Improved Alzheimer's Disease versus Frontotemporal Lobar Degeneration Differential Diagnosis Combining EEG and Neurochemical Biomarkers: A Pilot Study.

BACKGROUND: Distinguishing between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) results in poor diagnostic accuracy. OBJECTIVE: To investigate the utility of electroencephalography (EEG)-based biomarkers in comparison and in addition to established cerebrospinal fluid (CSF) biomarkers in the AD versus FTLD differential diagnosis. METHODS: The study cohort comprised 37 AD and 30 FTLD patients, of which 17 AD and 9 FTLD patients had definite diagnoses. All participants had CSF neurochemical (NCM) biomarker analyses (Aß1-42, T-tau, P-tau181, and Nf-L) and underwent 19-channel resting-state EEG. From the EEG spectra, dominant frequency peaks were extracted in four regions resulting in four dominant frequencies. This produced eight features (4 NCM + 4 EEG). RESULTS: When NCM and EEG markers were combined, the diagnostic accuracy increased significantly. In the whole group, the accuracy went up from 79% (NCM) to almost 82%, while in the definite group only, it went up from around 85% to almost 95%. Two differences in the occurrence of the dominant EEG frequency were discovered: people lacking a clear dominant peak almost all had definite AD, while people with two peaks more often had FTLD. CONCLUSION: Combining EEG with NCM biomarkers resulted in differential diagnostic accuracies of 82% in clinically diagnosed AD and FTD patients and of 95% in patients having a definite diagnosis, which was significantly better than with EEG or NCM biomarkers alone. This suggests that NCM and EEG markers are complementary, revealing different aspects of the disease and therefore confirms again their relevance in developing additional diagnosis tools.

Brain age as a surrogate marker for cognitive performance in multiple sclerosis.

BACKGROUND AND PURPOSE: Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as 'how old the brain looks' and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). METHODS: A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain-predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). RESULTS: Brain age was significantly related to SDMT scores in the MS_test dataset (r = -0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = -0.24, p < 0.001) and a significant weight (-0.25, p = 0.002) in a multivariate regression equation with age. CONCLUSIONS: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.

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.

The role of hippocampal theta oscillations in working memory impairment in multiple sclerosis.

Working memory (WM) problems are frequently present in people with multiple sclerosis (MS). Even though hippocampal damage has been repeatedly shown to play an important role, the underlying neurophysiological mechanisms remain unclear. This study aimed to investigate the neurophysiological underpinnings of WM impairment in MS using magnetoencephalography (MEG) data from a visual-verbal 2-back task. We analysed MEG recordings of 79 MS patients and 38 healthy subjects through event-related fields and theta (4-8 Hz) and alpha (8-13 Hz) oscillatory processes. Data was source reconstructed and parcellated based on previous findings in the healthy subject sample. MS patients showed a smaller maximum theta power increase in the right hippocampus between 0 and 400 ms than healthy subjects (p = .014). This theta power increase value correlated negatively with reaction time on the task in MS (r = -.32, p = .029). Evidence was provided that this relationship could not be explained by a 'common cause' confounding relationship with MS-related neuronal damage. This study provides the first neurophysiological evidence of the influence of hippocampal dysfunction on WM performance in MS.

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.

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

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time-frequency-channel domain.

The goal of this study was to capture the electroencephalographic signature of experimentally induced pain and pain-modulating mechanisms after painful peripheral electrical stimulation to determine one or a selected group of electrodes at a specific time point with a specific frequency range. In the first experiment, ten healthy participants were exposed to stimulation of the right median nerve while registering brain activity using 32-channel electroencephalography. Electrical stimulations were organized in four blocks of 20 stimuli with four intensities - 100%, 120%, 140%, and 160% - of the electrical pain threshold. In the second experiment, 15 healthy participants received electrical stimulation on the dominant median nerve before and during the application of a second painful stimulus. Raw data were converted into the time-frequency domain by applying a continuous wavelet transform. Separated domain information was extracted by calculating Parafac models. The results demonstrated that it is possible to capture a reproducible cortical neural response after painful electrical stimulation, more specifically at 250 milliseconds poststimulus, at the midline electrodes Cz and FCz with predominant δ-oscillations. The signature of the top-down nociceptive inhibitory mechanisms is δ-activity at 235 ms poststimulus at the prefrontal electrodes. This study presents a methodology to overcome the a priori determination of the regions of interest to analyze the brain response after painful electrical stimulation.

Does including the full CVLT-II and BVMT-R improve BICAMS? Evidence from a Belgian (Dutch) validation study.

BACKGROUND: The Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) is a fast, easy-to-administer and already widely validated neuropsychological battery for cognition in multiple sclerosis. OBJECTIVE: The goals of our study were to validate the BICAMS in a Belgian Dutch-speaking population and to investigate to what extent including extensive versions of two of the three BICAMS subtests improved its psychometric qualities. METHODS: Ninety-seven persons with MS and ninety-seven healthy controls were included and group-matched on age, education level and gender. All participants performed the BICAMS with an extensive version of the CVLT-II and BVMT-R. RESULTS: The SDMT and BVMT-R were able to dissociate between the MS and healthy control group, while the CVLT-II was not. Distributions of CVLT-II scores suggest learning effects in the MS group, indicating the need for alternative word lists or the construction of an adapted version fitted for repeated administration. Including the full CVLT-II and BVMT-R did not markedly improve the psychometric qualities of the BICAMS. CONCLUSION: This study validates the BICAMS in a Belgian Dutch-speaking population and facilitates the use of it in clinical practice, while providing evidence that including full versions of the CVLT-II and BVMT-R does not increase its psychometric qualities markedly.

The effect of morphological and microstructural integrity of the corpus callosum on cognition, fatigue and depression in mildly disabled MS patients.

AIM: To assess the value of callosal morphological and microstructural integrity in assessing different cognitive domains, fatigue and depression in mildly disabled multiple sclerosis (MS) patients. MATERIALS AND METHODS: We assessed 29 mildly disabled MS patients and 15 healthy controls using 3T magnetic resonance images (T1-weighted, FLAIR and DTI) and neuropsychological tests assessing different cognitive functions, depression and fatigue. We compared the added value of morphological measures (corpus callosum area corrected for total intracranial volume, index, circularity and the more detailed thickness profile) and diffusion features (fractional anisotropy and mean diffusivity) in multilinear models including standard clinical and whole-brain parameters in assessing neuropsychological scores. RESULTS: Even in mildly disabled MS patients, a significant reduction of the corpus callosum (p<0.001) was observed in comparison to healthy controls. Callosal area, index and circularity were significantly (p<0.002) related to whole-brain white matter volume, T2 lesion load and deep grey matter volume, but not with cortical grey matter. The combination of commonly used imaging and clinical parameters explained between 7% (Fatigue) and 50% (processing speed, verbal memory) of the adjusted variance. Inclusion of the mean diffusivity increased the adjusted R2 significantly to 69% (p=0.004) and 71% (p=0.002) for visuospatial and verbal memory respectively. CONCLUSION: Our results show that callosal features may be used as an alternative to measuring whole-brain volumes. Furthermore, the microstructural integrity of the corpus callosum can help to predict an MS patient's memory performance.

EEG Dominant Frequency Peak Differentiates Between Alzheimer's Disease and Frontotemporal Lobar Degeneration.

We investigated the power of EEG as biomarker in differential diagnosis of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). EEG was recorded from 106 patients with AD or FTLD, of which 37 had a definite diagnosis, and 40 controls. Dominant frequency peaks were extracted for all 19 channels, for each subject. The average frequency of the largest dominant frequency peaks (maxpeak) was significantly lower in AD than FTLD patients and controls. Based on ROC analysis, classification could be made with diagnostic accuracy of 78.9%. Our findings show that quantitative analysis of EEG maxpeak frequency is an easy and useful measure for differential dementia diagnosis.

Assessing PML risk under immunotherapy: if all you have is a hammer, everything looks like a nail.

Recently, three progressive multifocal leukoencephalopathy (PML) cases have been reported in multiple sclerosis (MS) patients, two treated with fingolimod (Gilenya, Novartis), the third with dimethyl fumarate (Tecfidera, Biogen). Because our immunotherapeutic arsenal in MS and other diseases is increasing, and because PML is a very serious health risk, it is of interest to the clinical community to show how we can assess this risk in a statistically sound way. The null-hypothesis for this analysis was that there is no elevated risk for PML in patients treated with one of these recent drugs, compared to the incidence in the general population. We conclude that the null hypothesis cannot be refuted.

Do advanced statistical techniques really help in the diagnosis of the metabolic syndrome in patients treated with second-generation antipsychotics?

OBJECTIVE: Metabolic and cardiovascular diseases in patients with schizophrenia have gained a lot of interest in recent years. Developing an algorithm to detect the metabolic syndrome based on readily available variables would eliminate the need for blood sampling, which is considered expensive and inconvenient in this population. METHOD: All patients fulfilled DSM-IV diagnosis of schizophrenia or schizoaffective disorder. We used the International Diabetes Federation criteria (European population) to diagnose the metabolic syndrome. We used logistic regression and optimized artificial neural networks and support vector machines to detect the metabolic syndrome in a cohort of schizophrenic patients of the University Psychiatric Center Kortenberg, KU Leuven, Belgium. Testing was done on one-third of the included cohort (202 patients); training was performed using a 10-fold stratified cross-validation scheme. The data were collected between 2000 and 2008. RESULTS: All 3 methods yielded similar results, with satisfying accuracies of about 80%. However, none of the advanced statistical methods could improve on the results obtained using a very simple and naive model including only central obesity and information on blood pressure. CONCLUSIONS: Although so-called pattern recognition techniques bear high promise in improving clinical decision making, the results should be presented with caution and preferably in comparison with a less complicated technique.

Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms.

OBJECTIVE: The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying primarily 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 mostly shows moderate accuracy. 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 extracted from the auditory and visual P300 paradigms and the mismatch negativity paradigm. METHODS: 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 and 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. First on separate paradigms and then on all combinations, we applied Naïve Bayes, Support Vector Machine and Decision Tree, with two of its improvements: Adaboost and Random Forest. RESULTS: For at least two classifiers the performance increased significantly by combining paradigms compared to single paradigms. The classification accuracy increased from at best 79.8% when trained on features from single paradigms, to 84.7% when trained on features from all three paradigms. CONCLUSION: A combination of features originating from three evoked potential paradigms allowed us to accurately classify individual subjects as either control or patient. Classification accuracy was mostly above 80% for the machine learners evaluated in this study and close to 85% at best.

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.

Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection.

BACKGROUND: The mechanisms underlying cognitive impairment in MS are still poorly understood. However, due to the specific pathology of MS, one can expect alterations in connectivity leading to physical and cognitive impairment. AIM: In this study we aimed at assessing connectivity differences in EEG between cognitively impaired (CI) and cognitively preserved (CP) MS patients. We also investigated the influence of the measures used to construct networks. METHODS: We included 308 MS patients and divided them into two groups based on their cognitive score. Graph theoretical network analyses were conducted based on networks constructed using different connectivity measures, i.e. correlation, correlation in the frequency domain, coherence, partial correlation, the phase lag index and the imaginary part of coherency. The most commonly encountered network parameters were calculated and compared between the two groups using Wilcoxon's rank test. Clustering coefficients and path lengths were normalized to a randomized mean clustering coefficient and path length for each patient. False discovery rate was used to correct for the multiple comparisons and Cohen's d effect sizes are reported. RESULTS: Coherence analysis suggests that theta and delta connectivity is significantly smaller in cognitively impaired patients. Small-worldness differences are found in networks based on correlation, theta and delta coherence and correlation in the frequency domain. Modularity was related to age but not to cognition. CONCLUSION: Cognitive deterioration in MS is a symptom that seems to be caused by neural disconnections, probably the white matter tracts connecting both hemispheres, and leads to a wide range in network differences which can be assessed by applying GTA to EEG data. In the future, these results may lead to cheaper and more objective assessments of cognitive impairment in MS.