Transfer Learning on Structural Brain Age Models to Decode Cognition in MS: A Federated Learning Approach.

Introduction. Classical deep learning research requires lots of centralised data. However, data sets are often stored at different clinical centers, and sharing sensitive patient data such as brain images is difficult. In this manuscript, we investigated the feasibility of federated learning, sending models to the data instead of the other way round, for research on brain magnetic resonant images of people with multiple sclerosis (MS). Methods. Using transfer learning on a previously published brain age model, we trained a model to decode performance on the symbol digit modalities test (SDMT) of patients with MS from structural T1 weighted MRI. Three international centers in Brussels, Greifswald and Prague participated in the project. In Brussels, one computer served as the server coordinating the FL project, while the other served as client for model training on local data (n=97). The other two clients were Greifswald (n=104) and Prague (n=100). Each FL round, the server sent a global model to the clients, where its fully connected layer was updated on the local data. After collecting the local models, the server applied a weighted average of two randomly picked clients, yielding a new global model. Results. After 22 federated learning rounds, the average validation loss across clients reached a minimum. The model appeared to have learned to assign SDMT values close to the mean with a mean absolute error of 9.04, 10.59 and 10.71 points between true and predicted SDMT on the test data sets of Brussels, Greifswald and Prague respectively. The overall test MAE across all clients was 10.13 points. Conclusion. Federated learning is feasible for machine learning research on brain MRI of persons with MS, setting the stage for larger transfer learning studies to investigate the utility of brain age latent representations in cognitive decoding tasks

The squares test as a measure of hand function in multiple sclerosis

Deterioration of hand function can be important in multiple sclerosis (MS). The standard way of assessing hand function in MS is the 9-hole peg test (9HPT), one of the three components of the MS functional composite measure. In this study we examine the squares test (ST), a test of hand function that is used extensively in handedness research. We evaluated reproducibility of the ST in 49 healthy controls, and both discriminatory power and concurrent validity of the ST in 38 MS patients and 18 age and gender matched controls. The ST proved to be a reliable and easy to administrate paper-and-pencil test of hand function. The ST showed a high and highly significant correlation with the standard 9HPT over a broad range of Expanded Disability Status Scale (EDSS) scores, and had high discriminatory power, also comparable to the 9HPT. Therefore, the ST is a candidate test for use in composite measures of MS related functional deficits for clinical practice and in clinical trials. (C) 2014 Elsevier B.V. All rights reserved

Acceptance and Commitment Therapy to Increase Resilience in Chronic Pain Patients: A Clinical Guideline

Chronic pain remains a very difficult condition to manage for healthcare workers and patients. Different options are being considered and a biopsychosocial approach seems to have the most benefit, since chronic pain influences biological, psychological and social factors. A conservative approach with medication is the most common type of treatment in chronic pain patients; however, a lot of side effects are often induced. Therefore, a premium is set on novel nonpharmacological therapy options for chronic pain, such as psychological interventions. Previous research has demonstrated that resilience is a very important aspect in coping with chronic pain. A more recent type of cognitive-behavioural therapy is Acceptance and Commitment Therapy, in which psychological flexibility is intended to be the end result. In this manuscript, current evidence is used to explain why and how a comprehensive and multimodal treatment for patients with chronic pain can be applied in clinical practice. This multimodal treatment consists of a combination of pain neuroscience education and cognitive-behavioural therapy, more specifically Acceptance and Commitment Therapy. The aim is to provide a clinical guideline on how to contribute to greater flexibility and resilience in patients with chronic pain

Acceptance and Commitment Therapy to Increase Resilience in Chronic Pain Patients: A Clinical Guideline

Chronic pain remains a very difficult condition to manage for healthcare workers and patients. Different options are being considered and a biopsychosocial approach seems to have the most benefit, since chronic pain influences biological, psychological and social factors. A conservative approach with medication is the most common type of treatment in chronic pain patients; however, a lot of side effects are often induced. Therefore, a premium is set on novel nonpharmacological therapy options for chronic pain, such as psychological interventions. Previous research has demonstrated that resilience is a very important aspect in coping with chronic pain. A more recent type of cognitive-behavioural therapy is Acceptance and Commitment Therapy, in which psychological flexibility is intended to be the end result. In this manuscript, current evidence is used to explain why and how a comprehensive and multimodal treatment for patients with chronic pain can be applied in clinical practice. This multimodal treatment consists of a combination of pain neuroscience education and cognitive-behavioural therapy, more specifically Acceptance and Commitment Therapy. The aim is to provide a clinical guideline on how to contribute to greater flexibility and resilience in patients with chronic pain

In search of biomarkers for schizophrenia using electroencephalography

The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying mainly on clinical signs and symptoms. Current neurophysiological measurements can distinguish groups of healthy controls and groups of schizophrenia patients. Individual classification based on neurophysiological measurements only shows moderate accuracy. In this study, we wanted to examine whether it is possible to distinguish controls and patients individually with a good accuracy. To this end we used a combination of features from different test paradigms, in particular the auditory and visual P300 and the mismatch negativity. We selected 54 patients and 54 controls, matched for age and gender, from the data available at the UPC Kortenberg. The EEG-data were high- and low-pass filtered, epoched, artefacts were rejected and the epochs were averaged. Features (latencies and amplitudes of component peaks) were extracted from the averaged signals. The resulting dataset was used to train and test classification algorithms. Here we applied Naïve Bayes and Decision Tree (without and with AdaBoost). A combination of three evoked potentials allowed us to accurately classify individual subjects as either control or patient. For the three investigated classifiers a total accuracy of more than 80%, a sensitivity of above 82% and a specificity of at least 78% was found. © 2014 IEEE.status: publishe

The effect of task modality and stimulus frequency in paced serial addition tests on functional brain activity.

The paced serial addition test (PSAT) is regularly used to assess cognitive deficits in various neuropsychiatric conditions. Being a complex test, it reflects the status of multiple cognitive domains such as working memory, information processing speed and executive functioning. Two versions of the PSAT exist. One uses auditory stimuli through spoken numbers and is known as the PASAT, while the other one presents patients with visual stimuli and is called PVSAT. The PASAT is considered more frustrating by patients, and hence the visual version is usually preferred. Research has suggested that an interference might exist between patients' verbal answers and the auditory presentation of stimuli. We therefore removed the verbal response in this study, and aimed to investigate differences in functional brain activity through functional magnetic resonance imaging.Fifteen healthy controls performed the two test versions inside an MRI scanner-switching between stimulus modality (auditory vs. visual) as well as inter-stimulus frequency (3s vs. 2s). We extracted 11 independent components from the data: attentional, visual, auditory, sensorimotor and default mode networks. We then performed statistical analyses of mean network activity within each component, as well as inter-network connectivity of each component pair during the different task types.Unsurprisingly, we noted an effect of modality on activity in the visual and auditory components. However, we also describe bilateral frontoparietal, anterior cingulate and insular attentional network activity. An effect of frequency was noted only in the sensorimotor network. Effects were found on edges linking visual and auditory regions. Task modality influenced an attentional-sensorimotor connection, while stimulus frequency had an influence on sensorimotor-default mode connections.Scanner noise during functional MRI may interfere with brain activation-especially during tasks involving auditory pathways. The question whether to use PVSAT or PASAT for an fMRI study is, therefore, an important one. Specific effects of both modalities should be known to study designers. We conclude that both tests should not be considered interchangeable, as significant changes were brought to light during test performance in different modalities

Spatiotemporal and spectral dynamics of multi-item working memory as revealed by the n-back task using MEG.

Multi-item working memory (WM) is a complex cognitive function thought to arise from specific frequency band oscillations and their interactions. While some theories and consistent findings have been established, there is still a lot of unclarity about the sources, temporal dynamics, and roles of event-related fields (ERFs) and theta, alpha, and beta oscillations during WM activity. In this study, we performed an extensive whole-brain ERF and time-frequency analysis on n-back magnetoencephalography data from 38 healthy controls. We identified the previously unknown sources of the n-back M300, the right inferior temporal and parahippocampal gyrus and left inferior temporal gyrus, and frontal theta power increase, the orbitofrontal cortex. We shed new light on the role of the precuneus during n-back activity, based on an early ERF and theta power increase, and suggest it to be a crucial link between lower-level and higher-level information processing. In addition, we provide strong evidence for the central role of the hippocampus in multi-item WM behavior through the dynamics of theta and alpha oscillatory changes. Almost simultaneous alpha power decreases observed in the hippocampus and occipital fusiform gyri, regions known to be involved in letter processing, suggest that these regions together enable letter recognition, encoding and storage in WM. In summary, this study offers an extensive investigation into the spatial, temporal, and spectral characteristics of n-back multi-item WM activity.info:eu-repo/semantics/publishe

Spatial maps of the 11 labelled components at the three most informative slices.

<p>Coordinates are given in MNI space and images are shown in radiological convention (left is right).</p