Commissural white matter disconnectivity in normal ageing and Alzheimer’s disease

The network of commissural white matter fibres responsible for connecting the hemispheres of the brain is known as the corpus callosum (CC). Atrophy to the CC is evident in studies of aging and Alzheimer’s disease (AD), but patterns and functional implications of neurodegeneration are still somewhat unclear. In this thesis, neuroimaging methods were used to further examine how structural and functional CC properties are affected by normal ageing and AD. In Study 1, diffusion tensor imaging (DTI) was used to examine the posterior CC tract bundles in young and older adults. Parietal and temporal midsagittal CC segments were particularly impaired in older adults, while occipital tracts were relatively preserved. Study 2 applied this methodology to study Mild Cognitive Impairment (MCI) and AD. MCI patients exhibited reduced integrity in midsagittal parietal segments compared to controls. AD patients exhibited reductions in parietal and temporal segments, yielding high classification accuracy (95-98%) against controls. Study 3 assessed visual interhemispheric transfer in aging using electroencephalography (EEG). Transfer speed was elongated in older adults, but was driven by earlier activation of the input hemisphere rather than delayed activation of the receiving hemisphere. This was not interpreted as impairment in older age, in line with findings of preserved occipital tracts in Study 1. Study 5 examined EEG functional connectivity methodology. We showed that connectivity was strongest at the dominant EEG frequency, which experiences slowing in older age. Previous studies using conventional frequency bands may therefore be biased against older adults. Study 6 applied these findings to study interhemispheric functional connectivity in older adults, while controlling for age-related frequency slowing. Age-related disconnectivity between frontal sites was evident, reflecting typical anterior-posterior neurodegeneration in older adults (Bennett, Madden, Vaidya, Howard, & Howard, 2010). These studies provide novel spatial and methodological insight into the CC during ageing and AD

Deep Learning of Resting-state Electroencephalogram Signals for 3-class Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Ageing

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings

Resting state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

The individual alpha peak frequency (IAPF) of the human EEG typically experiences slowing with increasing age. Despite this hallmark change, studies that investigate modulations of conventional EEG alpha power and connectivity by aging and age-related neuropathology neglect to account for inter-group differences in IAPF. To investigate the relationship of age-related IAPF slowing with EEG power and connectivity, we recorded eyes-closed resting state EEG in 37 young adults and 32 older adults. We replicated the finding of a slowed IAPF in older adults. IAPF values were significantly correlated with the frequency of maximum global connectivity and the means of their distributions did not differ, suggesting that connectivity was highest at the IAPF. Older adults expressed reduced global EEG power and connectivity at the conventional upper alpha band (10-12 Hz) compared to young adults. In contrast, groups had equivalent power and connectivity at the IAPF. The results suggest that conventional spectral boundaries may be biased against older adults or any group with a slowed IAPF. We conclude that investigations of alpha activity in aging and age-related neuropathology should be adapted to the IAPF of the individual and that previous findings should be interpreted with caution. EEG in the dominant alpha range may be unsuitable for examining cortico-cortical connectivity due to its subcortical origins

Visual and visuomotor interhemispheric transfer time in older adults

Older adults typically experience reductions in the structural integrity of the anterior channels of the corpus callosum. Despite preserved structural integrity in central and posterior channels, many studies have reported that interhemispheric transfer, a function attributed to these regions, is detrimentally affected by aging. In this study, we use a constrained event-related potential analysis in the theta and alpha frequency bands to determine whether interhemispheric transfer is affected in older adults. The crossed-uncrossed difference and lateralized visual evoked potentials were used to assess interhemispheric transfer in young (18–27) and older adults (63–80). We observed no differences in the crossed-uncrossed difference measure between young and older groups. Older adults appeared to have elongated transfer in the theta band potentials, but this effect was driven by shortened contralateral peak latencies, rather than delayed ipsilateral latencies. In the alpha band, there was a trend toward quicker transfer in older adults. We conclude that older adults do not experience elongated interhemispheric transfer in the visuomotor or visual domains and that these functions are likely attributed to posterior sections of the corpus callosum, which are unaffected by aging

Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study

Abstract: Background: The B-MaP-C study aimed to determine alterations to breast cancer (BC) management during the peak transmission period of the UK COVID-19 pandemic and the potential impact of these treatment decisions. Methods: This was a national cohort study of patients with early BC undergoing multidisciplinary team (MDT)-guided treatment recommendations during the pandemic, designated ‘standard’ or ‘COVID-altered’, in the preoperative, operative and post-operative setting. Findings: Of 3776 patients (from 64 UK units) in the study, 2246 (59%) had ‘COVID-altered’ management. ‘Bridging’ endocrine therapy was used (n = 951) where theatre capacity was reduced. There was increasing access to COVID-19 low-risk theatres during the study period (59%). In line with national guidance, immediate breast reconstruction was avoided (n = 299). Where adjuvant chemotherapy was omitted (n = 81), the median benefit was only 3% (IQR 2–9%) using ‘NHS Predict’. There was the rapid adoption of new evidence-based hypofractionated radiotherapy (n = 781, from 46 units). Only 14 patients (1%) tested positive for SARS-CoV-2 during their treatment journey. Conclusions: The majority of ‘COVID-altered’ management decisions were largely in line with pre-COVID evidence-based guidelines, implying that breast cancer survival outcomes are unlikely to be negatively impacted by the pandemic. However, in this study, the potential impact of delays to BC presentation or diagnosis remains unknown

Medical student radiology training: What are the objectives for contemporary medical practice?

Rationale and Objectives. The authors attempt to provide a set of objectives for medical student training in radiology for contemporary medical practice. Materials and Methods. A questionnaire containing a list of educational objectives was sent to 32 radiologists in charge of medical student training in radiology at accredited residency programs in Australia and New Zealand. The importance of including each preselected objective in the curriculum was measured by respondents' agreement or disagreement on a scale of 1-6. Opportunity also was given to respondents to suggest objectives other than those presented on the questionnaire. Results. Twenty of the 32 questionnaires were returned, and a set of educational objectives was established based on the responses. The objectives were ranked in importance according to the mean score assigned to each objective by the respondents. Conclusion. This new set of educational objectives for medical student radiology training reflects recent changes in radiologic and medical practice and points to potential future developments

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer’s Disease and Mild Cognitive Impairmen

—Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance— The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100%

NAD(P)H:Quinone oxidoreductase-1 C609T polymorphism analysis in human superficial bladder cancers: relationship of genotype status to NQO1 phenotype and clinical response to Mitomycin C.

NAD(P)H:Quinone oxidoreductase-1 (NQO1) has been implicated in the bioreductive activation of the clinically active anticancer drug Mitomycin C (MMC) and a polymorphic variant of NQO1 which lacks functional enzyme activity (NQO1*2) has been linked with poor survival in patients treated with MMC. The relationship between NQO1 activity and cellular response to MMC is however controversial and the aim of this study was to determine whether the response of bladder cancer patients to MMC can be forecast on the basis of NQO1*2 genotype status. Genomic DNA was extracted from formalin-fixed, paraffin-embedded tissue from 148 patients with low to intermediate grade (G1/G2) superficial (Ta/T1) bladder cancers and NQO1*2 genotype status determined by PCR-RFLP. NQO1*2 genotype status was retrospectively compared with clinical response to intravesical administered MMC with the primary end-point being time to first recurrence. NQO1 phenotype was determined by immunohistochemistry. Of the 148 patients genotyped, 85 (57.4%) were NQO1*1 (wild-type), 59 (39.8%) were NQO1*1/*2 (heterozygotes) and 4 (2.7%) were NQO1*2/*2. No NQO1 protein expression was detected in NQO1*2/*2 tumours. A broad spectrum of NQO1 protein expression existed in tumours genotyped as NQO1*1 and NQO1*1/*2 although tumours with NQO1*1 typically expressed higher NQO1 protein. A poor correlation existed between NQO1*2 genotype status and clinical response to MMC. The results of this retrospective study suggest that tailoring MMC therapy to individual patients with superficial bladder cancer on the basis of NQO1 genotype status is unlikely to be of clinical benefit