Fahr's disease: A rare neuropsychiatric disorder.

Fahr's disease is a rare clinical neurodegenerative entity, occurring mainly in 4th or 5th decade, showing gradually progressive bilateral symmetric calcifications in basal ganglia, subcortical white matter, thalami or cerebellum, which can lead to movement disorder and/or neuropsychiatric manifestations. We present two cases in the same family; a 68-year-old brother had involuntary jerky movements of hand and dysarthria for 10 years while the 44-year-old sister had right lower limb spasticity and decreased vision for 2 years. The serial MRI scans showed slow progression in the bilateral subcortical white matter and cerebellar dentate nuclei calcifications along with surrounding reactive gliosis.

The uncommon loop: Circumportal annular pancreas.

Circumportal annular pancreas (CAP) also known as portal annular pancreas (PAP) is an uncommon pancreatic anatomic variant in which normal pancreatic tissue completely surrounds the portal vein and can be mistaken for mass of pancreatic head. We present here a case of a 65 years old woman who was a diagnosed case of endometrial carcinoma, underwent CT scan for further metastatic workup which revealed this rare pancreatic variant.

Neurolymphomatosis of the Sciatic and Tibial Nerves.

Neurolymphomatosis (NL) is an uncommon clinical condition, characterised by lymphomatous infiltration of the central and/or peripheral nervous system. Most often it is caused by B-cell non-Hodgkin's lymphoma (NHL). Clinically, patients usually present with neuropathy involving the nerve roots, plexuses, peripheral or cranial nerves. NL usually occurs as a complication of prior lymphoma, but it can also present in the form of relapsed lymphoma. It is important to diagnose and start early treatment in all cases of nodal or visceral (including neural) lymphoma with chemo and/or radiation therapy. The PET-CT and MRI can help in making diagnosis. We are presenting a case of 28-year male patient, diagnosed as diffuse large B-cell lymphoma on the background of follicular lymphoma, which initially responded to treatment but then presented with NL, based on clinical history and radiological findings which were confirmed by histopathology.

Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits.

BACKGROUND: Spike sorting is a basic step for implantable neural interfaces. With the growing number of channels, the process should be computationally efficient, automatic,robust and applicable on implantable circuits. NEW METHOD: The proposed method is a combination of fully-automatic offline and online processes. It introduces a novel method for automatically determining a data-aware spike detection threshold, computationally efficient spike feature extraction, automatic optimal cluster number evaluation and verification coupled with Self-Organizing Maps to accurately determine cluster centroids. The system has the ability of unsupervised online operation after initial fully-automatic offline training. The prime focus of this paper is to fully-automate the complete spike detection and sorting pipeline, while keeping the accuracy high. RESULTS: The proposed system is simulated on two well-known datasets. The automatic threshold improves detection accuracies significantly( > 15%) as compared to the most common detector. The system is able to effectively handle background multi-unit activity with improved performance. COMPARISON: Most of the existing methods are not fully-automatic; they require supervision and expert intervention at various stages of the pipeline. Secondly, existing works focus on foreground neural activity. Recent research has highlighted importance of background multi-unit activity, and this work is amongst the first efforts that proposes and verifies an automatic methodology to effectively handle them as well. CONCLUSION: This paper proposes a fully-automatic, computationally efficient system for spike sorting for both single-unit and multi-unit spikes. Although the scope of this work is design and verification through computer simulations, the system has been designed to be easily transferable into an integrated hardware form.

Low SNR neural spike detection using scaled energy operators for implantable brain circuits.

Real time on-chip spike detection is the first step in decoding neural spike trains in implantable brain machine interface systems. Nonlinear Energy Operator (NEO) is a transform widely used to distinguish neural spikes from background noise. In this paper we define a general form of energy operators, of which NEO is a specific example, which gives better spike-noise separation than NEO and its derivatives. This is because of a non-linear scaling applied to the general discrete energy operator. Using two well-known publically available datasets, the performance of several operators is compared. On data sets that contain multi-unit spikes with low Signal to Noise ratio, the detection accuracy was improved by approximately 15%.