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ItemOpen Access
Recovery of clinical, cognitive and cortical activity measures following mild traumatic brain injury (mTBI): A longitudinal investigation
(Masson SpA, 2023) Coyle, Hannah; Bailey, Neil; Ponsford, Jennie; Hoy , Kate E.
The mechanisms that underpin recovery following mild traumatic brain injury (mTBI) remain poorly understood. Identifying neurophysiological markers and their functional significance is necessary to develop diagnostic and prognostic indicators of recovery. The current study assessed 30 participants in the subacute phase of mTBI (10–31 days post-injury) and 28 demographically matched controls. Participants also completed 3 month (mTBI: N = 21, control: N = 25) and 6 month (mTBI: N = 15, control: N = 25) follow up sessions to track recovery. At each time point, a battery of clinical, cognitive, and neurophysiological assessments was completed. Neurophysiological measures included resting-state electroencephalography (EEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG). Outcome measures were analysed using mixed linear models (MLM). Group differences in mood, post-concussion symptoms and resting-state EEG resolved by 3 months, and recovery was maintained at 6 months. On TMS-EEG derived neurophysiological measures of cortical reactivity, group differences ameliorated at 3 months but re-emerged at 6 months, while on measures of fatigue, group differences persisted across all time points. Persistent neurophysiological changes and greater fatigue in the absence of measurable cognitive impairment may suggest the impact of mTBI on neuronal communication may leads to increased neural effort to maintain efficient function. Neurophysiological measures to track recovery may help identify both temporally optimal windows and therapeutic targets for the development of new treatments in mTBI.
ItemOpen Access
Obsessive-compulsive disorder (OCD) is associated with increased electroencephalographic (EEG) delta and theta oscillatory power but reduced delta connectivity
(Pergamon Press, 2023) Perera, M. Prabhavi N.; Mallawaarachchi, Sudaraka; Bailey, Neil; Murphy, Oscar W.; Fitzgerald, Paul
Obsessive-Compulsive Disorder (OCD) is a mental health condition causing significant decline in the quality of life of sufferers and the limited knowledge on the pathophysiology hinders successful treatment. The aim of the current study was to examine electroencephalographic (EEG) findings of OCD to broaden our understanding of the disease. Resting-state eyes-closed EEG data was recorded from 25 individuals with OCD and 27 healthy controls (HC). The 1/f arrhythmic activity was removed prior to computing oscillatory powers of all frequency bands (delta, theta, alpha, beta, gamma). Cluster-based permutation was used for between-group statistical analyses, and comparisons were performed for the 1/f slope and intercept parameters. Functional connectivity (FC) was measured using coherence and debiased weighted phase lag index (d-wPLI), and statistically analyzed using the Network Based Statistic method. Compared to HC, the OCD group showed increased oscillatory power in the delta and theta bands in the fronto-temporal and parietal brain regions. However, there were no significant between-group findings in other bands or 1/f parameters. The coherence measure showed significantly reduced FC in the delta band in OCD compared to HC but the d-wPLI analysis showed no significant differences. OCD is associated with raised oscillatory power in slow frequency bands in the fronto-temporal brain regions, which agrees with the previous literature and therefore is a potential biomarker. Although delta coherence was found to be lower in OCD, due to inconsistencies found between measures and the previous literature, further research is required to ascertain definitive conclusions.
ItemOpen Access
Health economics-informed planning of psychiatric care: A primer and curriculum framework for psychiatrists
(Sage Publications Inc, 2023) Looi, Jeffrey; Robson, Stephen; Bastiampillai, Tarun; Allison, Stephen; Kisely, Steve
Objective Contemporary medical education lacks a strong focus on health economics which guides major decisions in private and public health services. We briefly outline the rationale, guiding principles, main analytic methods, and a suggested framework for health economics education in psychiatry. Conclusions Health economics aims to improve the efficiency of healthcare. Some analytic methods can be harnessed by psychiatrists to better plan clinical care. Health economic methods will also assist psychiatrists in translating their expertise and clinical priorities more effectively to policy-makers, governments, and private insurers motivated by economic reasoning.
ItemOpen Access
Concurrent transcranial magnetic stimulation and electroencephalography measures are associated with antidepressant response from rTMS treatment for depression
(Elsevier, 2023) Bailey, Neil; Hoy, Kate; Sullivan, Caley M.; Allman, Brienna; Rogasch, Nigel C; Daskalakis, Zafiris J.; Fitzgerald, Paul
Background Response rates to repetitive transcranial magnetic stimulation (rTMS) for depression are 25-45%. Participant features obtained prior to treatment that are associated with response to rTMS may be clinically useful. TMS-evoked neural activity recorded via electroencephalography (EEG) prior to treatment may be associated with treatment response. We examined whether these measures could differentiate responders and non-responders to rTMS for depression. Methods Thirty-nine patients with treatment-resistant major depressive disorder (MDD) and 21 healthy controls received TMS during EEG recordings (TMS-EEG). MDD participants then completed 5-8 weeks of rTMS treatment. Repeated measures ANOVAs compared N100 amplitude, N100 slope, and theta power across 3 groups (responders, non-responders and controls), 2 hemispheres (left, F3, and right, F4), and 2 stimulation types (single pulse and paired pulses with a 100ms inter-pulse interval [pp100]). Results Neither N100 amplitude nor theta power differed between responders and non-responders. Responders showed a steeper negative N100 slope for single pulses and steeper positive slope for pp100 pulses at F3 than non-responders. Exploratory analyses suggested this may have been due to the responder group showing larger P60 and N100 amplitudes. Limitations Our study had a small sample size. Conclusion Left hemisphere TEPs, in particular N100 slope, may be related to response rTMS treatment for depression. If our future research with larger sample sizes verifies this result, the finding may provide clinical utility in recommendations for rTMS treatment for depression.
ItemOpen Access
Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact
(Elsevier, 2023) Ajuwon, Busayo; Awotundun, Oluwatosin; Richardson, Alice; Roper, Katrina; Sheel, Meru; Rahman, Nurudeen; Salako, Abideen; Lidbury, Brett
Background Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited. Objective This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections. Methods We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV). Results We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done. Conclusion Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.