EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study

In young, first-episode, productive, medication-naive patients with schizophrenia, EEG microstates (building blocks of mentation) tend to be shortened. Koenig et al. [Koenig, T., Lehmann, D., Merlo, M., Kochi, K., Hell, D., Koukkou, M., 1999. A deviant EEG brain microstate in acute, neuroleptic-naïve schizophrenics at rest. European Archives of Psychiatry and Clinical Neuroscience 249, 205–211] suggested that shortening concerned specific microstate classes. Sequence rules (microstate concatenations, syntax) conceivably might also be affected. In 27 patients of the above type and 27 controls, from three centers, multichannel resting EEG was analyzed into microstates using k-means clustering of momentary potential topographies into four microstate classes (A–D). In patients, microstates were shortened in classes B and D (from 80 to 70 ms and from 94 to 82 ms, respectively), occurred more frequently in classes A and C, and covered more time in A and less in B. Topography differed only in class B where LORETA tomography predominantly showed stronger left and anterior activity in patients. Microstate concatenation (syntax) generally were disturbed in patients; specifically, the class sequence A→C→D→A predominated in controls, but was reversed in patients (A→D→C→A). In schizophrenia, information processing in certain classes of mental operations might deviate because of precocious termination. The intermittent occurrence might account for Bleuler's “double bookkeeping.” The disturbed microstate syntax opens a novel physiological comparison of mental operations between patients and controls.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Endocrine symptom assessment in women with breast cancer: what a simple "yes" means

GOALS OF WORK: To investigate the self-reported symptoms related to endocrine therapy in women with early or advanced breast cancer and the impact of these symptoms on quality of life (QL) indicators. MATERIALS AND METHODS: Symptom occurrence was assessed by the Checklist for Patients on Endocrine Therapy (C-PET) and symptom intensity was assessed by linear analogue self-assessment (LASA) indicators. Patients also responded to global LASA indicators for physical well-being, mood, coping effort and treatment burden. Associations between symptoms and these indicators were analysed by linear regression models. MAIN RESULTS: Among 373 women, the distribution of symptom intensity showed considerable variation in patients reporting a symptom as present. Even though patients recorded a symptom as absent, some patients reported having experienced that symptom when responding to symptom intensity, as seen for decreased sex drive, tiredness and vaginal dryness. Six of 13 symptoms and lower age had a detrimental impact on the global indicators, particularly tiredness and irritability. CONCLUSIONS: Patients' experience of endocrine symptoms needs to be considered both in patient care and research, when interpreting the association between symptoms and QL.

Differentiating hippocampal subregions by means of quantitative magnetization transfer and relaxometry: preliminary results

The hippocampal formation (HF) of healthy control subjects and schizophrenic patients was examined using an MRI experiment that implements sequences for relaxometry and magnetization transfer (MT) quantification. In addition to the semi-quantitative magnetization transfer ratio (MTR), all of the observable properties of the binary spin bath model were included. The study demonstrates that, in contrast to the MTR, quantitative MT parameters (especially the T2 relaxation time of restricted protons, T2b) are capable to differentiate functionally significant subregions within the HF. The MT methodology appears to be a promising new tool for the differential microstructural evaluation of the HF in neuropsychiatric disorders accompanied by memory disturbances.

New means in spinal pedicle hook fixation. A biomechanical evaluation

Pedicle hooks which are used as an anchorage for posterior spinal instrumentation may be subjected to considerable three-dimensional forces. In order to achieve stronger attachment to the implantation site, hooks using screws for additional fixation have been developed. The failure loads and mechanisms of three such devices have been experimentally determined on human thoracic vertebrae: the Universal Spine System (USS) pedicle hook with one screw, a prototype pedicle hook with two screws and the Cotrel-Dubousset (CD) pedicle hook with screw. The USS hooks use 3.2-mm self-tapping fixation screws which pass into the pedicle, whereas the CD hook is stabilised with a 3-mm set screw pressing against the superior part of the facet joint. A clinically established 5-mm pedicle screw was tested for comparison. A matched pair experimental design was implemented to evaluate these implants in constrained (series I) and rotationally unconstrained (series II) posterior pull-out tests. In the constrained tests the pedicle screw was the strongest implant, with an average pull-out force of 1650 N (SD 623 N). The prototype hook was comparable, with an average failure load of 1530 N (SD 414 N). The average pull-out force of the USS hook with one screw was 910 N (SD 243 N), not significantly different to the CD hook's average failure load of 740 N (SD 189 N). The result of the unconstrained tests were similar, with the prototype hook being the strongest device (average 1617 N, SD 652 N). However, in this series the difference in failure load between the USS hook with one screw and the CD hook was significant. Average failure loads of 792 N (SD 184 N) for the USS hook and 464 N (SD 279 N) for the CD hook were measured. A pedicular fracture in the plane of the fixation screw was the most common failure mode for USS hooks.(ABSTRACT TRUNCATED AT 250 WORDS)

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

Shaking head means "no"

A 45-year-old man was admitted to the emergency department because of twitching of the head. The patient took a tablet of sumatriptan every 3-4 h because of increasing head pain after a car accident. Owing to depression, the patient was on long-term treatment with venlafaxine. The patient presented as hypertensive, tachycardic, with dyskinesia and spontaneous myoclonic movements of the right sternocleidomastoid muscle. In a CT scan of the head and cervical spine any fractures, bleeding or damage of the vessels after the accident could be ruled out. After discontinuation of all serotonergic agents, administration of lorazepam symptoms resolved 24 h after the last intake of sumatriptan. Serotonin syndrome is a clinical diagnosis, which requires a high-index of diagnostic suspicion. Clinical features include a broad spectrum of symptoms ranging from mild to life-threatening manifestations. Management is based on removal of precipitating drugs and symptomatic care including benzodiazepines.