks, but the percentage change of OCT measures had no relation with EDSS changes, suggesting that the observation time of 96 weeks was insufficient to detect clinically meaningful OCT changes. In conclusion, analysing a large cohort of patients with SPMS enrolled in a phase 2B trial provided a large data set which served to explore outcome measures of disease progression. I found that none of the clinical, MRI or OCT variables was optimal, in isolation, to measure SPMS disability or progression. OCT and spinal cord MRI did not seem to provide better outcomes compared to MRI brain measures alone, implying that they could measure different aspects of the pathology underlying SPMS. My work suggests that investigation of composite outcome measures of multimodal variables might be the way forward to find more sensitive outcome measures for quantifying disability changes. Finally, my work also shows that multi-arm trials investigating different agents at the same time are feasible and advantageous in SPMS and should be replicated in MS and extended to other chronic neurological disorders